Data Architecture

Data Democratization Strategy and its Role in Business Decisions

Actian Corporation

January 20, 2025

Data democratization is a cornerstone of modern business strategy, enabling organizations to empower their workforce with access to critical data. By removing barriers to data access, companies can foster a collaborative and innovative environment that drives decision-making and operational efficiency. This comprehensive guide explores what data democratization entails, its benefits, and how to implement it effectively.

Understanding Data Democratization

Data democratization is reshaping how organizations operate in a data-driven world. By granting widespread access to data, businesses can enable employees at all levels to make informed decisions.

What is Data Democratization?

Data democratization refers to the process of enabling access to data across all levels of an organization, empowering employees to utilize data in decision-making without requiring specialized technical skills. It breaks down data silos and ensures that data insights are not limited to technical teams or upper management. By implementing a structured strategy, businesses can create an environment where data is a shared asset, fostering collaboration and innovation.

Benefits and Importance

Data democratization is pivotal for companies seeking a competitive edge in a rapidly evolving market. By distributing data access, organizations can:

Enhance Agility: Quickly adapt to market changes through data-informed decisions.
Improve Collaboration: Break down silos and encourage cross-functional teamwork.
Empower Employees: Equip all team members with the insights they need to contribute meaningfully.
Boost Innovation: Enable a broader range of employees to explore ideas backed by real data.
Reduce Bottlenecks: Minimize reliance on data specialists for routine queries, freeing them to focus on complex challenges.

By addressing these needs, data democratization fosters a culture where data-driven decisions are the norm, enabling organizations to stay ahead of competitors.

The Role of a Data Democratization Strategy

A data democratization strategy serves as a blueprint for integrating data into every facet of an organization. It ensures that data is not only accessible but also actionable, empowering teams to make informed decisions. Let’s go over the transformative role of such a strategy in modern businesses and highlights its far-reaching impact.

Accelerating Decision-Making Processes

A well-implemented data democratization strategy reduces the time taken to retrieve and analyze data, allowing businesses to make timely decisions. When data is easily accessible, employees can respond to opportunities and challenges more quickly, giving the organization a competitive advantage. Quick decisions, backed by accurate data, often lead to improved outcomes and increased market responsiveness.

Fostering Innovation Culture

Democratized data unlocks creativity by allowing diverse teams to analyze trends and identify patterns. This decentralized approach to data fosters a culture where innovation thrives, as employees feel empowered to experiment and propose data-driven solutions. Teams are better equipped to brainstorm, prototype, and implement innovative ideas, contributing to overall organizational growth.

Enhancing Operational Efficiency

Operational efficiency is achieved when employees at every level can use data to streamline workflows, reduce redundancies, and optimize resources. A democratized approach ensures that every department can act on insights, leading to coordinated and efficient operations. Whether it’s supply chain optimization, marketing strategy refinement, or customer service enhancements, accessible data makes every process more efficient.

Essential Components of a Successful Data Democratization Strategy

Creating a successful data democratization strategy requires attention to several key components. These elements ensure that the strategy is comprehensive, secure, and user-friendly. Below, we outline the foundational aspects of an effective approach to data democratization.

User-Centric Platforms and Tools

Implementing intuitive tools is critical. Platforms like the Actian Data Intelligence Platform help users access, interpret, and act on data without requiring technical expertise. User-friendly tools reduce the learning curve and make it easier for employees to extract actionable insights from complex datasets.

Solid Data Governance

Data governance ensures data accuracy, security, and compliance. Implementing data governance best practices is crucial for maintaining a reliable data ecosystem. Governance frameworks should define data ownership, accountability, and auditing processes. A well-governed data ecosystem fosters trust among users and ensures compliance with regulatory standards.

Sustained Training and Education

To maximize the potential of democratized data, organizations must invest in continuous training. Employees should be educated on data interpretation, best practices, and the ethical use of information. Training programs should be tailored to different roles within the organization, ensuring that every employee has the skills needed to work effectively with data.

Defined Data Access Protocols

Access protocols ensure the right data is available to the right people at the right time. Role-based access controls (RBAC) and automated workflows can help in managing permissions efficiently and securely. By defining clear access protocols, organizations can balance openness with security, ensuring sensitive data remains protected.

Realizing a Data Democratization Strategy: A Step-By-Step Blueprint

Transforming an organization with a data democratization strategy requires a clear and actionable plan. By following a structured approach, businesses can ensure successful implementation. This section provides a step-by-step blueprint for realizing a data democratization strategy.

1. Assess the Current Data Landscape

Begin by evaluating the current state of data within your organization. Identify existing silos, data flows, and the tools currently in use. This assessment provides a foundation for understanding gaps and areas for improvement. Take inventory of both structured and unstructured data, ensuring that no valuable information is overlooked.

2. Formulate Data Governance Policies

Develop comprehensive data governance policies to safeguard data integrity and security. These policies should outline:

Who owns the data.
Who can access specific datasets.
How data is stored and protected.

Clear governance policies establish a framework for responsible data management, ensuring that democratization efforts are both ethical and effective.

3. Incorporate User-Friendly Tools

Adopt platforms that simplify data analysis for non-technical users. Ensure these tools provide intuitive dashboards, real-time analytics, and easy-to-use interfaces. Prioritize solutions that integrate seamlessly with existing systems to avoid disruptions during the transition. Actian’s Zeenea platform can help you integrate your new data democratization plan effectively.

4. Optimize for Real-Time Data Integration

Real-time data integration ensures that employees always have the most up-to-date information. For insights into the future of data integration, organizations should prioritize systems capable of aggregating data from multiple sources seamlessly. Real-time insights empower teams to act swiftly, turning data into a competitive advantage.

5. Enforce Data Security Measures

Data democratization must be accompanied by robust security measures. A lack of data governance can undermine security, making it essential to implement encryption, multi-factor authentication, and regular audits. Additionally, organizations should conduct periodic vulnerability assessments to address
emerging threats.

6. Encourage Feedback Mechanisms

Gather feedback from users to refine tools, processes, and training programs. User input is invaluable in identifying challenges and opportunities for improvement. Regular feedback helps ensure that the strategy evolves to meet the changing needs of the organization.

7. Implement a Culture of Openness

Foster a culture that values transparency and collaboration. Leaders should champion data democratization efforts and model data-driven decision-making. An open culture encourages employees to embrace data as a core component of their work, driving widespread adoption of democratization initiatives.

8. Build Scalable Infrastructure

Ensure that the underlying infrastructure can scale with organizational growth. For guidance on how to build scalable data platform architectures, consider leveraging cloud-based platforms and modular solutions that offer flexibility and scalability. By investing in scalable infrastructure, organizations can future-proof their data democratization strategy.

Data Democratization FAQs

Frequently asked questions about data democratization highlight its relevance and implementation challenges. Check out our answers to some of these queries to help demystify the concept and provide actionable insights if you are looking to adopt this strategy.

What is Data Democratization?

Data democratization is the process of making data accessible to all employees, enabling informed decision-making at every level.

How Does Data Democratization Impact Security?

By implementing robust governance and security measures, data democratization can enhance security while expanding access.

Who Benefits From Data Democratization?

All stakeholders, from employees to customers, benefit from data democratization as organizations make better decisions and provide improved services.

The Future: Data Democratization and AI

The integration of data democratization and AI is shaping the future of business intelligence. As AI and machine learning advance, the potential of data democratization will expand. AI-powered tools can further simplify data analysis and provide actionable insights to non-technical users. Additionally, predictive analytics and automation will allow businesses to anticipate trends and act proactively. AI will play a role in identifying patterns, detecting anomalies, and delivering recommendations. As these technologies evolve, they will enhance the democratization process, enabling even greater organizational agility and innovation.

In the future, organizations that integrate data democratization with AI will be better positioned to innovate and maintain a competitive edge. The synergy of democratized data and AI-driven insights represents the next frontier in business intelligence.

By embracing data democratization today, businesses can prepare for tomorrow’s challenges and opportunities, ensuring sustainable growth and success. Request a demo to see how Actian’s wide variety of data tools can propel your business to the top of your industry.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

Data Management

Data Catalog vs. Data Dictionary: How Do They Help Businesses?

Actian Corporation

January 20, 2025

In today’s data-driven business world, effectively managing and utilizing data is critical for success. Two tools that organizations often rely on are data catalogs and data dictionaries. While both improve data accessibility and governance, they serve distinct purposes. This article explores their definitions, roles, and how businesses can leverage them to stay competitive.

What You Need to Know: Data Catalog vs. Data Dictionary

Understanding the fundamental differences between a data catalog and a data dictionary is essential to determine how they can enhance your organization’s data strategy.

Defining the Data Catalog

A data catalog is a comprehensive inventory of an organization’s data assets, enriched with metadata to facilitate data discovery and lineage. It centralizes information about where data resides, how it’s structured, and its context for business use, making it an essential tool for managing complex data ecosystems.

Key Data Catalog Features:

Searchability: Enables users to find datasets quickly using keywords or filters.
Metadata enrichment: Provides detailed context about data assets, including their sources and transformations.
Collaboration tools: Support tagging, commenting, and sharing knowledge.
Business glossary integration: Links business terminology to datasets for better clarity.
Governance integration: Ensures adherence to policies for data governance and compliance.

Explore data catalog examples to see how businesses use these tools to streamline data management and improve decision-making.

Understanding the Data Dictionary

A data dictionary definition refers to a structured reference guide containing detailed information about the data elements in a specific database or system. It is focused on standardizing and clarifying data fields for technical users such as database administrators and developers.

Key Features of a Data Dictionary:

Field definitions: Describes each data element, including name, type, format, and allowed values.
Relationship mapping: Illustrates connections between datasets or tables.
Standardization: Promotes consistent data usage across systems.
Compliance support: Helps organizations meet regulatory requirements by clearly defining sensitive data elements.

Unlike a data catalog, which provides a high-level overview, a data dictionary delves into technical specifics, making it one of the most effective metadata management tools for database optimization.

The Crucial Role of Data Catalogs and Dictionaries in Modern Business

Data catalogs and dictionaries are integral to managing and governing data effectively, supporting organizations in maximizing data’s potential.

Importance of Data Catalogs in Modern Data Management

A data catalog provides a centralized platform for managing and utilizing metadata, which is essential for large organizations dealing with data silos. Here are some data catalog benefits:

Improved discoverability: Facilitates data discovery and lineage by providing visibility into data assets.
Data democratization: Enables non-technical users to access and understand data confidently.
Metadata management: Offers rich contextual details, making data easier to interpret and use.
Compliance assurance: Integrates with governance policies to ensure regulatory alignment.

