Data Intelligence

Data Mesh 101: Best Practices for Metadata Management

Actian Corporation

January 14, 2024

dot graphic depicting data mesh and metadata management

In the ever-evolving landscape of data management, organizations are shifting towards new innovative approaches to tackle the complexities of their data landscapes. One such notable trend gaining substantial momentum is the concept of Data Mesh – a decentralized approach to data architecture, emphasizing autonomous, domain-oriented data products.

As we embark on this journey of decentralized data, let’s dig into the vital role of metadata and the importance of effectively managing it in the context of Data Mesh.

The Role of Metadata

Metadata, often referred to as ‘data about data,’ plays a fundamental role in shaping a functional data ecosystem. It extends beyond the simple task of describing datasets; rather, it involves understanding the data’s origins, quality, transformations, etc. The different types of metadata include:

Technical Metadata: Focuses on the technical aspects of data, such as data formats, schema, data lineage, and storage details.
Business Metadata: Business metadata revolves around the business context of data. It includes information about data ownership, business rules, data definitions, and any other details that help align data assets with business objectives.
Operational Metadata: Operational metadata provides insights into the day-to-day operations related to data. This includes information about data processing workflows, data refresh schedules, and any operational dependencies.
Collaborative Metadata: Collaborative metadata captures information about user interactions, annotations, and comments related to data assets.

In the decentralized framework of Data Mesh, metadata serves as the link, bridging different data domains with these different types of metadata. As data moves among different teams, metadata becomes the guide, assisting everyone in navigating the diverse data landscape. Metadata, therefore, acts as a valuable aid by providing insights into the structure and content of their assets. It facilitates data discovery for users, making it easier to discern and locate specific data that aligns with their needs.

Additionally, metadata forms the basis for data governance, providing a framework for enforcing quality standards, security protocols, and compliance measures uniformly across diverse domains. It plays a critical role in access control and ensures that users are not only informed but also adhere to the defined access policies.

Challenges of Managing Metadata in Data Mesh

One significant challenge stems from the decentralized nature of a Data Mesh. In a traditional centralized data architecture, metadata management is often handled by a dedicated team or department, ensuring consistency and standardization. However, in a Data Mesh, each domain team is responsible for managing its own metadata. This decentralized approach can lead to variations in metadata practices across different domains, making it challenging to maintain uniform standards and enforce data governance policies consistently.

The diversity of data sources and domains within a Data Mesh is another notable challenge in metadata management. Different domains may use various tools, schemas, and structures for organizing and describing their data. Managing metadata across these diverse sources requires establishing common metadata standards and ensuring compatibility, which can be a complex and time-consuming task. The heterogeneity of data sources adds a layer of intricacy to the creation of a cohesive and standardized metadata framework.

Ensuring consistency and quality across metadata is an ongoing challenge in a Data Mesh environment. With multiple domain teams independently managing their metadata, maintaining uniformity becomes a constant effort – Inconsistencies in metadata can lead to misunderstandings, misinterpretations, and errors in data analysis.

Best Practices for Managing Data in Data Mesh

To overcome these challenges, here are some best practices for managing metadata for your organization.

First, establishing clear and standardized metadata definitions across diverse domains is essential for ensuring consistency, interoperability, and a shared understanding of data elements. Clear definitions provide a common language and framework that ensures consistency in how data is described and understood across the organization.

Furthermore, standardized metadata definitions play a pivotal role in data governance. They provide a basis for uniformly enforcing data quality standards, security protocols, and compliance measures across diverse domains. This ensures that data is not only described consistently but also adheres to organizational policies and regulatory requirements, contributing to a robust and compliant data ecosystem.

However, it’s equally important to empower domain teams with ownership of their metadata. This decentralized approach fosters a sense of responsibility and expertise among those who know the data best. By giving domain teams control over their metadata, organizations leverage their specific knowledge to ensure accuracy, consistency, and trustworthiness across all data domains. This approach promotes adaptability within individual domains, contributing to a more reliable and informed data management strategy.

This dual strategy allows for both centralized governance, ensuring organization-wide standards, and decentralized ownership, promoting agility and domain-specific knowledge within the landscape of a Data Mesh.

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

Is Your Data Management Strategy Ready for Manufacturing?

Traci Curran

January 12, 2024

In the rapidly evolving landscape of the manufacturing industry, data has become a cornerstone of innovation. From robotics and connected factories to operational efficiency, the potential for leveraging data is boundless. However, to harness the full power of data, manufacturers must ensure that their data management strategies are not only current but also future-ready. For this reason, organizations must consider critical needs when creating a robust data management strategy. They must ensure that this strategy aligns with manufacturing priorities and carefully consider the value of digital transformation.

Defining the Manufacturing Data Management Strategy

A data management strategy is the backbone of successful data utilization in manufacturing. It encompasses the integration, standardization, and secure data storage, ensuring it is governed and trusted. In the context of the future of manufacturing, this strategy must align seamlessly with industry priorities, such as enhancing efficiency, maintaining quality control, predicting delays, and fostering innovation while simultaneously reducing costs.

The Role of Data Strategy in Digital Transformation for Manufacturing

A forward-thinking data management strategy is indispensable for any manufacturer looking to embark on a digital transformation journey. As the manufacturing landscape becomes increasingly digital and automated, selecting the right platform is crucial. A well-crafted data strategy, as often stated, is at the center of every successful digital transformation. This ensures not just immediate gains but also future-proofs the business against evolving technological landscapes.

Technology is a catalyst for digital transformation in manufacturing, enhancing efficiency, agility, and innovation. Integrating advanced technologies empowers manufacturers to optimize processes, improve product quality, and respond more effectively to market demands. By leveraging technology, manufacturers can not only optimize operations but also get ahead of any disruptions to suppliers or supply chains.

Key Metrics for Measuring Manufacturing Digital Transformation

Measuring the success of digital transformation in manufacturing requires defined metrics that should be an integral part of any data management strategy. These metrics serve as benchmarks, allowing manufacturers to gauge the impact of their digital initiatives. According to Gartner, “36% of manufacturing enterprises realize above-average business value from IT spending in digitalization at a reasonable cost compared with peers.”

Other metrics to consider include:

Customer Engagement

Track metrics such as website traffic, social media interactions, and customer feedback to assess the level of engagement with digital platforms.

Customer Satisfaction (CSAT) Scores

Use surveys and feedback mechanisms to measure customer satisfaction with digital services, products, and overall experiences.

Operational Efficiency

Assess improvements in operational efficiency through metrics like reduced process cycle times, decreased manual intervention, and streamlined workflows.

Employee Productivity

Monitor changes in employee productivity resulting from digital tools and automation. This can include metrics like tasks completed per hour or efficiency gains in specific processes.

Cost Reduction

Measure the cost savings achieved through digital optimization, such as reduced manual processes, lower maintenance costs, and improved resource utilization.

Data Quality and Accuracy

Evaluate the quality and accuracy of data, ensuring that digital transformation initiatives contribute to improved data integrity.

Customer Lifetime Value (CLV)

Evaluate the long-term value generated from each customer, factoring in repeat business, upsells, and customer loyalty influenced by digital initiatives.

Net Promoter Score (NPS)

Measure the likelihood of customers recommending your products or services as an indicator of overall satisfaction and loyalty.

Contextualized Data in the Fourth Industrial Revolution

Industry 4.0 represents a paradigm shift in manufacturing, characterized by integrating advanced technologies, digitalization, and data-driven decision-making. Entering the era of Industry 4.0 necessitates manufacturers to have clear, concise, and contextualized data.

Real-time decision-making is a cornerstone of Industry 4.0, and clear data ensures that manufacturers can swiftly respond to dynamic conditions, optimize processes, and troubleshoot issues in real-time. Predictive maintenance, a key aspect of this industrial revolution, relies on contextualized data to anticipate equipment needs and minimize downtime. By harnessing clear and contextualized data, manufacturers can optimize production processes, implement robust quality control measures, and achieve end-to-end visibility in the supply chain. This level of data clarity facilitates customization and personalization in production, enhances energy efficiency, and supports the integration of connected ecosystems within the manufacturing environment.

