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

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 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.

About Emma McGrattan

Data Management

How Partitioning on Your Data Platform Improves Performance

Colm Ginty

December 14, 2023

One of my goals as Customer Success Manager for Actian is to help organizations improve the efficiency and usability of our modern product suite. That’s why I recently wrote an extensive article on partitioning best practices for the Actian Data Platform in Actian communities resource.

In this blog, I’d like to share how partitioning can help improve the manageability and performance of the Actian platform. Partitioning is a useful and powerful function that divides tables and indexes into smaller pieces and can even subdivide them further into even smaller pieces. It’s like taking thousands of books and arranging them into categories—which is the difference between a massive pile of books in one big room and having the books strategically arranged into smaller topic areas; like you see in a modern library.

You can gain several business and IT benefits by using the partitioning function that’s available on our platform. For example, partitioning can lower costs by storing data most optimally and boost performance by executing queries in parallel across small, divided tables.

Why Distributing and Partitioning Tables are Critical to Performance

When we work in the cloud, we use distributed systems. So instead of using one large server, we use multiple regular-sized servers that are networked together and function like the nodes of a single enormous system. Traditionally, these nodes would both store and process data because storing data on the same node it is processed on enables fast performance.

Today, modern object storage in the cloud allows for highly efficient data retrieval by the processing node, regardless of where the data is stored. As a result, we no longer need to place data on the same node that will process it to gain a performance advantage.

Yet, even though we no longer need to worry about how to store data, we do need to pay attention to the most efficient way to process it. Oftentimes, the tables in our data warehouse contain too much data to be efficiently processed using only one node. Therefore, the tables are distributed among multiple nodes.

If a specific table has too much data to be processed by a single node, the table is split into partitions. These partitions are then distributed among the many nodes—this is the essence of a “distributed system,” and it lends itself to fast performance.

Partitioning in the Actian Data Platform

Having a partitioning strategy and a cloud data management strategy can help you get the most value from your data platform. You can partition data in many ways depending on, for example, an application’s needs and the data’s content. If performance is the primary goal, you can spread the load evenly to get the most throughput. Several partitioning methods are available on the Actian Data Platform.

Partitioning is important with our platform because it is architected for parallelism. Distributing rows of a large table to smaller sub-tables, or partitions, helps with fast query performance.

Users have a say in how the Actian platform handles partitions. If you choose to not manage the partition, the platform defaults to the automatic setting. In that case, the server makes its best effort to partition data in the most appropriate way. The downside is that with this approach, joining or grouping data that’s assigned to different nodes can require moving data across the network between nodes, which can increase costs.

Another option is to control the partitions yourself using a hash value to distribute rows evenly among partitions. This allows you to optimize partitioning for joins and aggregations. For example, if you’re querying data in the data warehouse and the query will involve many SQL joins or groupings, you can partition tables in a way that causes certain values in columns to be assigned to the same node, which makes joins more efficient.

When Should You Partition?

It’s a best practice to use the partitioning function in the Actian Data Platform when you create tables and load data. However, you probably have non-partitioned tables in your data warehouse, and redistributing this data can improve performance.

You can perform queries that will tell you how evenly distributed the data is in its current state in the data warehouse. You can then determine if partitioning is needed.

With Actian, you have the option to choose the best number of partitions for your needs. You can use the default option, which results in the platform automatically choosing the optimal number of partitions based on the size of your data warehouse.

I encourage customers to start with the default, then, if needed, further choose the number of partitions manually. Because the Actian Data Platform is architected for parallelism, running queries that give insights into how your data is distributed and then partitioning tables as needed allows you to operate efficiently with optimal performance.

For details on how to perform partitioning, including examples, graphics, and code, join the Actian community and view my article on partitioning best practices. You can learn everything you need to know about partitioning on the Actian Data Platform in just 15 minutes.

About Colm Ginty

Colm Ginty is a Customer Success Engineer at Actian, committed to helping businesses maximize value from the Actian Data Platform. With 8 years as a Data Engineer specializing in distributed systems like Spark and Kafka, Colm brings hands-on expertise in real-time data processing. He has presented case studies at data engineering meetups, focusing on system scalability and cost optimization. On the Actian blog, Colm writes about deployment best practices, performance tuning, and big data architectures. Check out his latest articles for practical guidance.

