4 Ways a Data Scientist Adds Business Value to a Financial Institution
Thanneermalai Krishnappan
Jun 27,
2018 |
Posted in
Data & Analytics
Big data is nothing without the expertise to turn cutting-edge technology into actionable insight. And with more and more financial institutions unlocking the power of big data, the value of data scientists, who know how to drive value from the vast amount of information that exists inside the institution, is becoming increasingly apparent.
By enlisting the help of an experienced data scientist, financial institutions are better equipped to reach their business objectives. Here are four ways data scientists can add value to any business:
1. EMPOWERS MANAGEMENT AND OFFICERS TO MAKE BETTER DECISIONS. An experienced data scientist serves as a trusted advisor and strategic partner to an institution’s management, and ensures the staff are maximizing their analytics’ capabilities. By tracking, measuring, and recording all performance metrics, a data scientist will demonstrate and communicate the value of an institution’s analytics product to facilitate improved decision-making across all levels of the organization.
2. DIRECTS ACTIONS BASED ON TRENDS AND HELPS DEFINE GOALS. After exploring and examining an institution’s data, data scientists can prescribe and recommend actions to improve a financial institution’s performance, better engage with customers, and ultimately—increase profitability.
3. CHALLENGES STAFF TO ADOPT BEST PRACTICES AND FOCUS ON WHAT MATTERS MOST. Data scientists are responsible for ensuring all staff are familiar with and well-versed in their institution’s analytics product. The scientist sets staff up for success by demonstrating how to effectively use the system to extract insight and drive action. Once the staff has a better understanding of the product capabilities, they can then focus their efforts on addressing key business challenges.
4. IDENTIFIES OPPORTUNITIES. While working with the institution’s current analytics system, a scientist will question existing assumptions and processes in order to develop additional methods and analytical algorithms. Data scientists will constantly work to improve the value derived from the institution’s data.
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