Additionally, manufacturers can identify potential risks through clear data insights and implement strategies to mitigate uncertainties. Clear data is crucial for ensuring compliance with regulatory standards, a necessity in Industry 4.0, given the increasing focus on stringent regulations.

Actian’s Role in Manufacturing Data Management

Actian has decades of experience helping manufacturers create and implement robust data management strategies. Actian’s solutions enable data-driven decision-making processes, ensuring manufacturers not only stay competitive in the present but also remain agile and prepared for the future.

In the dynamic landscape of manufacturing, a well-crafted data management strategy is not just a necessity, it’s a roadmap to success. As the industry hurdles towards an era of unprecedented technological advancement, manufacturers must ensure their strategies are not only current but also forward-looking. It’s time to embrace the future of manufacturing by putting data at the forefront of operations, and Actian is here to guide that transformative journey.

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.

Data Management

7 Reasons to Move Mission-Critical Databases to the Cloud Using Migration

Teresa Wingfield

January 10, 2024

Digital transformation refers to the integration and application of digital technologies, processes, and strategies across an organization to fundamentally improve how it operates and delivers value to its customers, the business, and others. It involves leveraging digital tools, data, and technologies to automate business processes, improve customer experiences, drive innovation, and support data-driven decision-making.

The adoption and growth of digital transformation are massive, according to Statista Research. “In 2022, digital transformation spending reached USD 1.6 trillion. By 2026, global spending on digital transformation is anticipated to reach USD 3.4 trillion.” A recent Gartner study reported that digital change is an organizational priority for 87% of senior executives and CIOs, requiring additional focus on data modernization.

Why Move Mission-Critical Databases With Cloud Migration?

Which digital transformation project(s) to pursue depends on your specific situation and goals to ensure successful outcomes. Moving mission-critical databases to the cloud is an untapped opportunity for many organizations, and it can offer tremendous advantages if they do not face compliance and confidentiality restrictions for cloud use or do not have complex legacy entanglements that make moving too hard. Explore these key benefits of cloud migration:

Faster Business Agility

The cloud removes the need for infrastructure procurement, setup, management, and maintenance. Cloud migration helps organizations respond quickly to market changes, customer demands, and competitive pressures by quickly scaling to meet business needs. This agility can lead to a competitive advantage in dynamic industries.

On-Demand Scalability

Cloud elasticity is useful in environments that require the ability to scale storage and compute up or down to match demand. Cloud-native deployments that offer cloud elasticity can ensure optimal performance even during peak usage periods, such as retail during busy holiday shopping or government at tax time.

Greater Sustainability

The shared infrastructure of cloud providers allows for better economies of scale because they can distribute the cost of data centers, cooling systems, and other resources efficiently among many customers. And, instead of over-provisioning infrastructure in their own data center to handle occasional spikes in demand, organizations can dynamically adjust resources in the cloud, minimizing waste.

Optimized User Experience

Regional data centers of cloud providers allow organizations to host databases closer to end users for better performance, responsiveness, and availability of applications. This is especially useful when low latency and quick data access are critical, such as electronic commerce, online gaming, live streaming, and interactive web applications.

Stronger Security and Compliance

Major cloud providers invest significantly in physical security, network security, and data center protections that should keep mission-critical databases safe, provided organizations adhere to best practices for implementation and security. In addition, cloud providers often offer compliance certifications for various regulatory standards that can help simplify the process of adhering to industry regulations and requirements.

Improved Backup and Recovery

The cloud allows organizations to create backups and implement disaster recovery more efficiently. This is crucial for safeguarding mission-critical data against unforeseen events such as data corruption, cyber-attacks, and natural disasters.

Staff Optimization

Many database vendors offer managed or co-managed services in the cloud. These may include taking care of tasks such as provisioning, scaling, hardware maintenance, software patches, upgrades, backups, query optimization, table and index management, application tuning, and more. This leaves teams more time to focus on strategic initiatives with higher business value.

How Actian Supports Migration of Mission-Critical Databases to the Cloud

Actian recognizes that mission-critical database migration to the cloud represents one of the most exciting digital transformation opportunities, making a high impact on business agility, scalability, sustainability, data protection, and staff optimization. This is why we developed the Ingres NeXt Initiative to provide businesses with flexible options for infrastructure, software, and management of the Ingres database running on Google Cloud, Microsoft Azure, and Amazon Web Services. Our cloud migration approach minimizes risk, protects business logic, lowers costs, reduces time and effort, and decreases business disruption.

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.

Data Intelligence

The Top Priorities & Challenges for CDOs in 2024

Actian Corporation

January 10, 2024

In the fast-paced world of modern business, data collection, transformation, and utilization have become indispensable for organizations striving to maintain a competitive edge. The pursuit of becoming more data-centric is evident across industries, with many organizations appointing Chief Data Officers (CDOs) to lead them into a future where valuable insights are swiftly uncovered and acted upon. In the summer of 2023, a comprehensive global study was conducted by AWS that delved into the evolving role of CDOs, their key priorities, and the challenges they faced in 2023.

In this article, we’ll go over their main findings and what’s going to be their focus in 2024.

Generative AI, an Upcoming Trend?

Enthusiastic Approach to the Potential of Generative AI

While Generative AI adoption is in its nascent stages, CDOs across industries are actively exploring its possibilities. There is a lot of excitement surrounding Generative AI, with some CDOs expressing how it has elevated their standing within their organizations. However, the study reveals that, for the time being, Generative AI use is largely experimental for many organizations. Nearly one-third of respondents indicated that they are “experimenting at the individual level,” without a comprehensive enterprise strategy.

Despite the current exploratory nature of Generative AI initiatives, CDOs envision a transformative future. A striking 80 percent of respondents believe that Generative AI will ultimately transform their organizations’ business. Furthermore, 46 percent foresee or are already witnessing widespread adoption of Generative AI within their organizations, and 62 percent are planning to increase their investments in Generative AI, underscoring the anticipation of its growing significance.

Ensuring Data Quality, Trust, and Security are the Biggest Challenges of Generative AI

However, a significant percentage of CDOs pinpointed data quality as the primary challenge for Generative AI. The foundational role of high-quality data in training Generative AI models cannot be overstated, and finding the right use cases is pivotal for generating meaningful insights and value.

Establishing barriers for responsible use also emerged as a concern, as a mere 43 percent of CDOs reflect the growing recognition of the need for ethical and responsible AI practices. Security and privacy of data closely followed, as well as data literacy and proficiency, underscoring the need for a workforce capable of harnessing the power of Generative AI.

Data Governance is Still a Priority

Companies are Changing Approaches to Data Governance

For the second consecutive year, data governance has emerged as the principal activity consuming a significant portion of CDOs’ time, reflecting a surge from 44 percent in 2022 to 63 percent in 2023. In addition, more than half of CDOs (51%) consider data governance as a top responsibility, with 66% indicating that it consumes at least 20% of their time.

The AWS Report highlights that data governance goals revolve around ensuring data availability, building trust in data, and safeguarding data protection. Without a robust data governance component, no data strategy can be executed efficiently – data governance is considered the number one avenue to value creation for CDOs.

CDOs acknowledge that accomplishing effective data governance is challenging, primarily due to the significant behavioral changes it necessitates within organizations. The traditional concept of “governance” is transforming some firms, with a positive shift toward a “data enablement” focus. This change in terminology reflects an evolving perspective that positions data governance as an enabler rather than a restrictive measure.

Data Culture and Literacy are Still a Challenge to use Data Effectively

Creating a data-driven culture emerges as the paramount challenge, according to the report. The survey highlights the multifaceted nature of this challenge, encompassing organizational behaviors, attitudes, and the absence of a data-driven culture or decision-making approach. CDOs grapple with the task of instilling a data-centric mindset within their organizations, encountering various hurdles in the process. The main challenges were based on:

Difficulty in Changing Organizational Behaviors and Attitudes (70%).
Absence of Data-Driven Culture or Decision-Making (59%).
Lack of Data Literacy or Understanding (50%).
Insufficient Resources to Accomplish Goals (55%).

