Stop Wasting Data: Build a Future-Proof Strategy With Data Governance

These days, it’s hard to find a company that isn’t trying to better itself and become data-driven with advanced analytics, AI, or Generative AI. Enterprises across industries of all types are scrambling to integrate new, emerging technologies into their environments, hoping to get the much-vaunted promise of increased productivity and enhanced capabilities as quickly as they can.

What most organizations promptly discover as part of that process, however, is that getting their data assets organized in a way that will allow them to fully take advantage of these technologies is much harder than it first appears. The reasons are many – from a complex mix of data formats, data silos and data management tools, to uncertainty around how to best manage the process, the data preparation and organization – and collectively, these factors are fast becoming a major stumbling block for many companies.

In fact, according to a recent survey by TECHnalysis Research of IT decision makers in over 1,000 U.S. companies that are doing work with GenAI, Data Preparation and Integration is one of the top five challenges that companies face. In an interesting twist, in an Actian survey of 550 professionals (70% of which were director or higher) in 6 countries and 7 industries, 79% indicated that they believe they’re prepared for Gen AI. However, when Gartner asked people in charge of AI data readiness, only 4% said they were ready.

Another problem is that most organizations don’t have any organized governance plans for their data. That same TECHnalysis Research survey found that just under 30% of large enterprises (1,000+ employees) and a whopping 64% of medium businesses (100-999 employees) don’t have governance plans of any kind for their GenAI projects.

One key issue is that many people don’t fully understand what data governance is and why it’s important. On top of that, even organizations that have started to put together data governance policies and procedures don’t know best practices to ensure that they’re getting the right kind of data fed into their algorithms and large language models (LLMs). The net result is a large percentage of organizations aren’t using their critical data as effectively as they could be and that, in turn, typically translates into customized models and applications that aren’t as effective or as productive as they were expected to be. In fact, according to Gartner, by 2027, 60% of organizations will fail to realize the anticipated value of their AI use cases due to incohesive ethical governance frameworks.

To address these issues, companies clearly need not only a wide range of tools to best organize, manage and prepare their data for use, but also a framework and set of guidelines. This ensures that the most effective policies and procedures for acquiring and using data are in place to maximize the return on investment of implementing these emerging technologies.

That’s where a company like Actian, a division of HCL Software, comes into play. Actian currently offers a range of tools designed to organize and optimize a company’s data assets for a wide variety of innovative technologies, including GenAI. This is essential because, as many businesses have already discovered, the quality of the output that an application creates is utterly dependent on the quality of the data its underlying model is trained on. It’s a classic case of garbage in, garbage out—or more positively, high-quality data in, effective, impactful and trustworthy results out.

Actian’s suite of tools tackles everything from data organization to advanced analytics, all designed towards optimizing large volumes of data for ingestion. In particular, the company’s tools have a strong focus on metadata, which is quickly proving to be an essential part of the training process. Essentially, the more accurately and thoroughly an organization’s data can be documented or described via metadata, the more effectively a company can use that data in its training process for multiple applications, including GenAI. Plus, well-documented data can help reduce hallucinations and other misleading output that all LLMs are still occasionally prone to produce.

To help broaden its range of capabilities in these areas, Actian recently completed the purchase of Zeenea, a company that’s built a Data Discovery Platform centered around a Data Catalog. Zeenea’s Data Catalog lets companies organize all their various data assets into a single catalog structure that leverages metadata to create a single searchable repository. This, in turn, helps data consumers within an organization find the information they need via either simple text-based searches or a visual Knowledge Graph. The Knowledge Graph utilizes semantic metadata to link together numerous independent data sources and provide context and easy-to-see visual connections across these data assets.

The latest enhancement to these catalog capabilities is the company’s new Actian Zeenea Federated Data Catalog, which takes the data cataloging concept to a new level by integrating its capabilities across an organization. This federated catalog leverages a domain-oriented data management approach where the teams most familiar with the data manage their own data assets, permissions, and governance in a dedicated data catalog. Domains can then publish their most valuable data assets in a shared Enterprise Data Marketplace, ready to be consumed as products by all business functions within the organization. By using the same principles and concepts across these different domains, organizations end up with a decentralized, yet consistent data management structure that provides an easier and more effective method for sharing critical data. Most importantly, they do so in a manner that provides a consistent set of governance principles, helping them avoid potential regulatory and other data compliance issues.

In addition to these data preparation and organization tools, Zeenea also provides its customers with a tested, mature set of data governance solutions and a comprehensive data governance framework to ensure best practices can be leveraged across the data preparation process.

Zeenea’s effective Data Governance Framework provides a straightforward but comprehensive set of policies and procedures that can help ensure that organizations of different types and with different needs can all get the most from their data assets. More than just a list of rules to follow, the framework has suggestions on organizational structure, questions and topics to be addressed in meetings, strategies for implementing some of the key concepts, and more.

The framework is also designed to help ensure buy-in across an organization’s critical decision-makers—a key make or break point for many advanced projects—as well as offer important practical benefits. For example, a properly followed framework can help organizations stay within any regulatory and legal requirements to which they might be subject, avoid data-driven bias in the output results, prevent loss of critical IP, address any potential ethical issues, and much more.

There’s no question that new technologies, like GenAI and the new kinds of applications the technology is enabling, are opening up some amazing new potential for companies to improve their productivity, stay ahead of their competitors, enhance their bottom line, and become truly “data driven.” At the same time, as with many other new technologies, it’s also opening up the potential for new types of risks and challenges.

As a result, companies who are eager to jump into the new exciting applications of data, like GenAI, need to be certain that they’re well prepared for the adventure. Taken together, Actian’s set of data preparation tools along with Zeenea’s Data Discovery Platform, Federated Data Catalog and governance framework can help companies have a smooth, organized, and comprehensive data preparation process. Given how important this process is and how much it can impact the ultimate success or failure of advanced data initiatives, it’s clear that it’s a topic that organizations of all types and sizes need to get much smarter about now.

About Bob O'Donnell

Bob O’Donnell is the president and chief analyst of TECHnalysis Research, LLC, a market research firm that provides strategic consulting and market research services to the technology industry and professional financial community. You can follow him on Twitter @bobodtech.