Data Management

Common Healthcare Data Management Issues and Solutions

Scott Norris

December 12, 2023

Summary

This blog addresses prevalent data management challenges in healthcare, emphasizing the need for modern solutions to ensure data quality, compliance, and integration across various systems.

Data Silos and Shadow IT: Departments often create isolated data repositories, bypassing IT protocols, leading to disconnected and outdated information. Implementing scalable data platforms with user-friendly integration capabilities can unify data and promote a data-driven culture.
Integration and Quality Barriers: Legacy systems may lack interoperability, hindering seamless data sharing. Adopting modern platforms that automate data profiling and ensure quality can provide comprehensive patient records and support data analytics.
Regulatory Compliance Challenges: Healthcare data is subject to strict regulations like HIPAA. Utilizing compliant data management technologies, role-based access controls, and encryption can protect patient data and maintain compliance.

A modern data management strategy treats data as a valuable business resource. That’s because data should be managed from creation to the point when it’s no longer needed in order to support and grow the business. Data management entails collecting, organizing, and securely storing data in a way that makes it easily accessible to everyone who needs it. As organizations create, ingest, and analyze more data than ever before, especially in the healthcare field, data management strategies are essential for getting the most value from data.

Making data management processes scalable is also critical, as data volumes and the number of data sources continue to rapidly increase. Unfortunately, many organizations struggle with data management problems, such as silos that result in outdated and untrustworthy data, legacy systems that can’t easily scale, and data integration and quality issues that create barriers to using data.

When these challenges enter the healthcare industry, the impact can be significant, immediate, and costly. That’s because data volumes in healthcare are enormous and growing at a fast rate. As a result, even minor issues with data management can become major problems as processes are scaled to handle massive data volumes.

Data management best practices are essential in healthcare to ensure compliance, enable data-driven outcomes, and handle data from a myriad of sources. The data can be connected, managed, and analyzed to improve patient outcomes and lower medical costs. Here are common data management issues in healthcare—and how to solve them:

Data Silos Are an Ongoing Problem

Healthcare data comes from a variety of sources, including patient healthcare records, medical notes and images, insurance companies, financial departments, operations, and more. Without proper data management processes in place, harnessing this data can get very complex, very fast.

Complexity often leads to data silos and shadow IT approaches. This happens when departments or individuals want to quickly access data, but don’t want to follow established protocols that could require IT help, so they take shortcuts. This results in islands of data that are not connected and may be outdated, inaccurate, or have other quality issues.

Breaking down silos and connecting data requires the right data platform. The platform should be scalable, have easy-to-use integration capabilities to unify data, and make data easy-to-access, without IT assistance. Making data easy discourages silos, fosters a data-driven culture that supports data management best practices, and allows all users to tap into the data they need.

Barriers to Data Integration and Quality

Many legacy systems used by healthcare organizations are not integration-friendly. They may have been built as a single-purpose solution and interoperability was not a primary concern. In today’s healthcare environment, connectivity is important to enable data sharing, automation, and visibility into the organization.

“The flow of data is as important as the flow of people,” according to FQHC Associates, which specializes in Federally Qualified Health Center (FQHC) programs. “One common issue in connected care is a lack of data standardization, in which the different platforms used by different departments are not mutually readable or easily transferable. This results in data silos, blocks productivity, and even worse, leads to misunderstandings or errors.”

Data integration—bringing together all required data from all available sources—on a single platform helps inform decision-making, delivers complete patient records, and enables healthcare data analytics. The Centers for Medicare & Medicaid Services (CMS) has mandates to prioritize interoperability—the ability for systems to “speak” to each other.

A modern platform is needed that offers simple integration and ensures data quality to give stakeholders confidence in their data. The platform must be able to integrate all needed data from anywhere, automate data profiling, and drive data quality for trusted results. Ensuring the accuracy, completeness, and consistency of healthcare data helps prevent problems, such as misdiagnosis or billing errors.

Complying With Ever-Changing Regulations

The healthcare industry is highly regulated, which requires data to be secure and meet compliance mandates. For example, patient data is sensitive and must meet regulations, such as the Health Insurance Portability and Accountability Act (HIPAA).

Non-compliance can result in stiff legal and financial penalties and loss of patient trust. Protecting patient data from breaches and unauthorized access is a constant concern, yet making data readily available to physicians when treating a patient is a must.

Regulations can be complex, vary by state, and continually evolve. This challenges healthcare organizations to ensure their data management plan is regularly updated to meet changing requirements. Implementing role-based access controls to view data, using HIPAA-compliant data management technologies, and encrypting data help with patient privacy and protection.