To address these challenges, CDOs are actively engaged in data-driven culture initiatives, with over half dedicating one-fifth of their time or more to these programs. These initiatives often include data literacy programs and change management approaches tailored to specific data or analytics projects.

Visible Business Value Creation

Analytics and AI in Project Development

In 2022, analytics and AI projects were recognized as crucial for delivering measurable value, a sentiment that has only strengthened in 2023. Over half of the respondents now prioritize a focused approach, concentrating on a small set of key analytics or AI projects as a primary avenue for value creation.

Despite data management being a primary responsibility, a noteworthy 44 percent of CDOs are emphasizing data management initiatives, such as enhancing data infrastructure, within the specific context of each analytics and AI use case rather than as a standalone effort.

Towards a Data Product Approach

The concept of data products, born out of the revolutionary framework known as the data mesh, represents a novel approach to data management. Grounded in the principle of treating data as a product, this innovative concept introduces a set of characteristics that redefine how organizations perceive and leverage their data assets.

According to the study, 39 percent of CDOs are embracing a data product management orientation, incorporating dedicated product managers into their teams. This approach ensures a comprehensive and disciplined management of all facets of analytics or AI initiatives, from inception to deployment and ongoing maintenance.

In the report, Sebastian Klapdor, Chief Data and Technology at Vista was quoted as saying: “The data product focus has brought data and analytics people much closer to the rest of the organization. Now data product managers will start to follow the same way of working as the PMs building customer facing software and I have taken responsibility for technology as well as data.“

In Conclusion

In conclusion, the landscape for CDOs in 2024 is shaped by dynamic challenges and evolving priorities as revealed in the CDO Agenda 2024 by AWS. The exploration of generative AI showcases both excitement and caution among CDOs – While transformative potential is widely acknowledged, challenges such as data quality, ethical considerations, and security underscore the need for a balanced and responsible approach.

In addition, data governance remains a persistent focus, with a shifting perspective towards “data enablement” and the ongoing struggle to instill a data-driven culture within organizations.

Finally, the pursuit of visible business value creation emphasizes a shift towards a data product approach and strategic integration of analytics and AI in project development. CDOs are not only navigating technological advancements but are also actively addressing the cultural and organizational shifts required to harness the full potential of data in the ever-evolving business landscape of 2024.

About Actian Corporation

Data Intelligence

Product Recap: A Look Back at 2023

Actian Corporation

January 8, 2024

2023 was another big year for the Actian Data Intelligence Platform. With more than 50 releases and updates to our platform, these past 12 months were filled with lots of new and improved ways to unlock the value of your enterprise data assets. Indeed, our teams consistently work on features that simplify and enhance the daily lives of your data and business teams.

In this article, we’re thrilled to share with you some of our favorite features from 2023 that enabled our customers to:

Decrease data search and discovery time.
Increase Data Steward productivity & efficiency.
Deliver trusted, secure, and compliant information across the organization.
Enable end-to-end connectivity with all their data sources.

Decrease Data Search and Discovery Time

One of the Actian Data Intelligence Platform’s core values is simplicity. We strongly believe that data discovery should be quick and easy to accelerate data-driven initiatives across the entire organization.

In fact, many data teams still struggle to find the information they need for a report or use case. Either because they couldn’t locate the data because it was scattered in various sources, files, or spreadsheets, or maybe they were confronted with an overwhelming amount of information, they didn’t even know how to begin their search.

In 2023, we designed our platform with simplicity. By providing easy and quick ways to explore data, the Actian Data Intelligence Platform enabled our customers to find, discover, and understand their assets in seconds.

A Fresh New Look for Actian Explorer

One of the first ways our teams wanted to enhance the discovery experience of our customers was by providing a more user-friendly design to our data exploration application, Actian Explorer. This redesign included:

New Homepage

Our homepage needed a brand-new look and feel for a smoother discovery experience. Indeed, for users who don’t know what they are looking for, we added brand-new exploration paths directly accessible via the Actian Explorer homepage.

Browsing by Item Type: If the user is sure of the type of data asset they are looking for, such as a dataset, visualization, data process, or custom asset, they directly access the catalog with it pre-filtered with the needed type of asset.
Browsing Through the Business Glossary: Users can quickly navigate through the enterprise’s business glossary by directly accessing the glossary assets that were defined or imported by stewards in Actian Studio.
Browsing by Topic: The app enables users to browse through a list of items that represent a specific theme, use case, or anything else that is relevant to business (more information below).

New Item Detail Pages

To understand a catalog item at a glance, one of the first notable changes was the position of the item’s tabs. The tabs were originally positioned on the left-hand side of the page, which took up a lot of space. Now, the tabs are at the top of the page, more closely reflecting the layout of the Studio app. This new layout allows data consumers to find the most significant information about an item such as:

The highlighted properties, defined by the Data Steward in the catalog design.
Associated glossary terms, to understand the context of the item.
Key people, to quickly reach the contacts that are linked to the item.

In addition, our new layout allows users to find all fields, metadata, and all other related items instantly. Divided into three separate tabs in the old version, data consumers now find the item’s description and all related items in a single “Details” tab. Indeed, depending on the item type you are browsing through, all fields, inputs & outputs, parent/children glossary items, implementations, and other metadata are in the same section, saving you precious data discovery time.

Lastly, the spaces for our graphical components were made larger – users now have more room to see their item’s lineage, data model, etc.

New Filtering System

Actian Explorer offers a smart filtering system to contextualize search results. Actian Data Intelligence Platforms preconfigured filters can be used such as by item type, connection, contact, or by the organization’s own custom filters. For even more efficient searches, we redesigned our search results page and filtering system:

Available filters are always visible, making it easier to narrow down the search.
By clicking on a search result, an overview panel with more information is always available without losing the context of the search.
The filters most relevant to the search are placed at the top of the page, allowing to quickly get the results needed for specific use cases.

Easily Browsing the Catalog by Topic

One major 2023 release was our topics feature. Indeed, to enable business users to (even more) quickly find their data assets for their use cases, Data Stewards can easily define Topics in Actian Studio. To do so, they simply select the filters in the catalog that represent a specific theme, use case, or anything else that is relevant to business.

Data teams using Actian Explorer can therefore easily and quickly search through the catalog by topic to reduce their time searching for the information they need. Topics can be directly accessed via the Explorer homepage and the search bar when browsing the catalog.

Alternative Names for Glossary Items for Better Discovery

In order for users to easily find the data and business terms they need for their use cases, Data Stewards can add synonyms, acronyms, and abbreviations for glossary items.

Ex: Customer Relationship Management > CRM

Improved Search Performance

Throughout the year, we implemented a significant amount of improvements to enhance the efficiency of the search process. The addition of stop words, encompassing pronouns, articles, and prepositions, ensures a more refined and pertinent outcome for queries. Moreover, we added an “INFIELD:” operator, enabling users the capability to search for datasets that contain a specific field.

Microsoft Teams Integration

Actian Data Intelligence Platform also strengthened our communication and collaboration capacities. Specifically, when a contact is linked to a Microsoft email address, the platform now facilitates the initiation of direct conversations via Teams. This integration allows Teams users to promptly engage with relevant individuals for additional information on specific items. Other integrations with various tools are in the works.

Increase Data Steward Productivity & Efficiency

Our goal at the Actian Data Intelligence Platform is to simplify the lives of data producers so they can efficiently manage, maintain, and enrich the documentation of their enterprise data assets in just a few clicks. Here are some features and enhancements that help to stay organized, focused, and productive.

Automated Datasets Import

When importing new datasets in the catalog, administrators can turn on our automatic import feature which automatically imports new Items after each scheduled inventory. This time-saving enhancement increases operational efficiency, allowing Data Stewards to focus on more strategic tasks rather than the routine import process.