The Actian Data Intelligence Platform is an example of how data catalogs streamline metadata management and foster collaboration across teams.

How Data Dictionaries Support Data Governance and Compliance

A well-structured data dictionary enhances governance by standardizing data definitions and usage across the organization. Its use cases include:

Data standardization: Ensures that data is consistently labeled and interpreted.
Regulatory compliance: Defines sensitive data clearly to meet legal requirements.
Database management: Optimizes database performance with detailed structural insights.
Application development: Provides developers with clear guidelines for working with data structures.

Learn more about why data governance is important and how data dictionaries contribute to compliance and data quality.

Harnessing the Power of Data: Business Applications of Data Catalogs and Dictionaries

When applied effectively, data catalogs and dictionaries enhance data accessibility and usability, driving better decision-making across the organization.

How Businesses Can Leverage Data Catalogs Effectively

By utilizing the features of a data catalog, businesses can:

Boost productivity: Reduce time spent searching for data and focus on analysis.
Enable self-service analytics: Empower teams to access and understand data independently.
Track data lineage: Build trust by providing insights into data origins and transformations.
Facilitate collaboration: Use tools like tagging and annotations to share knowledge.

Discover more about the benefits of data discovery and how it integrates with data catalogs to unify your organization’s data strategy.

Practical Use-Cases of Data Dictionaries in Business

Data dictionaries play a critical role in scenarios requiring precision and standardization. Common data dictionary use cases include:

System migrations: Ensuring seamless data mapping during platform transitions.
Compliance reporting: Providing detailed definitions of data for regulatory audits.
Application development: Guiding developers with clear data element definitions.
Error resolution: Supporting troubleshooting with precise technical details.

How Data Catalogs and Dictionaries Complement Each Other

While their functionalities differ, data catalogs and dictionaries often work together to provide a comprehensive framework for managing data:

A data catalog provides a high-level overview, focusing on data catalog vs metadata management and discoverability.
A data dictionary dives into granular details, offering technical clarity and ensuring standardized data usage.

For more insights, download the eBook: “What is a Smart Data Catalog?” to learn how these tools can work together to enhance your data strategy.

Data Catalog vs. Data Dictionary: Making the Right Choice for Your Business

Choosing the right tool depends on your business needs and the scale of your data environment.

Factors to Consider When Choosing Between a Data Catalog and Data Dictionary

When evaluating these tools, consider:

Complexity of data: Large-scale datasets often benefit more from a data catalog’s features.
Intended users: Data dictionaries are suited for technical teams, while data catalogs are designed for broader audiences.
Business goals: Use a catalog for discoverability and governance, and a dictionary for standardization and technical details.

Evaluating Business Requirements for Data Management

Ask these questions to determine your needs:

Who will use the tool? Is your primary audience technical or non-technical?
What are your challenges? Are you struggling with locating data or understanding its structure?
What is the scale of your data? Do you manage diverse and distributed datasets?

In many cases, integrating both tools ensures comprehensive data management.

Use Data Catalogs and Data Dictionaries to Manage Your Company’s Data

Combining the features of a data catalog with the technical precision of a data dictionary enables organizations to build a robust data strategy. These tools are the foundation for better collaboration, compliance, and innovation.

By aligning your data tools with your business needs, you can turn your data into a powerful asset for long-term success. Sign up to join a demo to see how Actian products can help you manage and govern your data.

About Actian Corporation

Data Management

Decentralize, Democratize, Differentiate: Embrace Data Products

Guillaume Bodet

January 16, 2025

The evolution of data management is being shaped by the decentralization of data ownership and the rise of new organizational needs. Traditional data management approaches—often centralized and cumbersome—are no longer sufficient to meet growing business demands for speed, accessibility, and quality.

Decentralization promotes accountability and ensures that data is managed by those with the deepest understanding of its context and relevance. These domains also are responsible for creating data products so the most valuable data assets can be shared and used across the organization.

ISG Research defines data products as “the outcome of data initiatives developed with product thinking and delivered as reusable data assets that can be discovered and consumed by others on a self-service basis, along with associated data contracts and feedback options.” They can be a domain specific data set, an algorithm, or a machine learning model.

Your company, like other modern organizations, requires ready-to-use data products that are easily accessible by analysts and business teams across the organization. Data products are expected to provide immediate value to both technical teams and data users in the business. The problem is that too often, data volumes are too vast to be useful—they overwhelm analysts and other data users.

That’s why data products should leverage metadata—the data about your data—because the value of data is activated when metadata is captured and utilized. In practice, an operational data product consists of both data and metadata, and the latter ensures that consumers have all the information they need to use the product. Tools that utilize metadata allow you to quickly find, understand, and manage data products via an internal data marketplace to unlock new business value and drive outcomes faster. Importantly, data products must allow users in your organization to access and use the data they need without barriers such as requiring specialized skills or IT help.

How to Choose the Right Solution

For data product platforms to succeed, they must be designed with usability and product experience in mind. Business leaders, analysts, and developers often have diverse skillsets and needs, making intuitive interfaces and data integration key factors. Additional factors to consider include self-service functionality that minimizes reliance on IT teams; clear documentation and data lineage to build trust and transparency; and feedback mechanisms to continuously refine and improve data quality.

Data products should excel in being:

Cleansed, transformed, high quality, and ready for analysis.
Accessible: Barrier-free access to data users and others who need it.
Datasets and data elements are seamlessly unified into a single, trusted unit, enabling effortless distribution.
Searchable and understandable. Metadata-driven and domain-centric so the data products are easy to find and understand.
Composed of one or more data assets that work together for rich data insights.
Reusable: Built from composable elements that can be leveraged to create multiple data products, including derivatives.

When data products are easy to use and have the capabilities you need, they enable you and your teams to innovate, collaborate, and make decisions with confidence—qualities that drive business growth.

The Business Case for Decentralizing Data Ownership

Decentralization of data is a concept supported by a data mesh architecture, which promotes the democratization of data across a business. Unlike centralized data warehouses, a decentralized approach, as highlighted in this practical guide to data mesh, federates data and delegates data ownership to specific business domains.

With businesses adopting decentralized models, the demand for advanced metadata solutions that enable data discovery is only expected to grow. According to the I SG Buyers Guide™ for Data Products, by 2027, more than 60% of enterprises are projected to adopt technologies that facilitate the delivery of data as a product as cultural and organizational approaches to data ownership, in the context of data mesh, evolve.

The move to decentralized data ownership requires a new focus on metadata management, which is where the right product can help. For example, a data discovery platform organizes metadata, providing a unified view of data assets across the organization. In addition, an internal data marketplace enables users to consume the data for other uses.

Treating data like a product that can be packaged, discovered, and consumed by others across your organization helps you achieve:

Quickly find the data you need.
Ensure that data from different domains works together seamlessly.
Maintain compliance and quality standards across your organization.

Gaining Trusted Insights into Data Products

It’s not surprising that many technology buyers and users have a lot of questions about the value, usability, capabilities, limitations, and return on investment of data products. A LinkedIn post from Ehtisham Zaidi, Gartner VP Analyst and Key Initiative Leader, Data Management, says that his team has fielded more than 500 inquiries about data products.

He notes that most organizations are not defining data products correctly, with some believing that any integrated dataset is a product. He says that most organizations also struggle with understanding and enabling core data components, such as data governance and management. As a result, organizations have questions about operationalizing and sharing data using a data marketplace.

As your business requirements evolve, new use cases for data arise, and you move toward a decentralized approach, understanding which data solutions meet your needs is increasingly important. You also need to know which solutions can address common challenges such as fragmented data, inefficient workflows, and a lack of confidence in data quality.

Resolving these issues requires implementing modern technology. As noted in the ISG Buyers Guide for Data Products, look for solutions that apply “product thinking” to data initiatives, treating data as reusable, shareable, and consumable assets. This approach helps you:

Streamline Access to Data. Easily discover and use data on a self-service basis using an enterprise data marketplace.
Enhance Data Confidence. Ensure the reliability and discoverability of data by leveraging metadata.
Accelerate Time-to-Value. Deliver actionable insights faster by leveraging easy-to-access, high-quality data.

Selecting the ideal data solution requires careful evaluation. The ISG Buyers Guide for Data Products, like the ISG Buyers Guide for Data Platforms, is a trusted, third-party resource for organizations looking to navigate this complex product landscape. The guide evaluates leading software providers, offering insights into usability, manageability, customer experience, and other critical factors.

For insights into choosing the right products for your business, download your complimentary copy of the ISG Buyers Guide for Data Product s. It evaluates 19 vendors based on a comprehensive set of criteria in various categories including product experience, capabilities, reliability, customer experience, return on investment, and more.

The guide explains how innovative solutions like the Actian Data Intelligence Platform are transforming the way companies manage, democratize, and consume data. The guide positions the platform as Innovative and a Leader in Manageability with strong performance in Customer Experience. If you’re looking to modernize, decentralize data, or implement data products, this essential guide can help inform your buying decision.

As the push to decentralize data continues, businesses like yours must adopt modern approaches to stay competitive. By investing in user-friendly, value-driven data products and an e-commerce-like internal data marketplace, you can harness the full potential of your data, enabling faster insights and confident decision-making.

About Guillaume Bodet

Guillaume Bodet is Chief Product Officer at Actian, defining and driving product vision for data management and intelligence. With 15+ years in the data industry, Guillaume's expertise spans architecture, innovation, machine learning, and analytics. Prior to Actian, he co-founded Zeenea, a data catalog startup. He has delivered keynote presentations at data summits and is a champion of market-leading data solutions. Guillaume's Actian blog posts cover strategy, data cataloging, and product roadmaps. Check his latest insights to stay ahead in the evolving data landscape.

Databases

Securing Your Data With Actian Vector, Part 2

Martin Fuerderer

January 13, 2025

Securing Your Data With Actian Vector hero image

Following up on my first blog post about Actian Vector’s database encryption capabilities, the next blog post in the series on data security explains function-based encryption in Actian Vector.

Function-Based Encryption in Actian Vector

The encryption that is completely controlled by a database application is called function-based encryption in Actian Vector. The application uses a pair of built-in SQL functions to encrypt and decrypt individual data values in table columns. Apart from applying these functions on the data as told by the SQL statements, the database server does not handle the encrypted data in any speciﬁc way. In particular, the database server is not capable of decrypting the encrypted data on its own.

The application specifies a passphrase with the encrypt function and needs to specify the same passphrase again with the decrypt function. The database server derives a symmetric encryption key from the given passphrase and uses this key to encrypt or decrypt the data. Neither the passphrase nor the derived encryption key are stored anywhere in the database server. The application is always responsible for supplying the correct passphrase to the encrypt and decrypt function.

