Reducing the time it takes to respond to data requests from the supervisor can generate cost savings in the order of 30 to 40 percent, for instance. In our experience, most of the value of a data transformation flows from improved regulatory compliance, lower costs, and higher revenues. In setting this ambition, institutions should take note of the scale of improvement other organizations have achieved. Any successful data transformation begins by setting a clear ambition for the value it expects to create. Others had embarked on ambitious programs to develop a new enterprise data warehouse or data lake without an explicit data strategy, with predictably disappointing results. Obvious though this step may seem, only about 30 percent of the banks in our survey had a data strategy in place. If you would like information about this content we will be happy to work with you. We strive to provide individuals with disabilities equal access to our website. To tackle these obstacles, smart institutions follow a systematic five-step process to data transformation. Yet many other organizations are struggling to capture real value from their data programs, with some seeing scant returns from investments totaling hundreds of millions of dollars.Ī 2016 global McKinsey survey found that a number of common obstacles are holding financial institutions back: a lack of front-office controls that leads to poor data input and limited validation inefficient data architecture with multiple legacy IT systems a lack of business support for the value of a data transformation and a lack of attention at executive level that prevents the organization committing itself fully (Exhibit 1). Another institution expects to grow its bottom line by 25 percent in target segments and products thanks to data-driven business initiatives. One US bank expects to see more than $400 million in savings from rationalizing its IT data assets and $2 billion in gains from additional revenues, lower capital requirements, and operational efficiencies. Successful data transformations can yield enormous benefits. Many have set up a new unit under a chief data officer to run their data transformation and ensure disciplined data governance. At the same time, they are taking advantage of cloud technology to make their business more agile and innovative, and their operations leaner and more efficient.
#Easy data transform software update
And where they once built relational data warehouses to store structured data from specific sources, they are now operating data lakes with large-scale distributed file systems that capture, store, and instantly update structured and unstructured data from a vast range of sources to support faster and easier data access. Leading financial institutions that once used descriptive analytics to inform decisionmaking are now embedding analytics in products, processes, services, and multiple front-line activities.
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