Orphan Fields Deletion

We’ve also added the to manage orphan fields more effectively. This includes the option to perform bulk deletions of orphan fields, accelerating the process of decluttering and organizing the catalog. Alternatively, Stewards can delete a single orphan field directly from its detailed page, providing a more granular and precise approach to catalog maintenance.

Building Reports Based on the Content of the Catalog

We added a new section in Actian Studio – The Analytics Dashboard – to easily create and build reports based on the content and usage of the organization’s catalog.

Directly on the Analytics Dashboard page, Stewards can view the completion level of their item types, including custom items. Each item type element is clickable to quickly view the catalog section filtered by the selected item type.

For more detailed information on the completion level of a particular item type, Stewards can create their own analyses. They select the item type and a property, and they’re able to consult, and for each value of this property, the completion level of all your item’s template, including its description, and linked glossary items.

New Look for the Steward Dashboard

Actian Explorer isn’t the only application that got a makeover. Indeed, to help Data Stewards stay organized, focused, and productive, we redesigned the dashboard layout to be more intuitive to get work done faster. This includes:

New Perimeter Design: A brand new level of personalization when logging in to the dashboard. The perimeter now extends beyond dataset completion – it includes all the Items that one is a curator for, including fields, data processes, glossary items, and custom items.

Watchlists Widget: Just as Data Stewards create topics for enhanced organization for Explorer users, they can now create watchlists to facilitate access to items requiring specific actions. By filtering the catalog with the criteria of their choice, Data Stewards save these preferences as new watchlists via the “Save filters as” button, and directly access them via the watchlist widget when logging on to their dashboard.

The Latest Searches Widget: Caters specifically to the Data Steward, focusing on their recent searches to enable them to pick up where they left off.
The most popular items widget: The most consulted and widely used items within the Data Steward’s perimeter by other users. Each item is clickable, giving instant access to its contents.

Data Sampling on Datasets

For select connections, it is possible to get Data Sampling for datasets. Our Data Sampling capabilities allow users to obtain representative subsets of existing datasets, offering a more efficient approach to working with large volumes of data. With Data Sampling activated, administrators can configure fields to be obfuscated, mitigating the risk of displaying sensitive personal information.

This feature carries significant importance to our customers, as it enables users to save valuable time and resources by working with smaller, yet representative, portions of extensive datasets. This also allows early identification of data issues, thereby enhancing overall data quality and subsequent analyses. Most notably, the capacity to obfuscate fields addresses critical privacy and security concerns, allowing users to engage with anonymized or pseudonymized subsets of sensitive data, ensuring compliance with privacy regulations, and safeguarding against unauthorized access.

Powerful Lineage Capabilities

In 2022, we made a lot of improvements to our lineage graph. Not only did we simplify its design and layout, but we also made it possible for users to display only the first level of lineage, expand and close the lineage on demand, and get a highlighted view of the direct lineage of a selected item.

This year we made significant other UX changes, including the possibility to expand or reduce all lineage levels in one click, hide the data processes that don’t have at least one input and one output, and easily view the connections via a tooltip for connections that have long names.

However, the most notable release is the possibility to have field-level lineage. Indeed, it is now possible to retrieve the input and output fields of tables and reports, and for more context, add the operation’s description. Then, users can directly view their field level transformations over time in the data lineage graph in both Actian Explorer and Actian Studio.

Data Quality Information on Datasets

By leveraging GraphQL and knowledge graph technologies, the Data Intelligence Platform provides a flexible approach to integrating best-of-breed data quality solutions. It synchronizes datasets via simple query and mutation operations from third-party DQM tool via our catalog API capabilities. The DQM tool will deliver real-time data quality scan results to the corresponding dataset within the platform, enabling users the ability to conveniently review data quality insights directly within the catalog.

This new feature includes:

A data quality tab in your dataset’s detail pages, where users can view its quality checks as well as the type, status, description, last execution date, etc.
The possibility to view more information on the dataset’s quality directly in the DQM tool via the “Open dashboard in [Tool Name]” link.
A data quality indicator of Datasets directly displayed in the search results and lineage.

Enable End-to-End Connectivity With all Their Data Sources

With the Actian Data Intelligence Platform, connect to all your data sources in seconds. Our platform’s built-in scanners and APIs enable organizations to automatically collect, consolidate, and link metadata from their data ecosystem. This year, we made significant enhancements to our connectivity to enable our customers to build a platform that truly represents their data ecosystem.

Catalog Management APIs

Recognizing the importance of API integration, the Actian Data Intelligence Platform has developed powerful API capabilities that enable organizations to seamlessly connect and leverage their data catalog within their existing ecosystem.

In 2023, the platform developed catalog APIs, which help Data Stewards with their documentation tasks. These catalog APIs include:

Query operations to retrieve specific catalog assets: Our API query operations include the retrieval of a specific asset, using its unique reference or by its name & type, or retrieving a list of assets via connection or a given Item type. Indeed, the platform’s catalog APIs enable flexibility when querying by being able to narrow results to not be overwhelmed with a plethora of information.

Mutation operations to create and update catalog assets: To save even more time when documenting and updating company data, the platform’s catalog APIs enable data producers to easily create, modify, and delete catalog assets. It enables the creation, update, and deletion of custom items and data processes as well as their associated metadata, and update datasets and data visualizations. This is also possible for contacts. This is particularly important when users leave the company or change roles – data producers can easily transfer the information that was linked to a particular person to another.

Property & Responsibility Codes Management

Another feature that was implemented was the ability to add code to properties & responsibilities to easily use them in API scripts for more reliable queries & retrievals.

For all properties and responsibilities that were built in the Actian Data Intelligence Platform (e.g.: Personally Identifiable Information) or harvested from connectors, it is possible to modify its name and description to better suit the organization’s context.

More Than a Dozen More Connectors to the List

Actian Data Intelligence Platform has advanced connectors to automatically synchronize metadata between our data discovery platform and all your sources. This native connectivity saves you the tedious and challenging task of manually finding the data you need for a specific business use case that often requires access to scarce technical resources.

In 2023 alone, we developed over a dozen new connectors. This achievement underscores our agility and proficiency in swiftly integrating with diverse data sources utilized by our customers. By expanding our connectivity options, we aim to empower our customers with greater flexibility and accessibility.

About Actian Corporation

Data Management

How to Build an Effective Data Management Project Plan

Scott Norris

January 4, 2024

There are a myriad of definitions of what a data management plan, or DMP, is and what it entails. These definitions often vary slightly between organizations, but the goal is the same—to create a document specifying how data is managed throughout the lifecycle of a project. It’s a necessary step to ensure that everyone throughout your organization who uses data follows established policies to ensure data integrity and security.

In essence, a comprehensive data management plan is a living document that covers the required data sources, governance, policies, access, management, security, and other components that come into play for using data. The plan also includes how data should be integrated, used, and stored for a project, use case, business application, or other purpose.

The plan is needed to ensure data meets your quality and usage requirements to deliver trusted outcomes. At a corporate level, you need to create a detailed plan to guide and govern your data usage, and have a modern data platform in place that allows you to manage your data while making it easily accessible to everyone who needs it.

Essential Components of a Data Management Plan

It’s best to think of the data management plan as a policy. A best practice is to define your goals and use cases for how you plan to utilize the data, and then create your plan based on those needs. You can always update the plan as requirements change.

Categorizing data can help inform the plan by answering questions such as:

What are you planning to do with the data?
Does the data format need to change?
How do you want to store the data?
What is the expiration date of the data?
Does the data set meet your usage requirements?

Based on your use cases and requirements, you may need to have a separate data policy for each project. The policies will probably be similar, and you can have a general overall data management plan that serves as the foundation for one-off plans that can be customized to meet a specific use case’s unique needs. For example, a plan may need to cover how data is managed to meet GDPR, HIPAA, or personally identifiable information (PII) requirements.

Likewise, the plan must meet the compliancy mandates of applicable countries or regions. This can get complex very quickly. That’s because some states, such as California, have their own data privacy laws that must be followed. Because policies and compliance mandates can change over time, the data management plan must be a live document that can be easily updated to meet evolving requirements.