Typically, the application chooses a passphrase and uses the encrypt function when inserting data values into a table. The data gets encrypted based on the chosen passphrase and then stored. When accessing the stored data, either during select or update, the application uses the decrypt function with the same chosen passphrase to get the data in cleartext. Accessing encrypted data without the decrypt function simply retrieves the encrypted data, which normally is not very useful.

The application can specify diﬀerent passphrases with individual invocations of the encrypt and decrypt functions. For example, diﬀerent database users can each use their own passphrase to insert data with encryption into the same table. The users can be sure that no other user can see the cleartext of their own data. Or, an application can encrypt data for diﬀerent table columns with diﬀerent passphrases, and provide the diﬀerent passphrases to diﬀerent sets of users. That way, one group of users can see the cleartext data only in one column whereas another group of users can see the cleartext of data in the other column.

The encrypt function can be applied multiple times with diﬀerent passphrases on the same data. To access the data in cleartext, the same passphrases must be used in reverse order to decrypt the data. When updating encrypted data, the decrypt function is applied ﬁrst, the cleartext value is updated, then the encrypt function is used to newly encrypt the updated data. These are just a few examples of the endless possibilities to combine the use of the encrypt and decrypt functions, all controlled by the application.

All the function-based encryption is done by using the crypto functions in regular SQL statements. This also means that the cleartext passphrases are provided to the crypto functions in the SQL statements. Proper use of function-based encryption therefore should take care that the SQL statements are kept suﬀiciently secure, e.g. that they are not traced or made visible in some SQL command history.

Also, communication between the application and the database server should be secured to avoid leaking the passphrases. With this regard, the “sql” Terminal Monitor utility is not really a secure example of an application because the typed-in cleartext passphrases in the SQL statements are visibly echoed in the terminal Window.

Examples of Function-Based Encryption:

Deﬁnition of the table used in the following examples:

CREATE TABLE t1 (name CHAR(20), salary VARCHAR(50)); \g

Even though the salary is a numerical value, the “salary” column is deﬁned as a suﬀiciently large character column to accommodate the bytes of the encrypted value.

Insert data with encryption into a table:

INSERT INTO t1 values ('Joe', AES_ENCRYPT_IV(40000, 'Secret passphrase')); \g
INSERT INTO t1 values ('Adam', AES_ENCRYPT_IV(38000, 'Secret passphrase')); \g
INSERT INTO t1 values ('CEO', AES_ENCRYPT_IV(350000, 'Super passphrase')); \g

The ﬁrst two statements encrypt the salary value based on the passphrase ‘Secret passphrase’, whereas the third statement uses the diﬀerent passphrase ‘Super passphrase’.

Selecting encrypted data:

1. Select without using the decrypt function:

SELECT name, salary FROM t1; \g
name          salary
Joe           XnE_msRe1?Uh]!cUUerQn4opT(C[!)O~yTrzE%JS
Adam          lyr*M%o53]O(5d$2`!3a:Bg$zW40wMySO5[-E7+u
CEO           a*#eE!~p(1-euLx?K$w(2)@FHk+dzM`5}Q#=lD@g
(3 rows)

The encrypted data in the “salary” column is shown encrypted and therefore meaningless. The length of the output for the salary column demonstrates that encrypted data usually takes more space than the corresponding cleartext values.

1. Select using the decrypt function with the ﬁrst passphrase:

SELECT name, AES_DECRYPT_IV(salary, 'Secret passphrase') FROM t1; \g

name          col2
Joe           40000
Adam          38000
CEO                  \020���x���
(3 rows)

The values encrypted with the ﬁrst passphrase are correctly decrypted. The value encrypted with a diﬀerent passphrase is not shown in cleartext.

1. Select a speciﬁc data record using its correct passphrase:

SELECT name, AES_DECRYPT_IV(salary, 'Super passphrase')
  FROM t1 WHERE name = 'CEO'; \g

name		col2
CEO		350000
(1 row)

The encrypted value is shown in cleartext.

When selecting encrypted data, the decrypt function needs the correct passphrase to show the cleartext. When using diﬀerent passphrases for diﬀerent data records, it is better to use a WHERE clause that restricts the result to those data records encrypted with the passphrase speciﬁed in the SELECT.

Updating encrypted data:

When updating encrypted data, the data needs to be decrypted and usually should be encrypted again after the update. For this, both functions, decrypt and encrypt, are used in the same UPDATE statement. At the same time it is also possible to change the passphrase.

UPDATE t1 SET salary = AES_ENCRYPT_IV(
AES_DECRYPT_IV(salary, 'Super passphrase') + 20000,
'Secret passphrase' ) WHERE name = 'CEO'; \g

The statement adds 20000 to the salary of ‘CEO’ and at the same time changes the passphrase to ‘Secret passphrase’. The decrypt function requires the original passphrase and the encrypt function is provided with the new passphrase. As the new passphrase is the same one that was used to encrypt the salary of the other two data records, a SELECT using this passphrase now can show the cleartext for all records:

SELECT name, AES_DECRYPT_IV(salary, 'Secret passphrase') FROM t1; \g

name          col2
Joe           40000
Adam          38000
CEO           370000
(3 rows)

The next blog post in this series will explain how encryption keys for the database encryption are used in Actian Vector.

About Martin Fuerderer

Martin Fuerderer is a Principal Software Engineer for HCLSoftware, with 25+ years in database server development. His recent focus has been on security features within database environments, ensuring compliance and robust data protection. Martin has contributed to major product releases and frequently collaborates with peers to refine database security standards. On the Actian blog, Martin shares insights on secure database server development and best practices. Check his latest posts for guidance on safeguarding enterprise data.

Data Platform

Why Analyst Reports Matter: Actian Leads the Way in Data Management

Jennifer Jackson

January 8, 2025

In a world where data-driven decisions define success and determine business outcomes, organizations often turn to industry technology analysts for trusted insights when choosing technology. Analyst reports provide a clear picture of emerging trends, competitive landscapes, and the vendors setting the pace.

These insights help businesses across all industries navigate complex markets, make informed IT investments, and strategically align their technology choices with both short- and long-term goals. By leveraging the research and expertise of analysts, companies gain the confidence needed to select the right platform and approach to drive innovation.

Recent analyst reports–several of which highlight Actian products–show how leading technologies play a pivotal role in managing and leveraging data throughout an organization. Here’s why business and IT leaders should pay attention to these reports and why Actian capabilities matter to modern companies.

Enabling Trusted Data Management Across All Environments

Actian has solidified its position as a key player in data management, earning recognition as an Honorable Mention vendor in the 2024 Gartner® Magic Quadrant™ for Cloud Data Management Systems. We are also included in the Forrester Data Management for Analytics Platforms Landscape, 2024. In addition, Actian was named an Innovative provider in ISG Buyers Guide™ for Data Products. These acknowledgments underscore Actian’s leadership in providing comprehensive data management products.

Why managing data matters. Effective data management–from improving data accuracy to integrating disparate sources and enabling actionable insights–is the backbone of modern business success. With the growing reliance on cloud environments, businesses must ensure their data is secure, accessible, and optimized for decision-making. The cloud offers unparalleled scalability and flexibility, but without robust data management, companies risk inefficiencies, compliance challenges, and missed opportunities.

Whether operating in cloud-first, hybrid, or on-premises settings, organizations must have the ability to manage trusted data efficiently. Actian understands that customers have data spread across multiple environments and can help them with their data no matter where it resides.

Supporting a Range of Workloads With Translytical Data Platforms

Forrester defines translytical data platforms as next-generation data solutions built on a single database engine to support transactional, operational, and analytical workloads without impacting data integrity, performance, or real-time analytics. This trend is critical as businesses strive to unlock real-time insights for various workloads. Actian’s inclusion as a Contender in the Forrester Wave™: Translytical Data Platforms, Q4 2024, reflects our leading capabilities to unify transactional and analytical workloads.

Forrester notes platform strengths and offers recommendations for organizations considering implementation:

Accelerate use cases through advanced self-service and automation. Look for vendors that provide a multi-persona user interface to facilitate collaboration, automate all translytical functions, offer translytical solutions as a service, and incorporate automation for tiered storage.
Enhance data management capabilities to support a wide range of use cases. Look for comprehensive data management support for data integration, curation, vector search, large language model integration, processing, transformation, security, metadata management, and connectivity to diverse data repositories.
Enable distributed processing and transactions across diverse data and clouds. Look for a platform that can leverage external data sources while ensuring consistency, such as databases, lakehouses, software as a service, devices, and marketplaces in data processing and transactions across hybrid and multiple clouds.

Why handling multiple workloads matters. Businesses require data platforms that keep up with the demand for real-time insights and operational efficiency. Translytical data platforms are emerging as a modern solution to drive faster insights, reduce complexity, and ensure data integrity. They enable businesses to streamline operations. By performing both transactional and analytical workloads, they allow businesses to streamline operations and support real-time decision-making without the need for separate systems.

Optimizing Industry-Leading Data Platform Software

The 2024 Data Platforms Buyers Guide from Ventana Research/ISG Research offers a comprehensive evaluation of 25 data platform providers, assessing their ability to meet buyers’ diverse needs. Actian’s designation as Exemplary highlights our commitment to delivering a platform that excels in functionality, innovation, and value.

Actian performed exceptionally well in the Manageability category, which evaluates how well products can be managed technologically and by business users, among other criteria. Actian was ranked as a Leader in the category.

With the ability to support both operational and analytic workloads, the Actian platform meets organizations’ need to perform a variety of use cases, including those involving GenAI. As ISG notes, “We assert that through 2027, the development of intelligent applications providing personalized experiences driven by GenAI will increase demand for data platforms capable of supporting hybrid operational and analytic processing.”

With capabilities spanning data integration, quality, and usability, the Actian Data Platform equips businesses to address complex data challenges and capitalize on growth opportunities. The recognition from ISG Research validates Actian’s industry leadership and ability to empower organizations with reliable, high-performing data platform software.

Why understanding the data platform landscape matters. Understanding the data platform ecosystem is crucial for businesses that want to stay competitive. With the right data platform, organizations can unify disparate data sources, improve data governance, and drive actionable insights. Staying informed about the capabilities of top providers helps businesses make strategic investments, ensuring their chosen platform aligns with both current and future needs. By leveraging insights from evaluations like analysts’ buyers guides, companies can confidently select solutions that optimize performance and support their ongoing needs.

