It has been road-tested in hundreds of IT and data transformations across industries, and we have observed its ability to reduce costs for traditional AI use cases and enable faster time to market and better reusability of new AI initiatives. The past few years have seen the emergence of a reference data architecture that provides the agility to meet today’s need for speed, flexibility, and innovation (Exhibit 1). Take advantage of a road-tested blueprintĭata and technology leaders no longer need to start from scratch when designing a data architecture. Their work offers a proven formula for those still struggling to get their efforts on track and give their company a competitive edge. This article shares five practices that leading organizations use to accelerate their modernization efforts and deliver value faster. The good news is that data and technology leaders can break this gridlock by rethinking how they approach modernization efforts.
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Amid it all, business leaders wonder what value they’re getting from these efforts.
Once organizations have a plan and are ready to implement, their efforts are often stymied as teams struggle to bring these behemoth blueprints to life and put changes into production. Traditional architecture design and evaluation approaches may paralyze progress as organizations overplan and overinvest in developing road-map designs and spend months on technology assessments and vendor comparisons that often go off the rails as stakeholders debate the right path in this rapidly evolving landscape. But often, we find the culprit is not technical complexity it’s process complexity. All of this can create data-quality issues, which add complexity and cost to AI development processes, and suppress the delivery of new capabilities.Ĭertainly, technology changes are not easy. The majority have integrated less than 25 percent of their critical data in the target architecture. For example, in banking, while 70 percent of financial institutions we surveyed have had a modern data-architecture road map for 18 to 24 months, almost half still have disparate data models. Leading AI adopters (those that attribute 20 percent or more of their organizations’ earnings before interest and taxes to AI) are investing even more in AI in response to the pandemic and the ensuing acceleration of digital.ĭespite the urgent call for modernization, we have seen few companies successfully making the foundational shifts necessary to drive innovation. In just two months, digital adoption vaulted five years forward amid the COVID-19 crisis. For today’s data and technology leaders, the pressure is mounting to create a modern data architecture that fully fuels their company’s digital and artificial intelligence (AI) transformations.
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