The plan also needs to cover storage, backup, and security for the data. How and where will you store your data? In the cloud, on-premises, or a hybrid environment? How often will the data need to be backed up, and by what method? In addition, will the security methods meet your compliance requirements?

In addition, the data management plan should cover how you will monitor contextual details, such as metadata. In certain industries, such as pharmaceuticals, the data lineage is important to back up certain theories and study outcomes, so it must be part of the plan.

Keep a Strong Focus on Data Quality

Ensuring data meets your quality standard is key and, therefore, must be included in the plan. The data management plan should cover how data is ingested, integrated, updated, and profiled to ensure it meets the quality you need. The plan should also include criteria for determining when data should be deleted.

It’s up to each organization to set the quality standard for their data, but every company must share this standard with all data users—and ensure the standard is enforced to avoid data quality being compromised. At Actian, we fully understand the need for quality data that establishes trust from internal users, customers, and partners. If there is an issue, the first step is to trace the problem to the root cause to see if established policies in the data management plan were followed.

Creating a detailed plan is only part of the overall task of delivering trusted data. The other part is to educate data users about the policies, protocols, tools, and data platform to ensure everyone understands what’s required of them and how to handle any issues that arise. Training may also be required to show business analysts and others how to use the platform and data tools to maintain data quality and get the best results.

Regardless of how detailed the plan is, every data user has a responsibility to make sure they are following company protocols and that their devices that are connected to the data ecosystem meet company policies. Going outside the plan or taking shortcuts, such as creating or using data silos, can compromise data quality. At Actian, we often talk about the fact that poor data quality is a detriment to a company and its position in the marketplace, while making quality data readily available drives new and sustainable value.

Data Champions Should Own the Data Plan

Depending on the size of your company, either a person or a team will need to own the data management project plan. Generally speaking, the plan should fall under the auspices of the data and analytics team, but actual ownership is typically high up in the food chain. The CTO or CIO will usually designate a data champion—an individual or small group—who understands the current and emerging business needs and can facilitate data management policies.

This top-down approach to owning the plan helps ensure that ever-growing data volumes meet your company’s actual requirements. For example, a data engineer can put any system in place to collect data, but without a detailed understanding of how the data will be used, the engineer’s approach may not align with how the CTO or CIO plans to leverage, manage, and govern it.

The owners of the data plan will need to regularly review it to ensure it meets current needs, which often change and evolve as new data and use cases become available. The plan should also stay current on protocols for determining who can access the data, and from where. This is important in hybrid work environments when employees may need to access data remotely.

You naturally want data to be easily and readily available to everyone who needs it, but not accessible to those without proper authorization. This approach promotes a data-driven culture, but helps safeguard against unauthorized data access.

Protecting your data is an important part of the plan. This not only includes keeping it secure against potential internal breaches, but also covers incidents that are unlikely to happen, yet still possible. For instance, if someone mistakenly forgets their laptop at the airport, what’s the process for ensuring data access is not compromised? The data management plan should cover these types of scenarios.

Communicate Policies and Share the Plan

For the plan to be truly effective and have the most impact, it must be shared with everyone who uses data or is involved in data-gathering processes. The effectiveness of the plan comes down to how well it’s communicated to internal teams and data users. There’s a valid reason for creating the plan—and everyone needs to be aware of it, embrace it, adhere to it, and view it as the valuable resource it is.

Actian can help customers build and implement a comprehensive data management project plan and offer best practices for making it easily shareable across the organization. Our experts can create a plan from a data platform point of view that covers data ingestion, integration, quality, usage, security, and other key factors.

Our Actian Data Platform offers new data quality and profiling capabilities to give business analysts and others complete confidence in their data. With more data to manage and more sources to connect to, you need a scalable platform that can meet today’s data needs by providing fast query speeds at a competitive price point, which our data platform delivers.

We can help you strategically and effectively connect, govern, and manage your data to inform business decisions, automate processes, and drive other uses. Try the Actian Data Platform to experience for yourself how easy it is to use and the value it offers. Have questions on creating a detailed plan for your specific needs? Talk to us. We’re here to help.

Additional Resources:

About Scott Norris

Scott Norris is a veteran IT professional with 30+ years as a Program Manager, Solutions Architect, and System Engineer. He has managed complex implementations involving data integration, pre-/post-sales consultations, and advanced system design. Scott has led workshops on program/project management, training, and application development. On the Actian blog, Scott shares tips on unified data strategies, client engagement, and modernization. Check out his posts for strategic guidance.

Data Integration

Top Capabilities to Look for in Database Management Tools

Derek Comingore

January 2, 2024

As businesses continue to tap into ever-expanding data sources and integrate growing volumes of data, they need a solid data management strategy that keeps pace with their needs. Similarly, they need database management tools that meet their current and emerging data requirements.

The various tools can serve different user groups, including database administrators (DBAs), business users, data analysts, and data scientists. They can serve a range of uses too, such as allowing organizations to integrate, store, and use their data while following governance policies and best practices. The tools can be grouped into categories based on their role, capabilities, or proprietary status.

For example, one category is open-source tools, such as PostgreSQL or pgAdmin. Another category is tools that manage an SQL infrastructure, such as Microsoft’s SQL Server Management Studio, while another is tools that manage extract, transform, and load (ETL) and extract, load, and transform (ELT) processes, such as those natively available from Actian.

Using a broad description, database management tools can ultimately include any tool that touches the data. This covers any tool that moves, ingests, or transforms data, or performs business intelligence or data analytics.

Data Management Tools for Modern Use Cases

Today’s data users require tools that meet a variety of needs. Some of the more common needs that are foundational to optimizing data and necessitate modern capabilities include:

Data Management: This administrative and governance process allows you to acquire, validate, store, protect, and process data.
Data Integration: Integration is the strategic practice of bringing together internal and external data from disparate sources into a unified platform.
Data Migration: This entails moving data from its current or storage location to a new location, such as moving data between apps or from on-premises to the cloud.
Data Transformation: Transformative processes change data from one format or structure into another for usage and ensure it’s cleansed, validated, and properly formatted.
Data Modeling: Modeling encompasses creating conceptual, logical, and physical representations of data to ensure coherence, integrity, and efficiency in data management and utilization.
Data Governance: Effective governance covers the policies, processes, and roles used to ensure data security, integrity, quality, and availability in a controlled, responsible way.
Data Replication: Replicating data is the process of creating and storing multiple copies of data to ensure availability and protect the database against failures.
Data Visualization: Visualizing data turns it into patterns and visual stories to show insights quickly and make them easily understandable.
Data Analytics and Business Intelligence: These are the comprehensive and sophisticated processes that turn data into actionable insights.

It’s important to realize that needs can change over time as business priorities, data usage, and technologies evolve. That means a cutting-edge tool from 2020, for example, that offered new capabilities and reduced time to value may already be outdated by 2024. When using an existing tool, it’s important to implement new versions and upgrades as they become available.

You also want to ensure you continue to see a strong return on investment in your tools. If you’re not, it may make more sense from a productivity and cost perspective to switch to a new tool that better meets your needs.

Ease-of-Use and Integration Are Key

The mark of a good database management tool—and a good data platform—is the ability to ensure data is easy-to-use and readily accessible to everyone in the organization who needs it. Tools that make data processes, including analytics and business intelligence, more ubiquitous offer a much-needed benefit to data-driven organizations that want to encourage data usage for everyone, regardless of their skill level.

All database management tools should enable a broad set of users—allowing them to utilize data without relying on IT help. Another consideration is how well a tool integrates with your existing database, data platform, or data analytics ecosystem.

Many database management tool vendors and independent software vendors (ISVs) may have 20 to 30 developers and engineers on staff. These companies may provide only a single tool. Granted, that tool is probably very good at what it does, with the vendor offering professional services and various features for it. The downside is that the tool is not natively part of a data platform or larger data ecosystem, so integration is a must.