Ensuring Data Quality for Diverse Use Cases

Data quality is the foundation of data-driven decision-making and operational excellence. Actian’s recognition as Exemplary in ISG Research’s 2024 Data Quality Buyers Guide reflects our platform’s ability to address business needs with robust data validation, cleansing, and enrichment capabilities. Whether ensuring accurate reporting, improving customer experiences, or supporting GenAI use cases, the Actian platform enables organizations to confidently optimize their data across a variety of use cases.

As ISG noted in its report, data quality is increasingly important for modern tools, such as those supporting machine learning. “Although data quality software has historically provided users with an environment to check and correct data quality issues manually, the use of machine learning (ML) to automate the monitoring of data is being integrated into data quality tools and platforms to ensure that data is complete, valid, and consistent as well as relevant and free from duplication.”

Why data quality matters for analysts and business users. Data quality directly impacts outcomes, including accurate analytics, reliable forecasting, and sound decision-making, reducing the risk of costly mistakes. Poor data quality, on the other hand, leads to inefficiencies, mistrust, and missed opportunities. For data analysts, data quality can be the difference between actionable insights and misleading conclusions, while for business users, it ensures processes run smoothly and decisions deliver value.

Making Data Integration Easy—No IT Skills Required

For data to reach its full potential, it must be integrated on a single platform, then made available to everyone who needs it. As data becomes available from more sources than ever before, connecting data can be tricky. It requires scalability and the ability to integrate systems, applications, and data sources. Requiring IT or specialized skill sets for integration creates bottlenecks and slows down insights.

Actian’s data integration capabilities stand out for their user-friendly design, enabling organizations to connect, transform, and manage data without requiring advanced IT skills. That’s why Actian was recognized as Exemplary in ISG Research’s 2024 Data Integration Buyers Guide. Our platform delivers tools that empower users to take control of their data integration processes. Intuitive interfaces and powerful automation simplify complex workflows, helping businesses unlock insights faster and more efficiently.

Data integration is playing an important role in data intelligence, serving as a critical layer in the tech stack. Data integration is enabling enterprises to extract data from applications, databases, and other sources and combine it for analysis in a data warehouse or data lakehouse with the intention of generating business insights, according to ISG Research. “Without data integration, business data would be trapped in the applications and systems in which it was generated.”

Why simplifying data integration matters. Bringing together data is crucial for organizations striving to keep pace with the rapidly increasing volume and complexity of data. When integration is made easy, teams across the organization benefit by accessing and using data without limitations, fostering innovation and improving collaboration. By reducing the technical barriers to integration, businesses can focus on leveraging their data to create value, improve operations, and respond quickly to market demands.

Proven Value Backed by Analyst Reports

In this era when data is the currency of innovation, organizations must implement industry-recognized technologies that deliver tangible results. Analyst reports serve as guideposts for navigating data products and features. They offer independent validation of vendors’ capabilities and provide a framework for assessing which solutions align with an organization’s specific goals. For businesses evaluating data platforms, these reports help differentiate between hype and genuine leadership.

Actian’s recognition in multiple analyst reports—including those from Gartner, Forrester, and ISG Research—highlights our ability to deliver measurable value across data manageability, quality, and integration, among other benefits. You can read more about our offerings in the IDC Vendor Profile and the Bloor Research InBrief. These accolades reflect not just market trends but the real-world impact of Actian’s product suite.

Now, we have added even more capabilities to optimize data, such as metadata management and data discovery. As organizations grapple with disparate data sources, our ever-expanding range of capabilities enable seamless discovery, understanding, and utilization of data. Have questions about our products? Connect with one of our experts.

About Jennifer Jackson

Jennifer"JJ" Jackson is CMO of Actian, leading global marketing strategy with a data-driven approach. With 25 years of branding and digital marketing experience and a background in chemical engineering, JJ understands the power of analytics from both a user and marketer perspective. She's spearheaded SaaS transitions, partner ecosystem expansions, and web modernization efforts at companies like Teradata. On the Actian blog, she discusses brand strategy, digital transformation, and customer experience. Explore her recent articles for real-world lessons in data-driven marketing.

AI & ML

The Key to Reaching GenAI’s Full Potential: Data Quality

Actian Corporation

December 31, 2024

The Key to Reaching GenAI’s Full Potential: Data Quality

Generative AI (GenAI) promises to revolutionize industries—from automating business processes and improving decision-making to driving innovation at never-before-seen speeds. However, behind every successful GenAI model lies a foundational truth: GenAI is only as good as the data that fuels it.

When data is incomplete, inconsistent, or inaccurate, even the most advanced GenAI tools will deliver flawed results. As a result, businesses risk poor decisions, operational inefficiencies, compliance failures, and reputational damage.

For organizations looking to optimize and scale GenAI outcomes, ensuring high-quality data isn’t just a technical requirement—it’s a strategic imperative. Like other data-driven use cases, GenAI requires trusted data that gives you full confidence in the results.

Data Quality Matters

Organizations racing to adopt GenAI are realizing that data quality can make or break their investment. High-performing GenAI models rely on large volumes of clean, trusted data to train, predict, and deliver valuable outcomes. Poor data, on the other hand, introduces biases, amplifies errors, and undermines trust.

Consider the following cautionary examples:

An online real estate company misused AI to predict housing prices, leading to a $300 million inventory write-down and mass layoffs.
A major airline chatbot provided incorrect legal advice, exposing the company to costly liabilities.
In hiring, AI recruiting tools introduced unintended biases, leading to legal and ethical scrutiny.

These cases demonstrate that flawed inputs—whether incomplete, inconsistent, or inaccurate data—produce unreliable AI outputs. In short, the “garbage in, garbage out” mantra applies to GenAI data.

According to Gartner, 30% of GenAI projects will fail by the end of 2025 due to poor data quality, unclear business value, and inadequate risk controls. These risks are real, but they are also avoidable. Ensuring high-quality data is the single most effective way to unlock the promise of GenAI.

5 Challenges in Ensuring Data Quality

Data professionals face several key challenges when preparing their data for GenAI. They include:

Disparate and Siloed Data Sources
Many organizations struggle with fragmented data across systems, regions, or departments. Without unified, consistent data, GenAI models lack the full picture to deliver meaningful insights.
Data Volume and Complexity
Businesses now manage massive, ever-growing data volumes coming from more sources than ever. Managing petabytes of sensor data, transactional records, time-series inputs, and other data requires modern solutions capable of real-time processing.
Outdated or Incomplete Data
Stale or flawed data leads to inaccurate predictions. For GenAI to remain relevant, data must reflect real-time updates, evolving business needs, and shifting trends.
Lack of Governance and Transparency
Without proper governance, organizations cannot ensure data accuracy, lineage, and compliance—critical elements for GenAI to meet regulatory and business standards.
Inconsistent Data Quality Processes
Many organizations lack standardized processes for maintaining data quality across departments and systems. Inconsistencies in data validation, cleansing, and monitoring can result in discrepancies that undermine the accuracy and ultimately the confidence in GenAI outputs. Without a unified approach, organizations struggle to ensure data remains reliable, up-to-date, and aligned with business goals.

The Solution: Building a Strong Foundation for Data

A unified data platform addresses these challenges, ensuring businesses can deliver clean, trusted, and integrated data to their GenAI models. This type of platform provides a comprehensive solution that supports:

Data Integration. The platform should seamlessly integrate data from disparate systems, creating a single, unified source of truth. This eliminates silos and ensures GenAI has access to all relevant inputs.
Real-Time Processing. A unified, scalable platform can handle massive data volumes, streaming extremely large data sets to power real-time predictions and insights.
Data Quality Management. From data deduplication to error detection, the platform can ensure that data is accurate, complete, and reliable before feeding it into GenAI models.
Governance and Compliance. Built-in governance tools in modern data platforms provide transparency and trust, allowing organizations to meet regulatory standards with confidence.
Scalability. A platform’s ability to quickly scale supports modern data formats, enabling businesses to integrate all required data to scale their GenAI operations.

With the right platform, businesses can cleanse, connect, and prepare their data for GenAI use cases—whether automating workflows, delivering predictive analytics, optimizing operations, or achieving other business goals. A platform can be a driving force for GenAI success—or limit outcomes. That’s why organizations must understand their needs and implement a platform that meets their current and future requirements.

Achieving GenAI Excellence: The Benefits of Quality Data

When organizations address data quality challenges, they unlock the full potential of GenAI and other data-driven use cases. Ongoing benefits include:

Accurate Insights. High-quality inputs result in reliable, actionable outputs, empowering smarter decision-making.
Operational Efficiency. Automating tasks with GenAI reduces manual effort, increases productivity, and frees staff time for other tasks.
Cost Savings. Clean data minimizes costly errors and accelerates the return on investment (ROI) for GenAI.
Innovation. With trusted data, businesses can confidently deploy GenAI for complex use cases, like predictive maintenance, demand forecasting, and customer personalization.
Competitive Advantage. Data-driven insights enabled by GenAI allow businesses to move faster, adapt to change quickly, and outperform competitors.

Take the Next Step: Prepare Your Data for GenAI

The journey to successful GenAI adoption starts with data readiness. Organizations that prioritize data quality will not only solve common challenges but also accelerate innovation and deliver measurable business outcomes.

The eBook “Realize the Promise of GenAI Today—and Avoid Common Pitfalls” offers proven strategies to help organizations ensure their data is ready for GenAI. It offers seven steps to achieve data readiness for GenAI and shares strategies to future-proof your data infrastructure for GenAI-driven success. It can equip organizations with the tools and knowledge to make GenAI work for them—powered by clean, trusted, and accurate data.

About Actian Corporation

Best Practices

The 7 Fundamentals That Are Crucial for CDO Success in 2025

Dee Radh

December 24, 2024

As data volumes continue to rapidly grow and organizations become increasingly data driven in the AI age, the data landscape of 2025 is poised to be more dynamic and complex than ever before.

For businesses to excel in this fast-evolving environment, chief data officers (CDOs) of the future must move beyond their traditional roles to become strategic transformation leaders. Key priorities will shape their agenda and be a driving force for success in an era of sweeping change.

The eBook “Seven Chief Data Officer (CDO) Priorities for 2025,” explores seven key priorities that will define successful data leadership in 2025. From crafting unified data strategies that feel less like governance manifestos and more like business transformation blueprints, to preparing trusted data for the AI revolution, you will learn:

What tomorrow’s successful CDOs look like.
The seven fundamentals that are crucial for CDO success.
Practical strategies for data management in 2025.

Expanding from Data Custodian to Strategic Visionary

The role of the CDO has undergone a significant change over the last few years—and it’s continuing to be redefined as CDOs prove their value. CDOs are now unlocking competitive advantages by implementing and optimizing comprehensive data initiatives. That’s part of the reason why organizations with a dedicated CDO are better equipped to handle the complexities of modern data ecosystems and maintain a competitive edge than those without this role.

