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IBV study on analytics, part six: Data management practices and realizing value from data and analytics

December 30, 2013

This is part six of our series on the findings and text from IBM Institute for Business Value’s latest study and paper: “Analytics: A blueprint for value - Converting big data and analytics insights into results,” from my colleagues Fred Balboni, Glenn Finch, Cathy Rodenbeck Reese and Rebecca Shockley.

In part five, we looked at the second level of impact, Drive. We examined Drive's first lever, Culture, and its impact on the availability and use of analytics. In this part, we will look at the next Drive lever: Data. We will also explore what is needed to realize value from data and analytics by examining data management practices.

Decision makers must have confidence in the data before they will use it to guide their actions. In organizations deriving the greatest value from analytics, the governance and security are sufficient to provide most users with a comfortable level of trust, yet flexible enough to allow business users to meet a diverse set of requirements. Furthermore, our research indicates that organizations with poor data management activities will continue to struggle to create value from data and analytics.

Leaders are confident about the data within their organizations. Two-thirds of the respondents from Leader organizations believe in the quality of the data and analytics available to them enough to use it in their day-to-day decision making processes (see Figure 8).

Figure 8 for RP post 6.jpg

To create this level of confidence, Leaders use a rigorous system of enterprise-level standards and strong data management practices to help ensure not only the timeliness and quality of the data, but its security and privacy. Leaders take a structured approach to data governance and security, and this vigilance is largely credited for the higher degree of trust most have about the data and analytics within their organizations.

More than half of Leaders (57 percent) have enterprise-level standards, policies and practices in place to integrate data across the organization. These standards cover data management practices from intake to transfers, data storage processes for static and streaming data, and metadata management to ensure data traceability and enterprise data definitions.

To protect this data, one out of five Leaders (20 percent) implement strict internal standards and a secure infrastructure for the collection, storage and use of all types of data and analytics, while another 45 percent have relatively strong systems in place to protect sensitive data through the use of such practices as enterprise standards, policies and role-based access.

Understanding the importance of a strong data management strategy, one European social services agency sought to better govern and secure its data to improve service to citizens. The government agency realized its fragmented approach to data was causing its 18 million beneficiaries in 11 million households to have to repeat data previously provided to the organization each time a citizen applied for a different benefit. Not only was it frustrating to the beneficiaries, with little sharing of data between branches and a lack of up-to-date data, it was administratively inefficient.

Executives set out to integrate their data and improve data quality as a means to provide consistent information to citizens, case workers and providers across multiple programs.  As a result, the agency has increased its effectiveness in providing the right services to eligible recipients and increased productivity among its agents by 35 percent.

In part seven, we will look at Trust (the final lever in the Drive level of impact) and examine how organizational confidence directly impacts an organization’s ability to create value from analytics.

Catch up on the entire series so far with parts one through five:

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