By contrast, tools that are provided by the database or platform vendor ensure seamless integration and streamline the number of vendors that are being used. You also want to use tools from vendors that regularly offer updates and new releases to deliver new or enhanced capabilities.

If you have a single data platform that offers the tools and interfaces you need, you can mitigate the potential friction that oftentimes exists when several different vendor technologies are brought together, but don’t easily integrate or share data. There’s also the danger of a small company going out of business and being unable to provide ongoing support, which is why using tools from large, established vendors can be a plus.

Expanding Data Management Use Cases

The goal of database management tools is to solve data problems and simplify data management, ideally with high performance and at a favorable cost. Some database management tools can perform several tasks by offering multiple capabilities, such as enabling data integration and data quality. Other tools have a single function.

Tools that can serve multiple use cases have an advantage over those that don’t, but that’s not the entire story. A tool that can perform a job faster than others, automate processes, and eliminate steps in a job that previously required manual intervention or IT help offers a clear advantage, even if it only handles a single use case. Stakeholders have to decide if the cost, performance, and usability of a single-purpose tool delivers a value that makes it a better choice than a multi-purpose tool.

Business users and data analysts often prefer the tools they’re familiar with and are sometimes reluctant to change, especially if there’s a long learning curve. Switching tools is a big decision that involves both cost and learning how to optimize the tool.

If you put yourself in the shoes of a chief data officer, you want to make sure the tool delivers strong value, integrates into and expands the current environment, meets the needs of internal users, and offers a compelling reason to make a change. You also should put yourself in the shoes of DBAs—does the tool help them do their job better and faster?

Delivering Data and Analytics Capabilities for Today’s Users

Tool choices can be influenced by no-code, low-code, and pro-code environments. For example, some data leaders may choose no- or low-code tools because they have small teams that don’t have the time or skill set needed to work with pro-code tools. Others may prefer the customization and flexibility options offered by pro-code tools.

A benefit of using the Actian Data Platform is that we offer database management tools to meet the needs of all types of users at all skill levels. We make it easy to integrate tools and access data. The platform offers no-code, low-code, and pro-code integration and transformation options. Plus, the unified platform’s native integration capabilities and data quality services feature a robust set of tools essential for data management and data preparation.

Plus, Actian has a robust partner ecosystem to deliver extended value with additional products, tools, and technologies. This gives customers flexibility in choosing tools and capabilities because Actian is not a single product company. Instead, we offer products and services to meet a growing range of data and analytics use cases for modern organizations.

Additional Resources:

About Derek Comingore

Derek Comingore has over two decades of experience in database and advanced analytics, including leading startups and Fortune 500 initiatives. He successfully founded and exited a systems integrator business focused on Massively Parallel Processing (MPP) technology, helping early adopters harness large-scale data. Derek holds an MBA in Data Science and regularly speaks at analytics conferences. On the Actian blog, Derek covers cutting-edge topics like distributed analytics and data lakes. Read his posts to gain insights on building scalable data pipelines.

Data Platform

The Actian Data Platform’s Superior Price-Performance

Phil Ostroff

December 27, 2023

When it comes to choosing a technology partner, price and performance should be top of mind. “Price-performance” refers to the measure of how efficiently a database management system (DBMS) utilizes system resources, such as processing power, memory, and storage, in relation to its cost. It is a crucial factor for organizations to consider when selecting a DBMS, as it directly impacts the overall performance and cost-effectiveness of their data management operations. The superior Data Management Platform, the Actian Data Platform, can provide the price-performance you’re looking for and more.

Getting the most value out of any product or service has always been a key objective of any smart customer. This is especially true of those who lean on database management systems to help their businesses compete and grow in their respective markets, even more so when you consider the exponential growth in both data sources and use cases in any given industry or vertical. This might apply if you’re an insurance agency that needs real-time policy quote information, or if you’re in logistics and need the most accurate, up-to-date information about the location of certain shipments. Addressing use cases like these as cost-effectively as possible is key in today’s fast-moving world. Key benefits of the Actian Data Platform include:

The Importance of Prioritizing Optimal Price-Performance

Today, CFOs and technical users alike are trying to find ways to get the best price-performance possible from their DBMS systems. Not only are CFOs interested in up-front acquisition and implementation costs, but also all costs downstream that are associated with the utilization and maintenance of whichever system they choose.

Technical users of various DBMS offerings are also looking for alternative ways to utilize their systems to save costs. In the back alleys of the internet (places like Reddit and other forums), users of various DBMS platforms are discussing how to effectively “game” their DBMS platforms to get the best price-performance possible, sometimes leading to the development of shadow database solutions just to try and save costs.

According to a December 2022 survey by Actian, 56% of businesses struggle to maintain costs as data volumes and workloads increase. These types of increases affect the total cost of ownership and related infrastructure maintenance, support, query complexity, the number of concurrent users, and management overhead, which have a significant impact on the costs involved in using a database management system.

Superior Price-Performance

Having been established over 50 years ago, Actian was in the delivery room when enterprise data management was born. Since then, we’ve had our fingers on the pulse of the market’s requirements, developing various products that meet various use cases from various industries worldwide.

The latest version of the data management platform, the Actian Data Platform, includes native data integrations with 300+ out-of-the-box connectors and scalable data warehousing and analytics that produce REAL real-time insights to more confident support decision-making. The Actian Data Platform can be used on-premises, in the cloud across multiple clouds, and in a hybrid model. The platform also provides no-code, low-code, and pro-code solutions to enable a multitude of users, both technical and non-technical.

The 2023 Gigaom TCP-H Benchmark Test

At Actian, we’re really curious about how our platform compared with other major players and whether or not it could help deliver the price-performance being sought after in the market. In June of 2023, we commissioned a TCP-H Benchmark test with GigaOm, pitting the Actian Data Platform against both Google Big Query and Snowflake. This test involved running 22 queries against a 30TB TCP-H data set. Actian’s response times were better than the competition in 20 of those 22 requests. Furthermore, the benchmark report revealed that:

In a test of five concurrent users, Actian was overall 3x faster than Snowflake and 9x faster than Big Query.

In terms of price-performance, the Actian Data Platform produced even greater advantages when running the five concurrent user TPC-H queries. Actian proved roughly 4x less expensive to operate than Snowflake, based on cost per query per hour, and 16x less costly than BigQuery.

These were compelling results. Overall, the GigaOm TCP-H benchmark shows that the data management platform, the Actian Data Platform, is a high-performance cloud data warehouse that is well-suited for organizations that need to analyze large datasets quickly and cost-effectively.

Actian customer, the Automobile Association (AA), located in the United Kingdom, was able to reduce their quote response time to 400 milliseconds. Without the speed provided by the Actian Platform, they wouldn’t have been able to provide prospective customers the convenience of viewing insurance quotes on their various comparison pages, which allows them to gain and maintain a clear advantage over their competitors.

Let Actian Help

If price-performance is a key factor for you, and you’re looking for a complete data platform that will provide superior capabilities and ultimately lower your TCO, do these three things:

Contact us! One of our friendly, knowledgeable representatives will be in touch with you to discuss the benefits of the Actian Data Platform and how we can help you have more confidence in your data-driven decisions that keep your business growing.
Check out our technology solutions.

About Phil Ostroff

Phil Ostroff is Director of Competitive Intelligence at Actian, leveraging 30+ years of experience across automotive, healthcare, IT security, and more. Phil identifies market gaps to ensure Actian's data solutions meet real-world business demands, even in niche scenarios. He has led cross-industry initiatives that streamlined data strategies for diverse enterprises. Phil's Actian blog contributions offer insights into competitive trends, customer pain points, and product roadmaps. Check out his articles to stay informed on market dynamics.

Data Management

Do You Have a Data Quality Framework?

Emma McGrattan

December 21, 2023

We’ve shared several blogs about the need for data quality and how to stop data quality issues in their tracks. In this post, we’ll focus on another way to help ensure your data meets your quality standards on an ongoing basis by implementing and utilizing a data quality management framework. Do you have this type of framework in place at your organization? If not, you need to launch one. And if you do have one, there may be opportunities to improve it.

