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When it comes to data quality, don’t forget about staffing

Erin Haselkorn Data quality

The old saying “information is power” could not be truer today. The information we house in our databases is used to do everything: help us better understand customers, provide a better customer experience, improve operational efficiency, and ensure more informed business decisions.

The desire for data and data-driven insight has proliferated every industry—and that drive for data puts data scientists and data managers in a great position. They are helping to influence some of the most strategic decisions and operations across the business. However, with that increased importance also comes new challenges. There is far more pressure for information to be accurate, consolidated, and complete. Otherwise, insight will either take too long to obtain or will be inaccurate. The lack of expertise in the market and resources for staffing is creating gaps in data insight.

Current staffing for data management

Most data practitioners are not operating in an environment that reflects the growing need for data insight. Information is stored across multiple data systems that may vary by department or business unit. In addition, data management processes and technology can vary greatly across each system. The challenge is that many of today’s data initiatives are siloed within one department, but they affect the entire organization.

The staffing structure around data is no different. Our research shows just 35 percent of global companies say information is reviewed and maintained centrally by a single director. We also found that 63 percent of organizations lack a coherent, centralized approach to their data quality strategy. More commonly, companies report that there is some centralization, but that many departments still adopt their own data quality strategy.

More companies are starting to have centralized practices, but it is moving very slowly. Our research shows only 29 percent of companies are taking a holistic approach to data management. While there will always be manipulation of the data on a departmental level, central ownership is needed in terms of governance and master data management.

This centralization is also timing with the proliferation of the chief data officer (CDO). For companies that are successful in being data-driven, there’s a reported investment in data practitioners—especially a CDO—and a maturity in terms of data quality practices. These organizations are also more likely to see data management as a continuous process rather than a one-off project.

There is certainly a case for adding a CDO to the organization, especially considering the value of data and the benefit of having someone to take responsibility for the quality, standards, meaning, security, metrics, integration, and coordination of data among the various divisions. This role is really designed to bridge the gap between technology and business needs, which is greatly needed in today’s data environment. This role will continue to become more prevalent over the next several years as an increased degree of centralization is needed.

A lack of centralization causes problems

A lack of central governance is creating a high degree of inaccurate information. In fact, 65 percent of businesses report inaccurate data undermines key initiatives, and on average, businesses report nearly a third of their total data might be inaccurate.

That high degree of inaccurate information has negative impacts on organizations that are hungry for data-driven insight. It affects their customer service, operational efficiency, market intelligence, and even their bottom line. Sixty-nine percent of businesses we surveyed reported despite multiple ongoing data initiatives, their organization struggles to be data-driven.

Despite being an area where companies have traditionally underinvested, ninety-three percent of businesses report progress with data quality in the last 12 months.

Companies are investing in technology, but they are lacking investment in strategy and people. A lack of centralization is causing organizations to invest in departmental silos and have different types of data management tools and data governance strategies across the organization.

Advancing the data agenda

Investing in the right data management organization is critical for data insight. Having a central data owner, like a CDO, supported by data stewards, data scientists, business analysts, information architects, etc., will have a dramatic impact on ensuring information is fit for purpose across the organization.

Consistent data processes and data management tools can be implemented under a single owner, enabling information to be consistently maintained, standardized, and validated across the organization.

Central strategies promote proactive techniques, enabling organizations to understand what common problems that occur and allows for root cause analysis on how those errors can be fixed. It also allows the business to more strategically invest in technology that ensures consistency across the organization.

Investing in staff is so important that it even affects the bottom line. An Experian study revealed that more companies who have enjoyed a significant increase in profits in the last 12 months manage their data quality strategy in a centralized way with ownership resting with a single director.

As you approach data insight this year, it is very important that time and energy is spent thinking about staffing appropriately. While departmental manipulation of data is very important and business users will increasingly want to access data-as-a-service, central data management groups need to be put in place to ensure the right governance and quality exists over the data.

For more information, be sure to download our research.

Download the research