Similarly, data governance best practices can be used to establish clear governance policies. Best practices help ensure data is accurate, protected, and compliant. Healthcare organizations need a modern data platform capable of offering transparency into data processes to ensure they are compliant. Automating data management tasks removes the risk of human errors, while also accelerating processes.

Dealing With Duplicate Patient Records

The healthcare industry’s shift from paper-based patient records to electronic health records enabled organizations to modernize and benefit from a digital transformation. But this advancement came with a challenge—how to link a person’s data together in the same record. Too often, healthcare facilities have multiple records for the same patients due to name or address changes, errors when entering data, system migrations, healthcare mergers, or other reasons.

“One of the main challenges of healthcare data management is the complexity of managing and maintaining patient, consumer, and provider identities across the enterprise and beyond, especially as your organization grows organically and through partnerships and acquisition,” according to an article by MedCity News.

This problem increases data management complexity by having duplicate records for the same patients. Performing data cleansing can detect duplicate records and reconcile issues. Likewise, having a robust data quality management framework helps prevent the problem from occurring by establishing data processes and identifying tools that support data quality.

Delivering Trust in Healthcare Data

Many healthcare organizations struggle to optimize the full value of their data, due to a lack of data standards, poor data quality, data security issues, and ongoing delays in data delivery. All of these challenges reduce trust in data and create barriers to being a truly data-driven healthcare company.

Solving these issues and addressing common data management problems in healthcare requires a combination of technology solutions, data governance policies, and staff training. An easy-to-use data platform that solves issues for data scientists, managers, IT leaders, and others in healthcare organizations can help with data management, data visualization, and data accessibility.

For example, the Actian Data Platform gives users complete confidence in their data, improves data quality, and offers enhanced decision-making capabilities. It enables healthcare providers to:

Connect data sources. Integrate and transform data by building or using existing APIs via easy-to-use, drag-and-drop blocks for self-service, removing the need to use intricate programming or coding languages.
Connect to multiple applications. Create connections to applications offering a REST or SOAP API.
Broaden access to data. Use no-code, low-code, and pro-code integration and transformation options to broaden usability across the business.
Simplify data profiling. Profile data to identify data characteristics and anomalies, assess data quality, and determine data preparation needs for standardization.
Improve data quality. Track data quality over time and apply rules to existing integrations to quickly identify and isolate data inconsistencies.‌

Actian offers a modern integration solution that handles multiple integration types, allowing organizations to benefit from the explosion of new and emerging data sources and have the scalability to handle growing data volumes. In addition, the Actian Data Platform is easy to use, allowing stakeholders across the organizations to truly understand their data, ensure HIPAA compliance, and drive desired outcomes faster.

Find out how the platform manages data seamlessly and supports advanced use cases such as generative AI by automating time-consuming data preparation tasks.

Additional Resources:

About Scott Norris

Data Intelligence

3 AI Trends Identified by Gartner to Look Out for in 2024

Actian Corporation

December 11, 2023

Analyzing Digital Data Copy Space Statistics, Financial Chart, Economy

Gartner is the world’s leading data research and advisory firm. At the Gartner Data & Analytics Summit 2023, the firm shared its vision of the key trends likely to impact and shape the future of Data Science and Machine Learning. Here’s a look back at the 3 AI trends to watch for your business in 2024.

At its Data & Analytics Summit in Sydney this past summer, Gartner outlined the key trends that will influence the future of data science and machine learning (DSML). At a time when many industries are being impacted by the explosion in the use of AI in business, the firm highlights the growing importance of data in artificial intelligence which is embarking on a path that is both more ethical and more responsible.

Trend #1: Edge AI as a Promise of Responsiveness

One of the Gartner 2024 trends is Edge AI. It enables calculations to be carried out close to where the data is collected, eliminating the need for a centralized Cloud Computing center or external data center. This promotes making intelligent decisions more quickly, without the need to connect to the Cloud or remote data centers. By enabling faster execution of AI algorithms, latency is reduced and systems are more responsive.

Edge AI applies to IoTs, taking advantage of available local computing power. This approach is crucial for applications requiring real-time decision-making, such as autonomous driving or smart medical devices. Edge AI also offers advantages in terms of data confidentiality and security. Indeed, because certain sensitive information can be processed locally without being transmitted to remote servers, this eliminates unnecessary data exposure to external threats.