A data quality framework supports the protocols, best practices, and quality measures that monitor the state of your data. This helps ensure your data meets your quality threshold for usage and allows more trust in your data. A data quality framework continuously profiles data using systematic processes to identify and mitigate issues before the data is sent to its destination location.

Now that you know a data quality framework is needed for more confident, data-driven decision-making and data processes, you need to know how to build one.

Establish Quality Standards for Your Use Cases

Not every organization experiences the same data quality problems, but most companies do struggle with some type of data quality issue. Gartner estimated that every year, poor data quality costs organizations an average of $12.9 million.

As data volumes and the number of data sources increase, and data ecosystems become increasingly complex, it’s safe to assume the cost and business impact of poor data quality have only increased. This proves there is a growing need for a robust data quality framework.

The framework allows you to:

Assess data quality against established metrics for accuracy, completeness, and other criteria.
Build a data pipeline that follows established data quality processes.
Pass data through the pipeline to ensure it meets your quality standard.
Monitor data on an ongoing basis to check for quality issues.

The framework should make sure your data is fit for purpose, meaning it meets the standard for the intended use case. Various use cases can have different quality standards (e.g. a customer’s bank account number must be 100% accurate, whereas a customer’s age or salary information might be provided within a range, so it won’t be 100% accurate). However, it’s common best practice to have an established data quality standard for the business as a whole. This ensures your data meets the minimum standard.

Key Components of a Data Quality Framework

While each organization will face its own unique set of data quality challenges, essential components needed for a data quality framework will be the same. They include:

Data Governance: Data governance makes sure that the processes, policies and roles used for data security, integrity, and quality are performed in a controlled and responsible way. This includes governing how data is integrated, handled, used, shared, and stored, making it a vital component of your framework.
Data Profiling: Actian defines data profiling as the process of analyzing data, looking at its context, structure and content, to better understand how it’s relevant and useful, what it’s missing, and how it can be augmented or improved. Profiling helps you identify any problems with the data, such as any inconsistencies or inaccuracies.
Data Quality Rules: These rules determine if the data meets your quality standard, or if it needs to be improved or transformed before being integrated or used. Predefining your rules will assist in verifying that your data is accurate, valid, complete, and meets your threshold for usage.
Data Cleansing: Filling in missing information, filtering out unneeded or bad data, formatting data to meet your standard, and ensuring data integrity is essential to achieving and maintaining data quality. Data cleansing helps with these processes.
Data Reporting. This reporting gives you information about the quality of your data. Reports can be documents or dashboards that show data quality metrics, issues, trends, recommendations, or other information.

These components work together to create the framework needed to maintain data quality.

Establish Responsibilities and Metrics

As you move forward with your framework, you’ll need to assign specific roles and responsibilities to employees. These people will manage the data quality framework and make sure the data meets your defined standards and business goals. In addition, they will implement the framework policies and processes, and determine what technologies and tools are needed for success.

Those responsible for the framework will also need to determine which metrics should be used to measure data quality. Using metrics allows you to quantify data quality across attributes such as completeness, timeliness, and accuracy. Likewise, these employees will need to define what good data looks like for your use cases.

Many processes can be automated, making the data quality framework scalable. As your data and business needs change and new data becomes available, you will need to evolve your framework to meet new requirements.

Expert Help to Ensure Quality Data

Your framework can monitor and resolve issues over the lifecycle of your data. The framework can be used for data in data warehouses, data lakes, or other repositories to deliver repeatable strategies, processes, and procedures for data quality.

An effective framework reduces the risk of poor-quality data—and the problems poor quality presents to your entire organization. The framework ensures trusted data is available for operations, decision-making, and other critical business needs. If you need help improving your data quality or building a framework, we’re here to help.

About Emma McGrattan

Emma McGrattan is CTO at Actian, leading global R&D in high-performance analytics, data management, and integration. With over two decades at Actian, Emma holds multiple patents in data technologies and has been instrumental in driving innovation for mission-critical applications. She is a recognized authority, frequently speaking at industry conferences like Strata Data, and she's published technical papers on modern analytics. In her Actian blog posts, Emma tackles performance optimization, hybrid cloud architectures, and advanced analytics strategies. Explore her top articles to unlock data-driven success.

Data Management

Is Your Data Quality Framework Up to Date?

Emma McGrattan

December 19, 2023

A data quality framework is the systematic processes and protocols that continually monitor and profile data to determine its quality. The framework is used over the lifecycle of data to ensure the quality meets the standard necessary for your organization’s use cases.

Leveraging a data quality framework is essential to maintain the accuracy, timeliness, and usefulness of your data. Yet with more data coming into your organization from a growing number of sources, and more use cases requiring trustworthy data, you need to make sure your data quality framework stays up to date to meet your business needs.

If you’re noticing data quality issues, such as duplicated data sets, inaccurate data, or data sets that are missing information, then it’s time to revisit your data quality framework and make updates.

Establish the Data Quality Standard You Need

The purpose of the framework is to ensure your data meets a minimum quality threshold. This threshold may have changed since you first launched your framework. If that’s the case, you will need to determine the standard you now need, then update the framework’s policies and procedures to ensure it provides the data quality required for your use cases. The update ensures your framework reflects your current data needs and data environment.

Evaluate Your Current Data Quality

You’ll want to understand the current state of your data. You can profile and assess your data to gauge its quality, and then identify any gaps between your current data quality and the quality needed for usage. If gaps exist, you will need to determine what needs to be improved, such as data accuracy, structure, or integrity.

Reevaluate Your Data Quality Strategy

Like your data quality framework, your data quality strategy needs to be reviewed from time to time to ensure it meets your current requirements. The strategy should align with business requirements for your data, and your framework should support the strategy. This is also an opportunity to assess your data quality tools and processes to make sure they still fit your strategy; and make updates as needed. Likewise, this is an ideal time to review your data sources and make sure you are bringing in data from all the sources you need—new sources are constantly emerging and may be beneficial to your business.

Bring Modern Processes into Your Framework

Data quality processes, such as data profiling and data governance, should support your strategy and be part of your framework. These processes, which continuously monitor data quality and identify issues, can be automated to make them faster and scalable. If your data processing tools are cumbersome and require manual intervention, consider modernizing them with easy-to-use tools.

Review the Framework on an Ongoing Basis

Regularly reviewing your data quality framework ensures it is maintaining data at the quality standard you need. As data quality needs or business needs change, you will want to make sure the framework meets your evolving requirements. This includes keeping your data quality metrics up to date, which could entail adding or changing your metrics for data quality.

Ensuring 7 Critical Data Quality Dimensions

Having an up-to-date framework helps maintain quality across these seven attributes:

Completeness

The data is not missing fields or other needed information and has all the details you need.

Validity

The data matches its intended need and usage.

Uniqueness

The data set is unique in the database and not duplicated.

Consistency

Data sets are consistent with other data in the database, rather than being outliers.

Timeliness

The data set offers the most accurate information that’s available at the time the data is used.

Accuracy

The data has values you expect and are correct.

Integrity

The data set meets your data quality and governance standards.

Your data quality framework should have the ability to cleanse, transform, and monitor data to meet these attributes. When it does, this gives you the confidence to make data-driven decisions.

What Problems Do Data Quality Frameworks Solve?

An effective framework can address a range of data quality issues. For example, the framework can identify inaccurate, incomplete, and inconsistent data to prevent poor-quality data from negatively impacting the business. A modern, up-to-date framework can improve decision-making, enable reliable insights, and potentially save money, by preventing incorrect conclusions or unintended outcomes caused by poor-quality data. A framework that ensures data meets a minimum quality standard also supports business initiatives and improves overall business operations. For instance, the data can be used for campaigns, such as improving customer experiences, or predicting supply chain delays.

Make Your Quality Data Easy to Use for Everyone

Maintaining data quality is a constant challenge. A current data quality framework mitigates the risk that poor quality data poses to your organization by keeping data accurate, complete, and timely for its intended use cases. When your framework is used in conjunction with the Actian Data Platform, you can have complete confidence in your data. The platform makes accurate data easy to access, share, and analyze to reach your business goals faster.

