Here is a brief excerpt from an article written by Sven Blumberg, Oliver Bossert, Hagen Grabenhorst, and Henning Soller for the McKinsey Quarterly, published by McKinsey & Company. To read the complete article, check out other resources, learn more about the firm, obtain subscription information, and register to receive email alerts, please click here.
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Companies that succeed at meeting their analytics objectives let business goals drive the technology. Here’s how they structure a data architecture that works.
Data architecture has been consistently identified by CXOs as a top challenge to preparing for digitizing business. Leveraging our experience across industries, we have consistently found that the difference between companies that use data effectively and those that do not—that is, between leaders and laggards—translates to a 1 percent margin improvement for leaders. In the apparel sector, for instance, data-driven companies have doubled their EBIT margin as compared to their more traditional peers.
Using data effectively requires the right data architecture, built on a foundation of business requirements. However, most companies take a technology-first approach, building major platforms while focusing too little on killer use cases. Many businesses, seeing digital opportunities (and digital competition) in their sectors, rush to invest without a considered, holistic data strategy. They either focus on the technologies alone or address immediate, distinct use cases without considering the mid- to long-term creation of sustainable capabilities. This goes some way toward explaining why a 2017 McKinsey Global Survey found that only half of responding executives report even moderate effectiveness at meeting their analytics objectives. The survey found the second-largest challenge companies face (after constructing a strategy to pursue data and analytics) is designing data architecture and technology infrastructure that effectively support data-and-analytics activities at scale. We found that eight out of ten companies embark on digital data enablement by making their IT departments responsible for the data transformation—with very grand implementation programs—and a small set of business use cases.
This strategy is quite different from that employed by next-generation digital leaders, who typically embark on transformation from a business perspective and implement supporting technologies as needed. Doing the technology first produces more problems than successes, including:
o Redundant and inconsistent data storage. Only two in ten banks we’ve worked with have established a common enterprise data warehouse, which is essential for creating a single source of truth for financial and customer data.
o Overlapping functionality. Every bank we’ve worked with has at least one business function supported by three different technological systems.
o A lack of sustainability. The solutions at which financial institutions typically arrive are often quick fixes that ignore the enterprises’ larger aspirations for datafication. For example, one insurance company extracted and replicated data from its warehouse each time it was needed rather than building data architecture that would allow it to store each customer element only once, thereby reducing costs and eliminating inefficiencies.
These problems have real business consequences. Meeting leading-edge business requirements, such as real-time customer and decision support, and large-scale analytics requires the integration of traditional data warehousing with new technologies.
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Here is a direct link to the complete article.
Sven Blumberg is a partner in McKinsey’s Düsseldorf office, and Oliver Bossert is a senior expert in the Frankfurt office, where is a consultant and Henning Soller is an associate partner.