Unlocking Data Assets for Capital Markets
A $6Tr Financial Services Industry spends more than $40Bn in acquiring and managing data. According to a survey by TMM Data & Digital Analytics Association, ~40% of data professionals spend more than 20 hrs / wk accessing, blending and preparing data rather than performing actual analysis. With more than 90% of all new enterprise data appearing in unstructured sources, businesses face an arduous challenge of making the data usable before losing its economic value. While legacy solutions are rigid, expensive and difficult to implement, companies lack tools to synthesize valuable unstructured information at scale. Refer to the visual representation of unstructured data flows across various organizations within Capital Markets.
>90%
20x
>80%
Challenge
A large capital markets organization services more than 30,000 global organizations including buy & sell side, fintech businesses and governments. Primary business includes providing information, data management solutions, analytics and insights to Asset Managers, Investment Managers, Banks, HedgeFunds, Insurance organizations, Brokers etc.
A lot of data monetized by the client is sourced from large and varied unstructured sources. As a result, data operations teams spend enormous time, effort and financial resources to retrieve data - running in thousands of data points from across 50+different data assets. Together, the client experiences significant challenges with process inefficiencies, data quality errors, limited scalability amidst explosion of new and alternative data assets across more than 100,000 sources.
Solution
Most of the solutions are built with technology in mind and not the data and user in mind. So, in simple words, a tech solution is given to the user and the expectation is for the user to work around it. We @ SageX on the other hand, flipped it by putting the data user in the center and putting the technology, operations and processes around the user.
Having spent years looking at data in deep trenches, we built the solution with 3 core principles:
- "SaaS First" experience for data users by abstracting everything tech and ops from the user workflows but still making it transparent to assess the efficiency of the engine and ROI
- The ops team knew their data the best and hence all the models in the framework learnt directly and in an accelerated manner from the data experts. Users look at data differently, interpret it differently and consume it differently so how can there be one model or approach?
- Data Scalability was the key with different data assets, different use cases, different teams, the solution provided connected, “ready for consumption data” for different systems, teams and assets.
Interested in simplifying your Unstructured Data Lifecycle?