As noted in our eBook “Seven Chief Data Officer (CDO) Priorities for 2025,” this critical position will become even more strategic. The role will highlight a distinct difference between good companies that use data and great companies that rely on data to drive every business decision, accelerate growth, and confidently embrace whatever is next.

The idea for this eBook began with a simple observation: The role of CDO has become a sort of organizational Rorschach test. Ask 10 executives what a CDO should do, and you’ll get 11 different answers, three strategic frameworks, and at least one person insisting it’s all about AI (it’s not).

While researching this piece, a fascinating pattern emerged. Data strategy isn’t just about governance and quality metrics, but about fundamental business transformation. But perhaps most intriguing is the transformation of the CDO role itself. What started as a data custodian and governance guru has morphed into something far more nuanced: Part strategist, part innovator, part ethicist, and increasingly, part business transformer.

The eBook dives deeper into these themes, offering insights and frameworks for navigating this evolution. But more than that, it attempts to capture this moment of transformation–where data leadership is becoming something new and, potentially, revolutionary.

The seven priorities outlined in the eBook aren’t just predictions; they’re emerging patterns. When McKinsey tells us that 72% of organizations struggle with managing data for AI use cases, they’re really telling us something profound about the gap between our technological ambitions and our organizational readiness. We’re all trying to build the plane while flying it–and some of us are still debating whether we need wings.

This eBook is for leaders who find themselves at this fascinating intersection of technology, strategy, and organizational change. Whether you’re a CDO looking to validate your roadmap, or an executive trying to understand why your data initiatives feel like pushing boulders uphill, we hope you’ll find something here that makes you think differently about the journey ahead.

Download the eBook if you’re curious about what data leadership looks like when we stop treating it like a technical function and start seeing it as a strategic imperative.

About Dee Radh

As Senior Director of Product Marketing, Dee Radh heads product marketing for Actian. Prior to that, she held senior PMM roles at Talend and Formstack. Dee has spent 100% of her career bringing technology products to market. Her expertise lies in developing strategic narratives and differentiated positioning for GTM effectiveness. In addition to a post-graduate diploma from the University of Toronto, Dee has obtained certifications from Pragmatic Institute, Product Marketing Alliance, and Reforge. Dee is based out of Toronto, Canada.

Data Management

What is Data Stewardship?

Actian Corporation

December 20, 2024

 Data stewardship is a critical business function that oversees and manages an organization’s data assets. It ensures that high-quality data is available to consumers while adhering to data governance policies. In today’s data-driven business landscape, effective data stewardship is essential for organizations that rely on high-quality and efficient data to make informed decisions, drive innovation, and maintain a competitive edge.

For any organization to implement a successful data program, its members must be aware of their responsibilities. Data stewardship helps ensure stakeholders are given clear responsibilities aligning with overall goals and data governance. This article will explore the concept of data stewardship, its importance in modern businesses, and how it benefits both data consumers and producers. It will also delve into the role of data stewards, best practices for implementing successful data stewardship programs, and background information to help harmoniously integrate data stewardship plans into data governance programs. 

The Role of a Data Steward

To better answer “What is data stewardship?” it helps to understand what a data steward does. Data stewards play a crucial role in ensuring the quality, security, and accessibility of an organization’s data assets. Their responsibilities span various aspects of data management, impacting multiple departments and stakeholders within the business.

On a daily basis, data stewards:

Monitor Data Quality: They continuously assess and maintain data quality standards, ensuring that data is accurate, consistent, and reliable across all systems and processes.
Implement and Enforce Data Governance Policies: Data stewards work closely with the data governance team to implement and enforce data usage, security, and compliance policies.
Collaborate With Stakeholders: They act as liaisons between different departments, such as IT, business units, and compliance teams, to address data-related issues and requirements.
Manage Metadata: Data stewards oversee the creation and maintenance of metadata, which helps users understand the context, origin, and quality of data assets.
Resolve Data Issues: They investigate and resolve data quality issues, discrepancies, and conflicts that arise within the organization.
Provide Data-Related Training and Support: Data stewards educate users across the organization on data best practices, tools, and processes.
Participate in Data-Related Projects: They contribute to data integration, migration, and analytics initiatives to ensure data quality and consistency throughout these processes.

Data stewards commonly work with several departments and stakeholders across the organization. They collaborate closely with IT departments on data infrastructure, security, and integration projects. Compliance and legal teams work hand-in-hand with data stewards to ensure data management practices meet compliance standards. Data analytics teams benefit from the support of data stewards in data preparation and quality assurance for their analytical initiatives. At the executive level, data stewards provide valuable insights on data asset management and its impact on business objectives, helping to shape overall data strategy.

In a larger organizational sense, data stewards advocate for end users, ensuring that data producers and IT organizations meet the needs of those who rely on high-quality, accessible data. They play a crucial role in supporting regulatory compliance, and by maintaining high-quality data, they enable executives and managers to make informed decisions based on reliable information. Perhaps most importantly, data stewards help organizations recognize and leverage the value of their data assets, promoting data as a strategic asset that can drive business growth and innovation.

What is Data Stewardship’s Relation to Data Governance?

Data stewardship and data governance work together to ensure the effective management of an organization’s data. While data governance provides the overall framework, policies, and standards for data management, data stewardship focuses on the day-to-day implementation and execution of these policies. 

In terms of organizational structure, data stewardship often operates within the broader data governance framework:

Data Governance Managers: Sets overall strategy and policies.
Data Stewardship Managers: Coordinates data stewardship activities across the organization.
Domain Data Stewards: Manage data within specific business domains or functional areas.

This hierarchical structure ensures that data stewardship activities align with the organization’s overall data governance goals while allowing for specialized management of data assets within different areas of the business. 

Best Practices for Data Stewardship Programs

Now that you’re familiar with its larger place in an organization, you need to know what data stewardship practices can make your program as successful as possible. Managers of successful data stewardship programs employ several best practices to maximize efficiency and achieve business goals:

Establish Clear Roles and Responsibilities: Define the roles of data stewards, data owners, and other stakeholders involved in data management.
Develop Data Quality Standards: Create and maintain detailed standards for data accuracy, completeness, consistency, and timeliness.
Implement Data Governance Frameworks: Align data stewardship activities with broader data governance initiatives to ensure consistency and effectiveness.
Leverage Automation and Tools: Utilize data quality tools, metadata management systems, and data catalogs to streamline data stewardship processes.
Foster Cross-Functional Collaboration: Encourage communication and cooperation between different departments to address data-related challenges.
Provide Ongoing Training and Education: Offer regular training sessions to keep data stewards and other stakeholders updated on best practices and emerging trends.
Establish Key Performance Indicators (KPIs): Define and track metrics defined by executives to measure the success of data stewardship initiatives and identify areas for improvement.
Conduct Regular Audits and Assessments: Perform periodic reviews of data assets, processes, and policies to ensure continued alignment with business objectives and data compliance.
Implement a Data Catalog: Maintain a centralized metadata repository to help users utilize data assets to their fullest capabilities.

Benefits of Data Stewardship for Data Consumers

Effective data stewardship provides numerous benefits to data consumers within an organization. By ensuring high-quality, accessible, and reliable data, data stewards enable users to make informed decisions and drive business value.

Key benefits for data consumers include:

Improved data quality and reliability.
Enhanced data discoverability and accessibility.
Greater trust in data-driven insights and decisions.
Reduced time spent on data preparation and cleansing.
Increased compliance with data usage policies and regulations.
Streamlined data request and access processes.
Reduced risk of using outdated or inaccurate data.
Improved collaboration and data sharing between departments.

Benefits of Data Stewardship for Data Producers

Data stewardship not only benefits consumers but also provides significant advantages to data producers. By implementing best practices and standards, data stewards help producers optimize their data management processes and increase the value of their data assets.

Benefits for data producers include:

Streamlined data production processes.
Reduced data redundancy and duplication.
Improved data integration capabilities.
Increased efficiency in data management workflows.
Better alignment between data production and business needs.
Improved data lifecycle management.
Greater visibility into data usage and value.

Use Cases for Data Stewardship

Data stewardship plays a crucial role across many industries, helping organizations manage their data assets effectively and derive maximum value from their information. Here are some industry-specific use cases that highlight the importance of quality data stewardship programs:

Healthcare

In the healthcare industry, data stewardship is essential for managing patient information, ensuring regulatory compliance, and supporting medical research:

Electronic Health Records (EHR) Management: Ensuring the accuracy and completeness of patient records, facilitating better care coordination and reducing medical errors.
HIPAA Compliance: Helping maintain patient data privacy and security in accordance with HIPAA regulations.
Clinical Research: Supporting the integration and quality control of data used in medical research and clinical trials.

Financial Services

The financial sector relies heavily on accurate and timely data for risk management, regulatory reporting, and customer service:

Regulatory Reporting: Ensuring the accuracy and consistency of data used for regulatory reports.
Customer Data Management: Maintaining the quality and integrity of customer information across various systems, supporting Know Your Customer (KYC) initiatives and personalized services.
Fraud Detection: Ensuring the quality and integration of data used in fraud detection algorithms.

Telecommunications

In the rapidly evolving telecommunications industry, data stewardship is vital for managing customer data, network information, and service quality:

Customer Experience Management: Improving the accuracy and consistency of customer data, enabling personalized services and effective customer support.
Network Performance Monitoring: Managing the quality and reliability of data used to monitor network performance and plan infrastructure improvements.
Regulatory Compliance: Supporting compliance with telecommunications regulations by ensuring data accuracy in regulatory reporting.

Manufacturing

In manufacturing, data stewardship is crucial for optimizing production processes, managing supply chains, and ensuring product quality:

Supply Chain Optimization: Maintaining accurate and up-to-date information on suppliers, inventory, and logistics, enabling better supply chain management.
Quality Control: Stabilizing the integrity and consistency of data used in quality control processes, supporting continuous improvement initiatives.
Predictive Maintenance: Managing the data used for predictive maintenance models, helping to reduce downtime and maintenance costs.

Actian Supports Data Stewardship Best Practices

Actian provides a comprehensive suite of data management solutions that support and enhance data stewardship practices across organizations. The Actian Data Intelligence Platform offers a robust foundation for implementing effective data stewardship programs through its various features and capabilities. Its data integration tools, including DataConnect, help organizations maintain high-quality data by providing robust Extract, Transform, Load (ETL) capabilities and quality checks. And businesses can utilize the Actian Data Intelligence Platform to standardize data storage and usage according to data governance policies, which increases data democratization while making data stewardship simpler and easier to understand for stakeholders.

