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C-Suite & Data: Close the Trust Gap with Better Data Management

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise technology. In this feature, Syniti CEO Kevin Campbell offers commentary on the C-suite and data, and how enterprises can close the “trust gap” with better data management.

Organizations have thought of data as the sole purview of IT rather than the true business driver it is for far too long. Fortunately, mindsets are evolving. Business leaders have learned that data must be considered as a crucial element of success and a strategic advantage due to its ability to drive value.

Data management is becoming a C-suite priority for many businesses, according to worldwide study conducted earlier this year by HFS Research, which revealed that 46 percent of participants said their CEOs are establishing the data objectives. Even though it’s encouraging to see that data has moved up to the C-suite agenda, the same study revealed a significant gap between perceived data trust and actual operationalizing of data across the business. Executive participants revealed that 60 percent or less of their organization’s data is useful, despite the fact that 80 percent of them claimed they trust that data. This reveals a significant disparity between accepting the data and really applying it to influence important business outcomes.

What’s the root of the disconnect if trusting the data doesn’t match operational realities? And how can this be fixed?

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C-Suite Data Management

Data Quality Reigns Supreme

Although the C-suite obviously has data as a top priority, organizational execution gaps still exist, as the HFS report demonstrates. Most study respondents blame a dearth of standard data governance and monitoring for the difficulty they have accessing and acting on the data, despite large investments in data management techniques and tools.

While it’s true that poor governance limits better use of the data, the data’s quality also a concern. And that’s not the same as “trusting.” If their data quality was improved, 95 percent of executives agreed, their businesses would be more innovative, competitive and able to act faster. Over 25 percent of those surveyed think inaccurate data caused their company to lose 20 percent or more of its revenue. Those beliefs are founded in truth; Gartner estimates that poor quality data costs organizations an average of $12.9 million while other analysts put that number even higher. The bottom line: it’s expensive and the problem will only get worse.

As technologies like generative AI gain traction, data quality is becoming even more important. These tools are guided by large learning models (LLMs), which are “fed” by data from different sources. If that data is poor quality, it will lead to output that’s flawed or outright inaccurate. These are data-driven technologies that require quality input data in order to be successful. There’s no way around it.

Blazing a Trail Toward Usable Data

When discussing usability, it’s helpful to recall Mark Twain’s adage that “Data is like garbage. You’d better know what you are going to do with it before you collect it.” There is simply a mountain of information to gather, yet not all of it will be significant or helpful to you. To achieve your intended business outcomes and goals, you must prioritize.

That’s one of the major problems many enterprises are currently facing, and it has to do with data governance. Governance supports your broader data management approach; the two go hand-in-hand.

While centralized company-wide data management was reportedly put in place by 73% of respondents, it wasn’t immediately obvious how that governance is set up. A centralized governance system that supports numerous functions is used by 44% of leaders. Self-service was favored by 14% of respondents, but a startling 19% indicated they were still developing an approach.

Centralized governance is lacking in 56% of businesses. Therefore, consistent governance techniques are not always in line with the goals of centralized data management systems, which may help explain the discrepancy between data usability and data trust. Consequently, we must consider this when evaluating people’s opinions of usable or good data.

According to the HFS study, a lack of taxonomy and a lack of governance have the largest negative effects on data quality. Using governance, data can be gathered, managed and supported in a clear way. This will help validate that your data is useful, if you are serious about data quality, because you will have set up usage guidelines. Data quality solutions are important levers because they can aid in boosting the volume of consumable or usable data that passes through the data cycle and generates the necessary business benefits. Additionally, you can reduce IT expenditures by implementing centralized systems and policies.

Data Management Done Right

Enterprises commonly see data management as an internal technological issue. Organizations frequently blame bad data on a lack of control and oversight. The opportunity therefore lies in a more thorough understanding of data and more effective ways to implement change so that data is aligned with company objectives.

Consider these three factors to maximize the return on all of those investments:

  • Your guiding light must be the support and enablement of business results. Your data management objectives must be distinct and understandable. Ensure that line-of-business and IT are collaborating to achieve the same objectives. IT and business must work together to operationalize data, with an emphasis on business goals rather than technology capabilities.
  • Ensure that effective change management and cultural change happen; this will boost the proportion of valuable data. Because managing data is essentially a technological challenge for most companies, concentrate on cultural transformation. Focus on governance and monitoring to better understand the data cycle.
  • The broader market has invested in tools for data management and data quality. Though understanding of the benefits is still vague, it is even less evident how they relate to data management regulations. Therefore, maximizing the use of investments and preventing solutions from going unused are crucial elements of better value capture.

Closing the Data Gap

Executives are prioritizing data and its management, yet issues still exist. According to research, enterprise faith in data is high, possibly unrealistically so. However, the majority of those polled also felt that a sizable portion of the data is useless. To achieve its objective—business advantage—data quality must be high. To support the entire data management plan, good data governance must also be in place.

Take into account all of these difficulties before starting a data project, and then apply the suggestions above to lay a solid foundation. This will help you create a reliable program that eliminates the gap between usability and trust and makes data a business driver.

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