In traditional data warehouses, specific types of data are stored using a predefined database structure. Due to this “schema on write” approach, prior to all data sources being consolidated into one warehouse, there needs to be a significant transformation effort. From there, data lakes emerge!

“More than 15 billion IoT devices will connect to the enterprise infrastructure by 2029.” Finding data is not going to be a challenge, clearly, but taking advantage of it all to drive business outcomes will be. Combining AI and machine learning (ML) with data collection and processing capabilities of the edge and the cloud may hold the answer.

For all of the buzz surrounding both artificial intelligence and data-driven management, many companies have seen mixed results in their quest to harness the value of enterprise data. To avoid those pitfalls, we mixed best-of-breed and proprietary solutions to develop our enterprise data platform (EDP), focusing much of our attention on a combination of smart changes in technology, culture and process for data lakes.

Data paves the way for every strategic move made by banks and insurance companies. Whether looking to create a new service, complying with regulations, or overhauling and re-engineering legacy operations, a massive data project is always central to the effort. For financial services businesses, the pace at which they can reshape and repurpose data has become a key determinant of their ability to predict market trends and meet client expectations.

Co-authored by Samta Bansal More than two million British people use Open Banking-based products today, a number that, despite the disruption of COVID-19, is double that seen at the start of 2020. No wonder International Banker dubbed 2021 “The Year of Open Banking.” As open banking spreads worldwide, its growth comes with the pressing need to drive standardization that bridges geographic boundaries and regulatory frameworks.

The application landscape is swiftly moving from monolithic architectures towards next-gen applications running on microservices. It’s all about fast delivery of applications and services to stay competitive in the market. Making this change gives businesses the agility and flexibility to quickly launch innovative new digital services.

In financial services, data is essential for storing product information, capturing customer details, processing transactions and keeping records of accounts; the relationship between products and their underlying data has always been symbiotic. A significant amount of data infrastructure is static, fragmented across data silos or based on legacy platforms. This has created an impedance mismatch between products and the underlying data.

Enterprises are quickly evolving from a posture that approached the cloud as a kind of playground to one that goes all in to achieve cloud-first, cloud-native IT. With this transition from free-for-all to mature-business-service architecture, usually involving multiple public cloud providers, comes the need to answer some thorny questions. It’s no longer sufficient to endlessly pile on additional cloud services to a growing hybrid or distributed cloud infrastructure.

The thing about data is there’s no end to how much of it you can collect and keep. Each day, if you’re like most of the global banks I work with, you’re generating oceans of the stuff. Yet the bulk of it will never be very helpful or even relevant to your day-to-day business decisions.