This convergence of AI and edge computing paves the way for solutions that are not only more efficient but also more responsible, as they are potentially more energy-efficient. According to forecasts by the Gartner Institute, more than 55% of all data analysis performed by deep neural networks will take place at the point of capture in an Edge system by 2025, compared to less than 10% in 2021!

Trend #2: Responsible AI as an Ethical Promise

Gartner highlights the key role of Responsible AI in its AI trend forecast for 2024. This set of principles and practices aims to ensure that AI is used ethically and responsibly. It addresses the social, environmental, and economic impact of AI, and aims to minimize risks and maximize benefits.

In technological terms, Responsible AI translates into a series of measures aimed at improving the transparency, reliability, and safety of AI systems. The key focus is on data and algorithm transparency. This enables users to understand how AI systems work, and to detect any misappropriated biases so that data can be used in a virtuous and respectful way. The second major area is the reliability of AI systems, whose robustness must be guaranteed, even under complex conditions or in the event of computer attacks. Thus, AI systems must be secure to protect personal data and sensitive information.

According to the Gartner Institute, “Responsible AI makes AI a positive force rather than a threat to society and itself”. To achieve this, the advice is simple: adopt a risk-proportionate approach to bringing value to AI, while exercising extreme caution when applying solutions and models.

Trend #3: Data-Centric AI as a Promise of Relevance

Gartner’s third major AI trend for 2024 highlights the centrality of data in the mass adoption of AI. Artificial intelligence is based on algorithms, which determine its relevance and performance. But rather than focusing solely on algorithms, data-centric AI focuses more on the quality, diversity, and governance of data. The aim is to improve model accuracy by relying on rich, perfectly maintained data sets.

For companies, data-centric AI promises better customer understanding, more informed decision-making, and more robust innovations. By focusing on data quality, organizations can increase the effectiveness of their AI initiatives, reduce algorithmic biases, and boost user confidence. In doing so, data-centric AI offers a more reliable and sustainable way of harnessing the potential of artificial intelligence. According to Gartner forecasts, by 2024, 60% of AI data will be used to simulate reality, identify future scenarios, and reduce the risk of AI errors, compared with just 1% in 2021!

Between performance, ethics, compliance, safety, and responsibility, the AI 2024 roadmap is ambitious. Will you rise to the challenge?

The Top Priorities & Challenges for CDOs in 2024

Generative AI, an Upcoming Trend?

Enthusiastic Approach to the Potential of Generative AI

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

Data Governance is Still a Priority

Companies are Changing Approaches to Data Governance

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

Visible Business Value Creation

Analytics and AI in Project Development

Towards a Data Product Approach

In Conclusion

About Actian Corporation

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Product Recap: A Look Back at 2023

Decrease Data Search and Discovery Time

A Fresh New Look for Actian Explorer

New Homepage

New Item Detail Pages

New Filtering System

Easily Browsing the Catalog by Topic

Alternative Names for Glossary Items for Better Discovery

Improved Search Performance

Microsoft Teams Integration

Increase Data Steward Productivity & Efficiency

Automated Datasets Import

Orphan Fields Deletion

Building Reports Based on the Content of the Catalog

New Look for the Steward Dashboard

Data Sampling on Datasets

Powerful Lineage Capabilities

Data Quality Information on Datasets

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

Catalog Management APIs

Property & Responsibility Codes Management

More Than a Dozen More Connectors to the List

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How to Build an Effective Data Management Project Plan

Essential Components of a Data Management Plan

Keep a Strong Focus on Data Quality

Data Champions Should Own the Data Plan

Communicate Policies and Share the Plan

About Scott Norris

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Top Capabilities to Look for in Database Management Tools

Data Management Tools for Modern Use Cases

Ease-of-Use and Integration Are Key

Expanding Data Management Use Cases

Delivering Data and Analytics Capabilities for Today’s Users

About Derek Comingore

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The Actian Data Platform’s Superior Price-Performance

The Importance of Prioritizing Optimal Price-Performance

Superior Price-Performance

The 2023 Gigaom TCP-H Benchmark Test

Let Actian Help

About Phil Ostroff

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Do You Have a Data Quality Framework?

Establish Quality Standards for Your Use Cases

Key Components of a Data Quality Framework

Establish Responsibilities and Metrics

Expert Help to Ensure Quality Data

About Emma McGrattan