Actian’s solutions integrate seamlessly with data governance frameworks, helping organizations align their data stewardship practices with broader governance initiatives. As organizations grow and evolve, the Actian Data Platform is designed to scale with increasing data volumes and user communities, providing ongoing support for data stewardship efforts. More help creating large-scale data stewardship initiatives can be found in the blog “The Importance of Data Stewardship Agreements Across Companies.”

Key Takeaways

data stewardship

About Actian Corporation

Data Platform

What is a Data Marketplace and Why Should You Use One?

Actian Corporation

December 20, 2024

Collecting data takes time, but the more information you have at your disposal, the easier it is to plan your business’s growth strategies and identify areas of improvement. Though many businesses rely on data they collect in-house, sourcing your own information can often result in more limited data sets that make it difficult to put a full picture together. That’s where data marketplaces can help.

Let’s take a closer look at how these marketplaces work and why businesses should consider leveraging marketplaces when collecting data.

What is a Data Marketplace?

A data marketplace is essentially an online store for data where businesses and individuals alike can buy and sell information as well as exchange data to facilitate growth. These marketplaces allow those who collect data to turn a profit from the information they gather and make it easy for businesses to access data that would otherwise take weeks or months to collect on their own.

Though both marketplaces and data exchanges allow people to trade data sets, they do so in different ways. In marketplaces, individuals and businesses have the opportunity to buy, sell or trade information. The information can be purchased or traded by anyone.

On data exchanges, the system works more as an even trade between approved organizations and individuals. The data provider can determine who is eligible to receive and share data with or restrict it from certain parties as needed.

Both systems can help people access critical data to help them drive decisions, but marketplaces can be more flexible.

What Types of Information Can You Get on a Data Marketplace?

The information available on each marketplace can vary. However, there are a few common types of data that people typically offer for sale or exchange on most marketplaces. This includes but is not limited to the following:

Demographic information.
Consumer behavior.
Social media information.
Scientific studies.
Public records.
Market conditions and trends in industries.
Sales data for specific markets.

If you’re looking for something specific, you may need to search several data marketplaces to find the information you’re looking for at a price point that works for your budget. Marketplaces like Kaggle and World Bank Data may help you round out your data sets and give you access to the information you need.

The Benefits of Using a Data Marketplace

Data marketplaces give you access to more data than you can reasonably compile on your own. But that’s far from the only benefit they can provide. Here’s how using a data marketplace can streamline your business:

Better Collaboration: Data marketplaces foster an open-source culture of publishing and sharing data with the knowledge that more data means more productive and healthier societies.
Opportunity to Monetize Data: You’re able to both purchase and sell data on a marketplace. This means you can make money from the data you already collect through the course of your business, thereby increasing your cashflow and diversifying your income sources.
Improved Efficiency: Purchasing data on marketplaces can take just a few minutes compared to the months it may take to collect the data on your own. This improves efficiency and helps your team make informed decisions faster.
Enhanced Productivity: Getting the right data from a marketplace and cleansing it against your data quality standards gives you all the information you need to begin analysis. Make sure the dataset meets your data governance protocol to ensure the dataset remains current. This means your team will spend less time formatting and interpreting the data to fit their needs and more time producing quality work.
Easy to Integrate: Data marketplaces offer the assets you’re looking for in different formats that can seamlessly integrate with your existing data fabric. Key stakeholders will be able to access the information in a way that works for their needs.

Keep in mind that these are just a few ways that data marketplaces can improve your business and your data acquisition efforts.

How the Enterprise Data Marketplace Can Help Your Business

Though there are many data marketplaces that you can use to gain access to essential information, Actian Data Intelligence Platform’s new data marketplace can take the stress out of choosing a quality service that you can trust.

The data intelligence platform allows you to access data in a familiar format. The interface is designed similarly to an e-commerce site, so you can pick and choose data based on your needs and queries. Even better, the system can integrate with Actian’s DataConnect service to make interpreting, organizing, and managing that newly purchased data a breeze.

Transform Your Information into a Strategic Data Asset

With data marketplaces, you’re able to access new data, transforming the information you rely on into a strategic asset that can benefit your business in multiple ways. Explore the different assets available to you and your team on the Enterprise Data Marketplace today.

About Actian Corporation

Data Governance

What is Data Discovery and Why Does it Matter?

Actian Corporation

December 19, 2024

No matter what industry your business is in, you rely on complex and growing data volumes to keep your organization running efficiently and sustainably. There are many ways to collect and organize that data, but to put all that information to use, you need to have trusted systems in place to interpret and optimize that information. The data discovery process aims to simplify your efforts. Here’s what you need to know about the process and why it’s so essential for businesses.

What Exactly is Data Discovery?

Data discovery is the process of collecting data that businesses like yours rely on daily, along with using various tools and programs to analyze the collected information. During analysis, you’ll be able to spot trends, identify patterns, and find ways to improve your operations. This form of data analysis relies on visual presentations of information. As a result, it is easy for anyone, not just experienced data scientists and data analysts, to interpret and understand the insights the data is revealing.

Many data discovery tools use Artificial Intelligence (AI) to sort through and interpret complex data sets. This effectively makes it easier for data users at all levels of the business to access information clearly and concisely.

Data Discovery vs. Traditional Data Analysis

Traditional data analysis relies on predefined queries to sort and interpret data. This means users need to know what they’re looking for and how to input that specific query into the system to gain access to relevant information.

Data discovery, on the other hand, offers a more flexible approach. Users can interact with the data and sort the information in a way that meets their needs each time. The query can be adjusted on the fly and doesn’t have to conform to pre-existing rules or benchmarks.

Why Data Discovery is Important

Data discovery can simplify your data management and analysis efforts, making data more accessible and user-friendly at all levels. Here are a few benefits of incorporating this type of data management into your organization:

Improved Compliance: Data discovery lets you better interpret and understand your data. This makes it easier to spot compliance violations quickly so you can course correct before damage is done.
Better Efficiency: This method of data analytics makes it easier to interpret data and get the information you need quickly. This boosts efficiency and helps key stakeholders make important decisions in less time.
Real-Time Monitoring of Information: AI systems commonly used for data discovery methods are capable of monitoring information 24/7. This makes it easier to identify risks and make adjustments to keep those risks from turning into major issues.
Improved Workflows: Because data discovery systems rely on programs and data analytics software to interpret data, they automate much of your data team’s workflow. This automation frees up teams for more important tasks while also reducing the risk of human error.
Deeper Insights into Customer Behavior: Understanding your customers’ needs and behaviors can help you make better business decisions. Data discovery methods help you evaluate those behaviors and identify key areas of improvement to drive sales.

Implementing a data discovery system can help your business make sense of complex data and streamline your operation so you can grow sustainably and strategically.

How the Data Discovery Process Works

Though the discovery data management method can be customized to fit your business’s unique needs, the process you’ll want to use will largely be the same across different industries. These are the four steps you’ll need to take:

1. Set Clear Goals

As with any form of data management, you’ll need to identify clear goals for your discovery process. This will help ensure that the data you collect and analyze meets your business’s needs. Some goals may include monitoring and identifying customer behavior, tracking performance of certain products or services, and other similar targets.

Figure out how those business goals can be achieved by leveraging the data you collect and determine what types of information you want the database to contain. Remember that your goals can change over time, so you may need to re-evaluate throughout the year.

2. Gather Your Data

Data discovery software lets you collect raw data based on your goals and analyze that information to help you figure out where you need to improve and what you’re doing right. Collect data for each key goal you’re trying to reach. Then, let your system analyze the information for you. Comprehensive data discovery tools will help you take that raw data and turn it into visual representations that everyone on your team can understand.

3. Analyze the Visualizations

The data you collect still needs a human layer of review. This is your opportunity to look at the information and see what it tells you. Take your time and go through each dataset closely. If you find that certain information is lacking, go back through the discovery process and fill in the gaps. Your tools will help you do this based on the parameters you specify.

4. Repeat the Process

Data discovery is an ongoing process that can grow and change with your business. It’s not something you do only once. Use the information you’ve gathered to make the necessary changes and repeat the discovery process to see how behaviors have changed. This information can help you make better-informed business decisions that support your long-term goals.

What to Look for in Your Data Discovery Tools

Sorting through tons of data requires comprehensive data discovery software and tools. Every tool works in slightly different ways, so make sure you’re choosing the best programs for your needs from the beginning. Look for these important characteristics as you explore your options and identify ways to power your data strategy:

Easy to Use Solutions: The tools you select should be easy to use and designed with the average data team skill set in mind. You shouldn’t have to hire an experienced data scientist to show you how to use the system or interpret the information.
Multiple Visualization Methods and Options: Different types of data can benefit from different forms of visualization. Review the methods and options each tool offers and choose the system that can present data in a way that your team can understand and use.
AI-Powered Analytics to Make Interpretations Easier and Faster: You have better things to do than spending hours analyzing and interpreting complex datasets. Look for tools that feature AI-powered analytics systems to help take the strain off of your team while still giving you actionable insights for your brand.
Mobile-Friendly Interfaces to Improve Accessibility: There may be times when you need to access information away from your headquarters and your computers. Make sure the tools you choose are designed with mobile devices like smartphones and tablets in mind.

The right tools can make all the difference in your data management and data governance efforts.

Gain the Insights Your Business Needs

Data discovery should help your business improve your processes and make accessing and interpreting the information you collect easier and faster. With the Actian Data Intelligence Platform, you can take control of your data and put it to work for your business. Learn more about our platform and see how it can simplify your business.

About Actian Corporation

Data Management

Why Next-Gen Data Catalogs are a Must-Have

Traci Curran

December 17, 2024

data-catalogs-for-modern-data-management

Next-Gen Data Catalogs

Data management is undergoing a significant transformation with the emergence of next-generation data catalogs. As organizations face an unprecedented surge in data volumes across their operations, sophisticated yet user-friendly tools are becoming increasingly necessary to manage this information overload effectively. Next-generation data catalogs have emerged as a compelling solution, offering an impressive array of advanced features and intuitive functionality that caters to diverse user needs.

Data Catalogs Make Big Moves

The evolution of data catalogs in recent years has been remarkable. While proficient at metadata organization and management, traditional catalog systems were primarily designed with IT professionals in mind, resulting in interfaces and workflows that proved challenging for users with limited technical expertise. As businesses across industries began generating and exchanging increasingly larger volumes of data, a pressing need arose for non-technical users to harness data insights for various business objectives and strategic decision-making processes.

