Data quality staffing critical to information success

The old saying ‘information is power’ could not be more true today. The information we house in our databases is used to do everything from getting a better understanding of our customer, providing a better customer experience, improving operational efficiency and ensuring more informed business decisions.

The desire for data and data-driven insight has proliferated across our businesses. In fact, the concept has become so important that 97 percent of U.S. companies feel driven to turn data into insight. Behind this drive are customers: Companies want to better understand their customer needs so they can find more of them and increase the value of existing clients.

All of 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 not siloed within one department, but they affect the entire organization.

The staffing structure around data is no different. According to a recent Experian Data Quality study, just 35 percent of global companies say information is reviewed and maintained centrally by a single director. The research 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. In the past year, there has been a slight increase in the number of companies with central ownership, but it is extremely modest considering the reliance organizations place on data. 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 CDO. Today, most of those organizations who manage data quality centrally either have it managed by the CDO, CIO or CTO.

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. Ninety-two percent of organizations suspect their customer and prospect data might be inaccurate in some way. The percentage of inaccurate data has actually been going up over the past several years. On average, respondents globally think 26 percent of their total data might be inaccurate. In the U.S., that average goes up to 32 percent.

That high degree of inaccurate information obviously has negative impacts on organizations that are hungry for data-driven insight. It affects their customer service, operational efficiency, market intelligence and even the bottom line.

All of these errors are not happening despite investment in data management. Eighty-eight percent of companies have some sort of data quality solution in place today. In addition, 84 percent of companies are looking to invest in some sort of data quality solution in the next year.

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 consistently across the organization.

Investing in staff is so important that it even affects the bottom line. A recent Experian Data Quality 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 in 2015, 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 2015 data quality benchmark report.

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