Submitter
Michael King, Director, Data Governance and Analytics
Company
The Bank of New York Mellon
Industry
Financial Services
Business Challenge
Every day, in addition to a standard set of questions for which several dashboards exist, Operations deals with hundreds of new business questions that impact BNY customers, executives and client relationship managers. Answering those questions is time consuming and onerous due to the variety and volume of new questions. Some of the answers to these new questions may be found in existing dashboards, but invariably result in a data gathering, querying and analysis effort that takes several hours to days. It is cost and time prohibitive to try to build out new dashboards to answer every new question that arises and needs immediate answers. ThoughtSpot allows the data analytics team to create a data model and allow the business user, be it the C-Suite/relationship managers/risk team/compliance, to instantly utilize the data that would otherwise take weeks to access.
Analytics Solution
The former approach presented challenges, as dashboards had to be built by analysts trying to anticipate the business needs. Due to large volumes of data, the data needed to be aggregated so the legacy tools could be performant. The static dashboards and reports only allowed the business to be reactive instead of proactive. If the dashboard couldn’t provide the answer, the business would wait for hours or days to get answers for ad hoc questions. This manual, time-consuming, reactive approach caused BNY to miss service level agreements with clients, resulting in eight to nine figure losses.
With the new approach, ThoughtSpot provides an entry point to start with a question and allows for the ease and flexibility for all users no matter the technical skill set to ask questions to get very specific answers. On top of that, ThoughtSpot’s AI proactively surfaces insights, anomalies and trends to users that they may not have been aware of.
Impact
With the end-user self-service capability to search within large, granular datasets, BNY Operations will be able to streamline operations, quickly identify operational trends and help reduce service level agreement payouts, saving BNY at least $2 million.