Modern data catalogs have undergone a fundamental shift in their approach, prioritizing user experience and accessibility. These next-generation solutions emphasize intuitive data discovery, comprehensive understanding, and seamless access to critical information regardless of technical proficiency. This transformation aligns perfectly with the contemporary perspective that data represents a valuable organizational asset that should be readily available to all stakeholders who require it for their roles and responsibilities.

Reasons for the Shift

The evolution of data catalogs can be attributed to several compelling factors:

More Data Getting Made: The exponential growth in data generation across various sources and platforms necessitates more sophisticated and efficient data discovery, management, and utilization methods.
People Want to Crunch Numbers Themselves: Business professionals increasingly seek data autonomy, driving the demand for self-service analytics capabilities through user-friendly catalog interfaces.
Moving to the Cloud: The distributed nature of modern data environments, spanning cloud services, on-premises data centers, and third-party applications, creates an urgent need for enhanced visibility and unified data management.
Playing by the Rules: Stringent regulatory requirements and compliance standards mandate a comprehensive understanding of data lineage, governance, and metadata management.
Smart Tech Gets Smarter: Advanced artificial intelligence and machine learning capabilities continue to evolve, offering increasingly sophisticated automated solutions for data classification, tagging, and discovery processes.

The Eckerson Group recently published a Top Tier Report on Next-Generation Data Catalogs that provides detailed insights into the future trajectory of this technology space. The report emphasizes that next-generation data catalogs need to focus on ease of use, speed of deployment, and affordability to meet evolving business requirements effectively.

In their comprehensive analysis, the report specifically highlighted Actian Data Intelligence Platform, noting that it stands out as unique among data catalog vendors. The platform supports an enterprise data marketplace in which multiple business domains can share data products in a seamless fashion. When business users request access to a data product, the platform routes their requests through a third-party workflow system, such as Jira or ServiceNow. Actian Data Intelligence Platform also integrates with numerous third-party applications, such as Monte Carlo and Soda, to import metadata into the catalog. This innovative approach demonstrates the platform’s commitment to delivering a comprehensive and integrated data management solution.

Ways to Use and Put Next-Gen Data Catalogs into Action

Next-generation data catalogs are revolutionizing how organizations manage and utilize their data assets. By prioritizing ease of use, data accessibility, and improved decision-making, these innovative tools empower businesses to navigate the complexities of modern data landscapes with unprecedented efficiency. The emergence of enterprise data marketplaces, as exemplified by the Actian Data Intelligence Platform, marks a significant leap forward in collaborative data sharing and governance. These marketplaces facilitate seamless data exchange between different business domains while maintaining strict security protocols and access controls.

As data continues to grow exponentially in volume and importance, the role of next-gen catalogs in driving business success cannot be overstated. Their ability to democratize data access while maintaining robust governance frameworks positions them as indispensable assets for forward-thinking organizations. These catalogs enable organizations to implement comprehensive data governance strategies, ensuring compliance with regulatory requirements while fostering a culture of data-driven decision-making. Through automated metadata management and advanced search capabilities, they significantly reduce the time and effort required to locate and utilize relevant data assets.

Curious about harnessing the power of next-gen data catalogs for your business? Explore Actian and discover how it can transform your data management strategy. In this era of data-driven decision-making, the question isn’t whether you can afford to invest in advanced data catalog solutions—it’s whether you can afford not to. With features like automated data discovery, intelligent metadata management, and seamless integration capabilities, Actian provides a comprehensive solution that addresses the evolving needs of modern enterprises while ensuring scalability and future-readiness.

About Traci Curran

Traci Curran is Director of Product Marketing at Actian, focusing on the Actian Data Platform. With 20+ years in tech marketing, Traci has led launches at startups and established enterprises like CloudBolt Software. She specializes in communicating how digital transformation and cloud technologies drive competitive advantage. Traci's articles on the Actian blog demonstrate how to leverage the Data Platform for agile innovation. Explore her posts to accelerate your data initiatives.

Databases

User-Friendly External Smartblobs Using a Shadow Directory

Mary Schulte

December 16, 2024

I am very excited about the HCL Informix^® 15 external smartblob feature.

If you are not familiar with them, external smartblobs allow the user to store actual Binary Large Object (blob) and Character Large Object (clob) data external to the database. Metadata about that external storage is maintained by the database.

Notes: This article does NOT discuss details of the smartblobs feature itself, but rather proposes a solution to make the functionality more user-friendly. For details on feature behavior, setup, and new functions, see the documentation.

At the writing of this blog, v15.0 does not have the ifx_lo_path function defined, as required below. This has been reported to engineering. The workaround is to create it yourself with the following command:

create dba function ifx_lo_path(blob)
  returns lvarchar
  external name '(sq_lo_path)'
  language C;

This article also does not discuss details of client programming required to INSERT blobs and clobs into the database.

The external smartblob feature was built for two main reasons:

1. Backup size

Storing blobs in the database itself can cause the database to become extremely large. As such, performing backups on the database takes an inordinate amount of time, and 0 level backups can be impossible. Offloading the actual blob contents to an external file system can lessen the HCL Informix backup burden by putting the blob data somewhere else. The database still governs the storage of, and access to, the blob, but the physical blob is housed elsewhere/externally.

2. Easy access to blobs

Users would like easy access to blob data, with familiar tools, without having to go through the database.

Using External Smartblobs in HCL Informix 15

HCL Informix 15 introduces external smartblobs. When you define an external smartblob space, you specify the external directory location (outside the database) where you would like the actual blob data to be stored. Then you assign blob column(s) to that external smartblob space when you CREATE TABLE. When a row is INSERTed, HCL Informix stores the blob data in the defined directory using an internal identifier for the filename.

Here’s an example of a customer forms table: custforms (denormalized and hardcoded for simplicity). My external sbspace directory is /home/informix/blog/resources/esbsp_dir1.

CREATE TABLE custforms(formid SERIAL, company CHAR(20), year INT, lname CHAR(20), 
formname CHAR(50), form CLOB) PUT form IN (esbsp);

Here, I INSERT a 2023 TaxForm123 document from a Java program for a woman named Sanchez, who works for Actian:

try(PreparedStatement p = c.prepareStatement("INSERT INTO custforms 
(company, year, lname, formname, form) values(?,?,?,?,?)");

FileInputStream is = new FileInputStream("file.xml")) {
p.setString(1, "Actian");
p.setString(2, "2023");
p.setString(3, "Sanchez");
p.setString(4, "TaxForm123");
p.setBinaryStream(5, is);
p.executeUpdate();
}

After I INSERT this row, my external directory and file would look like this:

[informix@schma01-rhvm03 resources]$ pwd
/home/informix/blog/resources
[informix@schma01-rhvm03 resources]$ ls -l esbsp*
-rw-rw---- 1 informix informix 10240000 Oct 17 13:22 esbsp_chunk1

esbsp_dir1:
total 0
drwxrwx--- 2 informix informix 41 Oct 17 13:19 IFMXSB0
[informix@schma01-rhvm03 resources]$ ls esbsp_dir1/IFMXSB0
LO[2,2,1(0x102),1729188125]

Where LO[2,2,1(0x102),1729188125]is an actual file that contains the data that I could access directly. The problem is that if I want to directly access this file for Ms. Sanchez, I would first have to figure out that this file belongs to her and is the tax document I want. It’s very cryptic!

A User-Friendly Smartblob Solution

When talking to Informix customers, they love the new external smartblobs feature but wish it could be a little more user-friendly.

As in the above example, instead of putting Sanchez’s 2023 TaxForm123 into a general directory called IFMXSB0 in a file called LO[2,2,1(0x102),1729188125, which together are meaningless to an end-user, wouldn’t it be nice if the file was located in an intuitive place like /home/forms/Actian/2024/TaxForm123/Sanchez.xml or something similar…something meaningful…how YOU want it organized?

Having HCL Informix automatically do this is a little easier said than done, primarily because the database would not intuitively know how any one customer would want to organize their blobs. What exact directory substructure? From what column or columns do I form the file names? What order? All use cases would be different.

Leveraging a User-Friendly Shadow Directory

The following solution shows how you can create your own user-friendly logical locations for your external smartblobs by automatically maintaining a lightweight shadow directory structure to correspond to actual storage locations. The solution uses a very simple system of triggers and stored procedures to do this.

Note: Examples here are shown on Linux, but other UNIX flavors should work also.

How to Set Up in 4 Steps

For each smartblob column in question

STEP 1: Decide how you want to organize access to your files.

Decide what you want the base of your shadow directory to be and create it. In my case for this blog, it is: /home/informix/blog/resources/user-friendly. You could probably implement this solution without a set base directory (as seen in the examples), but that may not be a good idea because users would unknowingly start creating directories everywhere.

STEP 2: Create a create_link stored procedure and corresponding trigger for INSERTs.

This procedure makes sure that the desired data-driven subdirectory structure exists from the base (mkdir -p), then forms a user-friendly logical link to the Informix smartblob file. You must pass all the columns to this procedure from which you want to form the directory structure and filename from the trigger.

CREATE PROCEDURE

CREATE PROCEDURE create_link (p_formid INT, p_company CHAR(20), p_year INT,
p_lname CHAR(20), p_formname CHAR(50))

DEFINE v_oscommand CHAR(500);
DEFINE v_custlinkname CHAR(500);
DEFINE v_ifmxname CHAR(500);
DEFINE v_basedir CHAR(100);

-- set the base directory
LET v_basedir = '/home/informix/blog/resources/user-friendly';

-- make sure directory tree exists
LET v_oscommand = 'mkdir -p ' || TRIM(v_basedir) || '/' || TRIM(p_company) || '/' || 
TO_CHAR(p_year);
SYSTEM v_oscommand; 

-- form full link name 
LET v_custlinkname = TRIM(v_basedir) || '/' || TRIM(p_company) || '/' || TO_CHAR(p_year) 
|| '/' || TRIM(p_lname) || '.' || TRIM(p_formname) || '.' || TO_CHAR(p_formid);

-- get the actual location 
SELECT IFX_LO_PATH(form::LVARCHAR) INTO v_ifmxname FROM custforms WHERE formid = p_formid; 

-- create the os link 
LET v_oscommand = 'ln -s -f ' || '''' || TRIM(v_ifmxname) || '''' || ' ' || v_custlinkname; 
SYSTEM v_oscommand;

END PROCEDURE

CREATE TRIGGER

CREATE TRIGGER ins_tr INSERT ON custforms REFERENCING new AS post
FOR EACH ROW(EXECUTE PROCEDURE create_link (post.formid, post.company,
post.year, post.lname, post.formname));

STEP 3: Create a delete_link stored procedure and corresponding trigger for DELETEs.

