Here is a brief excerpt from an article written byBy 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:
- 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.
- Overlapping functionality. Every bank we’ve worked with has at least one business function supported by three different technological systems.
- 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.
The two-speed data-architecture imperative
Today, enterprises must cope with increasingly large and complex data volumes (worldwide, data storage doubles every two years) coming from diverse sources in a wide variety of formats that traditional data infrastructures struggle, and most often fail, to operationalize. Developing new business capabilities—such as individual pricing for customers based on real-time profitability, as some insurance companies have done, automating credit decisions that lead to improved outcomes for banks and greater customer satisfaction, or running automated, more cost-effective strategic marketing campaigns as we’ve seen in the chemicals sector—demands new ways of managing data.
This does not mean, however, that legacy data and IT infrastructures must be trashed, or that new capabilities need to be bolted on. It does mean that the traditional data warehouse, through which the organization gains stability and financial transparency, must be scaled down and integrated with the high-speed transactional architecture that gives the organization the capability to support new products and services (as well as real-time reporting). This is the two-speed principle.
This new, complex technical environment requires companies to closely examine business use cases before making costly technology decisions, such as needlessly ripping out and replacing legacy architectures. Instead, it is preferable to use a capability-oriented reconceptualization of data management as an enabler of digital applications and processes (Exhibit).
To implement an end-to-end digital data architecture, an enterprise needs first to develop a point of view on its current and, if possible, future business requirements, sketch its desired, flexible data-management architecture, and create a roadmap for implementation. To begin, one must identify the key business use cases.
To do this, we recommend a thorough review of best-practice use cases across industries that address common value drivers (financial transparency, customer satisfaction, rapid product development, real-time operational reporting, and so on). Then, the company should compare those use cases with its market position and strategic direction, prioritizing those that best reflect the company’s situation and aspirations. Once those reference use cases are identified, the company can begin to define target data-architecture capabilities. In this process, the business leads and technology follows.
The high-level structure in the exhibit above represents a layered data architecture that has been applied successfully by many organizations, across many industries, especially in finance. It extends to accommodate new digital capabilities such as collecting and analyzing unstructured data, enabling real-time data processing, and streaming analytics.
The exhibit shows a reference architecture that combines both the traditional requirements of financial transparency via a data warehouse and the capability to support advanced analytics and big data. In a phrase, it’s a two-speed approach.
The two-speed architecture adheres to three core principles:
- A limited number of components with a clear demarcation of capabilities to manage complexity while providing the required functionalities, such as advanced analytics and operational reporting
- Layers that enable the transparent management of data flows and provide a single source of truth to protect against silos and data inconsistencies (through the data warehouse, which models, integrates, and consolidates data from various sources)
- Integration of state-of-the-art solutions with traditional components, such as the data warehouse, to satisfy such new requirements as real-time processing, and an operational data store (ODS) based on new database technologies
We have used this model to:
- Help clients think through and evaluate their options on an architectural level before discussing concrete technical solutions.
- Map technology components against capabilities to manage and avoid redundancies while identifying gaps.
- Create plans for stepwise transformations driven by business value while limiting business disruption.
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Here is a direct link to the complete article.