Issue link: https://read.uberflip.com/i/1407728
A STRATEGIC PLAYBOOK FOR DATA, ANALYTICS, AND MACHINE LEARNING Organizations that continue to struggle with legacy infrastructure likely face one or more well-known challenges involving people, process, and technology. Becoming a data-driven organization requires new thinking about all three. Traditional data analytics architectures rooted in struc- tured databases and relational data warehouses have grown costly to manage, update, and secure. These limitations prevent enterprises from fully monetizing valuable data. Legacy architecture wasn't built to sup- port enterprise-wide data management and analytics at the scale required to handle petabytes or even exabytes of data across the enterprise. Most CIOs recognize the limits of legacy data infrastruc- ture—but may hesitate to push their organization out of its technological comfort zone. Such a short-term perspective could inhibit long-term business growth. "Don't let familiarity turn into a blind spot that stifles innovation," says Bice. INNOVATION SPOTLIGHT Amazon.com Amazon.com once maintained one of the biggest Oracle data warehouses in the world, but it wasn't enough to keep pace with Amazon's growth. "Five years ago, our ability to grow and analyze our business was limited by technology choice," says Jeff Carter, Vice President of Data, Analytics, and Machine Learning at Amazon. Amazon made the strategic decision to move off the legacy data warehouse and onto a cloud-native data lake architecture comprising a variety of AWS services. The migration involved moving 50 petabytes of data, 75,000-plus data warehouse tables, and 7,500 OLTP databases supporting the company's business-critical ordering, processing, and fulfillment systems. Carter admits to some initial concerns about such a massive migration causing disruptions. "We didn't want to be the team that shut down the business," he says. "But what we found was in pretty much every instance, availability was better." "By migrating to the AWS technologies and implementing the data lake, we have been able to scale to meet our business needs," says Carter, adding that the new environ- ment is about 30-50% less expensive to maintain than the previous architecture. Limitations of monolithic architectures u t 4