This procedure will delete the shadow directory link if the row is deleted.

CREATE PROCEDURE

CREATE PROCEDURE delete_link (p_formid INT, p_company CHAR(20), p_year INT,
p_lname CHAR(20), p_formname CHAR(50))

DEFINE v_oscommand CHAR(500);
DEFINE v_custlinkname CHAR(500); 
DEFINE v_basedir CHAR(100);

-- set the base directory
LET v_basedir = '/home/informix/blog/resources/user-friendly';

-- form full link name
LET v_custlinkname = TRIM(v_basedir) || '/' ||
TRIM(p_company) || '/' || TO_CHAR(p_year) || '/' || TRIM(p_lname) || '.'
|| TRIM(p_formname) || '.' || TO_CHAR(p_formid);

-- remove the link
LET v_oscommand = 'rm -f -d ' || v_custlinkname;
SYSTEM v_oscommand;

END PROCEDURE

CREATE TRIGGER

CREATE TRIGGER del_tr DELETE ON custforms REFERENCING old AS pre FOR EACH ROW
(EXECUTE PROCEDURE delete_link (pre.formid, pre.company, pre.year, pre.lname, pre.formname));

STEP 4: Create a change_link stored procedure and corresponding trigger for UPDATEs, if desired. In my example, Ms. Sanchez might get married to Mr. Simon and an UPDATE to her last name in the database occurs. I may then want to change all my user-friendly names from Sanchez to Simon. This procedure deletes the old link and creates a new one.

Notice the update trigger only must fire on the columns that form your directory structure and filenames.

CREATE PROCEDURE

CREATE PROCEDURE change_link (p_formid INT, p_pre_company CHAR(20), 
p_pre_year INT, p_pre_lname CHAR(20), p_pre_formname CHAR(50), p_post_company CHAR(20), 
p_post_year INT, p_post_lname CHAR(20), p_post_formname CHAR(50))

DEFINE v_oscommand CHAR(500);
DEFINE v_custlinkname CHAR(500);
DEFINE v_ifmxname CHAR(500);
DEFINE v_basedir CHAR(100);
-- set the base directory
LET v_basedir = '/home/informix/blog/resources/user-friendly';

-- get rid of old

-- form old full link name
LET v_custlinkname = TRIM(v_basedir) || '/' || TRIM(p_pre_company) || '/' || 
TO_CHAR(p_pre_year) || '/' || TRIM(p_pre_lname) || '.' || TRIM(p_pre_formname) || '.' 
|| TO_CHAR(p_formid) ;

-- remove the link and empty directories
LET v_oscommand = 'rm -f -d ' || v_custlinkname;
SYSTEM v_oscommand;

-- form the new
-- make sure directory tree exists
LET v_oscommand = 'mkdir -p ' || TRIM(v_basedir) || '/' || TRIM(p_post_company) || '/' || 
TO_CHAR(p_post_year);
SYSTEM v_oscommand;

-- form full link name
LET v_custlinkname = TRIM(v_basedir) || '/' || TRIM(p_post_company) || '/' || 
TO_CHAR(p_post_year) || '/' || TRIM(p_post_lname) || '.' || TRIM(p_post_formname) 
|| '.' || TO_CHAR(p_formid) ;

-- get the actual location
-- this is the same as before as id has not changed
SELECT IFX_LO_PATH(form::LVARCHAR) INTO v_ifmxname FROM custforms WHERE formid = p_formid;

-- create the os link
LET v_oscommand = 'ln -s -f ' || '''' || TRIM(v_ifmxname) || '''' || ' ' || v_custlinkname;
SYSTEM v_oscommand;

END PROCEDURE

CREATE TRIGGER

CREATE TRIGGER upd_tr UPDATE OF formid, company, year, lname, formname ON custforms
REFERENCING OLD AS pre NEW as post

FOR EACH ROW(EXECUTE PROCEDURE change_link (pre.formid, pre.company, pre.year, pre.lname, 
pre.formname, post.company, post.year, post.lname, post.formname));

Results Example

Back to our example.

With this infrastructure in place, now in addition to the Informix-named file being in place, I would have these user-friendly links on my file system that I can easily locate and identify.

INSERT

[informix@schma01-rhvm03 2023]$ pwd

/home/informix/blog/resources/user-friendly/Actian/2023
[informix@schma01-rhvm03 2023]

$ ls Sanchez.TaxForm123.2

If I do an ls -l, you will see that it is a link to the Informix blob file.

[informix@schma01-rhvm03 2023]$ ls -l
total 0
lrwxrwxrwx 1 informix informix 76 Oct 17 14:20 Sanchez.TaxForm123.2 -> 
/home/informix/blog/resources/esbsp_dir1/IFMXSB0/LO[2,2,1(0x102),1729188126]

UPDATE

If I then update her last name with UPDATE custforms SET lname = ‘Simon’ where formid=2,my file system now looks like this:

[informix@schma01-rhvm03 2023]$ ls -l
lrwxrwxrwx 1 informix informix 76 Oct 17 14:25 Simon.TaxForm123.2 -> 
/home/informix/blog/resources/esbsp_dir1/IFMXSB0/LO[2,2,1(0x102),1729188126]

DELETE

If I then go and DELETE this form with DELETE FROM custforms where formid=2, my directory structure looks like this:

[informix@schma01-rhvm03 2023]$ pwd
/home/informix/blog/resources/user-friendly/Actian/2023
[informix@schma01-rhvm03 2023]$ ls
[informix@schma01-rhvm03 2023]$

We Welcome Your Feedback

Please enjoy the new HCL Informix15 external smartblob feature.

I hope this idea can make external smartblobs easier for you to use. If you have any feedback on the idea, especially on enhancements or experience in production, please feel free to contact me at mary.schulte@hcl-software.com. I look forward to hearing from you!

Find out more about the launch of HCL Informix 15.

Notes

1. Shadow directory permissions. In creating this example, I did not explore directory and file permissions, but rather just used general permissions settings on my sandbox server. Likely, you will want to control permissions to avoid some of the anomalies I discuss below.

2. Manual blob file delete. With external smartblobs, if permissions are not controlled, it is possible that a user might somehow delete the physical smartblob file itself from its directory. HCL Informix, itself, cannot control this from happening. In the event it does happen, HCL Informix does NOT delete the corresponding row; the blob file will just be missing. There may be aspects to links that can automatically handle this, but I have not investigated them for this blog.

3. Link deletion in the shadow directory. If permissions are not controlled, it is possible that a user might delete a logical link formed by this infrastructure. This solution does not detect this. If this is an issue, I would suggest a periodic maintenance job that cross references the shadow directory links to blob files to detect missing links. For those blobs with missing links, write a database program to look up the row’s location with the IFX_LO_PATH function, and reform the missing link.

4. Unique identifiers. I highly recommend using unique identifiers in this solution. In this simple example, I used formid. You don’t want to clutter things up, of course, but depending on how you structure your shadow directories and filenames, you may need to include more unique identifiers to avoid directory and link names duplication.

5. Empty directories. I did not investigate if there are options to rm in the delete stored procedure to clean up empty directories that might remain if a last item is deleted.

6. Production overhead. It is known that excessive triggers and stored procedures can add overhead to a production environment. For this blog, it is assumed that OLTP activity on blobs is not excessive, therefore production overhead should not be an issue. This being said, this solution has NOT been tested at scale.

7. NULL values. Make sure to consider the presence and impact of NULL values in columns used in this solution. For simplicity, I did not handle them here.

Informix is a trademark of IBM Corporation in at least one jurisdiction and is used under license.

About Mary Schulte

Mary Schulte is Senior Sales Engineer at Actian, drawing upon decades of experience with powerhouse database vendors like Informix and Netezza. She has written thousands of lines of Informix 4GL and ESQL/C for global clients, including American Airlines' groundbreaking datablade implementation. Mary has delivered countless training sessions, helping organizations optimize their database environments. Her posts on the Actian blog center on query performance, analytics databases like Vector, and practical tips for leveraging Informix. Browse her articles for expert guidance.

Data Democratization Strategy and its Role in Business Decisions

Understanding Data Democratization

What is Data Democratization?

Benefits and Importance

The Role of a Data Democratization Strategy

Accelerating Decision-Making Processes

Fostering Innovation Culture

Enhancing Operational Efficiency

Essential Components of a Successful Data Democratization Strategy

User-Centric Platforms and Tools

Solid Data Governance

Sustained Training and Education

Defined Data Access Protocols

Realizing a Data Democratization Strategy: A Step-By-Step Blueprint

1. Assess the Current Data Landscape

2. Formulate Data Governance Policies

3. Incorporate User-Friendly Tools

4. Optimize for Real-Time Data Integration

5. Enforce Data Security Measures

6. Encourage Feedback Mechanisms

7. Implement a Culture of Openness

8. Build Scalable Infrastructure

Data Democratization FAQs

What is Data Democratization?

How Does Data Democratization Impact Security?

Who Benefits From Data Democratization?

The Future: Data Democratization and AI

About Actian Corporation

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Data Catalog vs. Data Dictionary: How Do They Help Businesses?

What You Need to Know: Data Catalog vs. Data Dictionary

Defining the Data Catalog

Key Data Catalog Features:

Understanding the Data Dictionary

Key Features of a Data Dictionary:

The Crucial Role of Data Catalogs and Dictionaries in Modern Business

Importance of Data Catalogs in Modern Data Management

How Data Dictionaries Support Data Governance and Compliance

Harnessing the Power of Data: Business Applications of Data Catalogs and Dictionaries

How Businesses Can Leverage Data Catalogs Effectively

Practical Use-Cases of Data Dictionaries in Business

How Data Catalogs and Dictionaries Complement Each Other

Data Catalog vs. Data Dictionary: Making the Right Choice for Your Business

Factors to Consider When Choosing Between a Data Catalog and Data Dictionary

Evaluating Business Requirements for Data Management

Use Data Catalogs and Data Dictionaries to Manage Your Company’s Data

About Actian Corporation

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Decentralize, Democratize, Differentiate: Embrace Data Products

How to Choose the Right Solution

The Business Case for Decentralizing Data Ownership

Gaining Trusted Insights into Data Products

About Guillaume Bodet

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Securing Your Data With Actian Vector, Part 2

Function-Based Encryption in Actian Vector

Examples of Function-Based Encryption:

Deﬁnition of the table used in the following examples:

Insert data with encryption into a table:

Selecting encrypted data:

Updating encrypted data:

About Martin Fuerderer

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Why Analyst Reports Matter: Actian Leads the Way in Data Management