Why You Need a Modern Data Architecture

Data & analytics are at the core of the digital transformation. Making sure that the information democracy is advocated across the enterprise has become an imperative. The marketplace for data & analytics solution providers is undergoing a rapid and profound transition as the supporting technologies are also changing. Cloud is becoming the standard choice more and more and pricing models are under pressure from both open-source as well as cloud providers. Successful implementations of digital platforms remain inevitable, but how to build a data architecture that works? How to modernize to level up?

Let the Business Lead with Purpose

My experiences learned me that companies that succeed at meeting their analytics objectives let business goals drive the technology and not vice versa. Data architecture has been consistently identified by IT management as a top challenge to preparing for digitizing business. In 2017 McKinsey already calculated that the difference between companies that use data effectively and those that do not translates to a 1 percent margin improvement for leaders compared to the laggards. 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, many to most companies take a technology-first approach, building major platforms while focusing too little on killer use cases. Many businesses, seeing digital opportunities as well as digital competition in their sectors, rush to invest without a considered, holistic data strategy. But putting together a data architecture design and the necessary technology infrastructure to effectively support data & analytics activities at scale is a tough cookie and usually only based on a small set of business use cases. Doing the technology first produces more problems than successes, including:

  • Redundant and inconsistent data storage
  • Overlapping functionality
  • A lack of sustainability

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. Meeting leading edge business requirements and large-scale analytics requires the integration of traditional data warehousing with new technologies. With all do respect but the design of such an architecture is not a job for the average enterprise architect. Believe me, I’ve seen more of them nearly choking to death than delivering a solid data architecture.

The Future of Data Infrastructure in Digital Enterprise

As a consultant I’m able to snoop around at many customers and despite the clamoured demise of the traditional data warehouse it still serves as the basis for analytics solutions and remains foundational. However, increased demand for new data types and new use cases continues to expand. Data architectures need to evolve in order to meet these demands in both distributed and centralized solutions. This often means adding new technologies like Hadoop a.o. Advanced architectures like the logical data warehouse help make this a reality. Key questions for the Data Architect are:

  • How to puzzle the pieces when it comes down to hubs, lakes and warehouses?
  • How do we balance the trade-offs between all the options?
  • What are the technology options and how can we integrate them?

There’s a big chance that also your organizations’ data culture is changing. You may need DataOps! More about that in a next article but should you have questions already, do not hesitate to reach out.