Additional Resources:

About Emma McGrattan

Data Intelligence

What is Cloud FinOps?

Actian Corporation

December 17, 2023

Cloud Financial Management Finops Conceptual Illustration

As organizations pursue their digital transformation journey, Cloud Computing has become an essential foundation for business performance. However, the unlimited flexibility of Cloud services is sometimes accompanied by rising costs, prompting companies to consider ways of controlling expenditure without degrading employee usage. To do so, they are implementing a Cloud financial management approach, also known as Cloud FinOps.

Does the term FinOps ring a bell? Derived from the contraction of Financial Operations, the term refers to a financial management methodology applied in Cloud Computing. The emergence of Cloud FinOps is linked to the need to control costs associated with the exponential growth in the use of Cloud services. This approach aims to reconcile the actions of financial, operational, and technical teams to optimize Cloud spending and guarantee optimal use of resources.

Cloud Finops focuses on cost transparency, identifying optimization opportunities, and empowering teams to take responsibility for their use of Cloud resources. By fostering collaboration between IT, finance, and business teams, Cloud Finops improves visibility, cost predictability, and operational efficiency, enabling companies to maximize the benefits of the Cloud while maintaining strict financial control.

How Does Cloud Finops Work?

Cloud Finops works through a combination of specific practices, processes, and architecture. In terms of architecture, cost monitoring tools, such as Cloud Financial Management platforms, are deployed to collect real-time data on resource usage. This information is then analyzed to identify opportunities for optimization.

In terms of processes, Cloud Finops encourages close collaboration between financial, operational, and technical teams, establishing regular review cycles to evaluate costs and adjust resource allocations. This iterative approach enables you to optimize spending on an ongoing basis, ensuring that your company makes efficient use of Cloud services while creating the conditions for total cost control.

What are Cloud FinOps Best Practices?

The practice of Cloud FinOps relies on a combination of methods, tools, processes, and vision. To take full advantage of your Cloud Finops approach, you’ll need to foster the emergence of a number of best practices.

Transparency & Synergy

The founding principles of Cloud FinOps are based on cross-functional collaboration. This involves the close involvement of financial, operational, and technical teams. This synergy enables a common understanding of business objectives and associated costs, promoting continuous optimization of Cloud resources.

Automation & Control

Automating processes is essential to ensure optimum cost management on a day-to-day basis. The use of automation solutions for automatic resource provisioning, instance scheduling, and all repetitive cloud management tasks, improves operational efficiency and avoids unnecessary waste.

Reporting & Analysis

To guarantee cost transparency, you need to be able to provide detailed, accessible reports on resource utilization. These reports enable teams to make informed decisions. This greater visibility encourages users to take responsibility and makes it easier to identify areas for improvement.

What are the Main Challenges for Cloud Finops?

To deliver its full potential, Cloud FinOps must overcome the complexity of Cloud pricing models. Indeed, the diversity of these models, which vary from one Cloud provider to another, makes it difficult to accurately forecast costs. As a result, expenditure can fluctuate according to demand, making budget planning more delicate.

Finally, compliance management, data security, and Cloud migration considerations are also complex aspects to integrate into an effective FinOps approach.

What Does the Future Hold for Cloud Finops?

As companies move further along the road to cloudification, the future of Cloud FinOps looks brighter month after month. Tools and platforms specializing in the financial management of Cloud resources, offering advanced cost analysis, automation, and forecasting capabilities, are likely to continue to grow in line with Cloud adoption.

Closer integration and collaboration between financial, operational, and technical teams will enable companies to place greater emphasis on financial governance in the Cloud, integrating FinOps principles right from the start of their Cloud projects.

About Actian Corporation

Data Management

How IT Leaders Leverage Unstructured Data

Emma McGrattan

December 15, 2023

Data-driven organizations are accustomed to using structured data. This type of data is well-defined, organized, stored in a tabular form, and typically managed in a relational database management system (RDBMS). The data is predefined and formatted to fit a set structure. A vast range of tools have been developed to optimize this type of data, which includes customer names, sales data, and transaction dates. The data is easily searchable by programming languages and data analytics tools, unlike unstructured data.

Unstructured data is different. It does not have a predefined data model or structure, making it more challenging to organize, process, and analyze using traditional databases or structured data formats. Unstructured data lacks a specific schema or format, and it can take many forms, including text, images, videos, audio recordings, social media posts, and more.

Let’s explore how IT leaders can leverage unstructured data to gain a better business advantage.

Automate Workflows for Unstructured Data

The majority of data—between 80% and 90%, according to some estimates, is unstructured. This means the data represents a huge treasure trove of value to businesses that can leverage it and use it effectively.

Bringing automated processes to unstructured data can help ensure the data is properly ingested and stored in a way that makes it accessible and usable across the enterprise. Automating processes improves efficiency, yet automation is oftentimes complex due to the data’s variability, size, and lack of a standard format. At the same time, organizations that can successfully automate unstructured data can unlock insights faster to drive decision-making.

According to TDWI, “Automating workflows to curate and deliver data to cloud-native analytics tools will help IT organizations efficiently leverage massive stores of unstructured data while reducing the manual effort required for data curation by data analysts and researchers. Data workflow automation is becoming a new requirement of unstructured data management platforms.”

IT leaders who implement the tools and technologies to harness unstructured data and make it available to analysts and business users can realize a variety of benefits such as:

Extracting information from texts to better understand customer needs, customer sentiments, and market trends.
Reviewing social media and other unstructured data to understand customer sentiment, preferences and behaviors, then delivering personalized recommendations for products, services, or content.
Analyzing text in documents such as legal contracts to ensure compliance.
Performing analysis on images for use cases spanning medical imaging diagnosis to quality control.
Identifying positive and negative customer reviews to understand how customers view a brand and to inform marketing strategies.
Reviewing unstructured data sources, including emails, text data, and transaction records to help detect fraud.
Integrating unstructured data with structured customer data to provide a complete view of customers, which can be used to personalize campaigns, improve customer service, and enhance customer experiences.

Using Unstructured Data for AI

Organizations across all industries are looking to implement Artificial Intelligence (AI) or Generative AI use cases. These use cases require data—often large volumes of data—and that can include unstructured data.

Fast Company writes that “unstructured data is the fuel needed for AI, yet most organizations aren’t using it well. One reason for this is that unstructured data is difficult to find, search across, and move, due to its size and distribution across hybrid cloud environments.”

Making all data readily available can support a diverse range of use cases, including those involving AI. For example, chatbots can analyze unstructured data to route customer questions to the appropriate source for an answer.

In addition, unstructured data, including streaming data from social media posts, news articles, sensor data, and other sources, can enable new possibilities for AI and machine learning. These possibilities include enabling AI to understand context and quickly analyze large data sets or volumes of text to identify relationships or summarize the information.

Integrate Data on an Easy-to-Use Platform

Managing and leveraging unstructured data allows organizations to gain deeper, richer insights into all aspects of the business. Likewise, implementing a data management strategy that includes unstructured data gives IT visibility into where the data is stored, which team owns the data, the costs to store it, and other pertinent information.

The ability to leverage alternative data, such as unstructured data, helps businesses make more informed decisions, identify changing market conditions sooner, and reach business objectives faster. Accessing unstructured data can advance priorities that may not be readily apparent. For instance, it can help with environmental, social, and governance (ESG) initiatives by enhancing transparency, assisting with ESG reporting and disclosure, and benchmarking ESG performance against industry leaders.

The unified Actian platform makes data easy across cloud, on-premises, and hybrid environments to empower business users and drive data-intensive applications. It also supports businesses’ confidence in their data, improves data quality, assists in lowering costs, and enables better decision-making across the business.

The Actian Data Platform is unique in its ability to collect, manage, and analyze data in real-time with its transactional database, data integration, data quality, and data warehouse capabilities in one easy-to-use platform.

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