Big data analytics significantly improve distributors' performance. Identifying new cross-and up-selling opportunities with existing customers is a way to quickly and easily increase sales. With data analytics, you have instant access to real-time and fact-based analysis with just the click of a button. Furthermore, your report can be drilled down for more detailed information. Data analytics tools allow inventory and operations managers to quickly analyze stock-turns, inventory-to-purchase ratio, or slow-moving stock to identify over- or under-stock and enable you to avoid the cost of a product becoming dead stock. Big data analytics can improve efficiency, help managers to save time and accomplish more in an average work day. The ability to quickly slice and dice data for fast insights eliminates the time spent waiting for static reports. This enables managers to closely monitor activities, and detect and prevent any potential challenges before they become problems.

With the help of big data analytics, banks can analyze the market trends and decide on lowering or increasing interest rates for different individuals across various regions. Big data points out anomalies in customer data which helps business analysts and product managers be more accurate. With fraud detection algorithms, customers who have poor credit scores can be identified so that banks don’t loan money to them. With performance analytics, employee performance can be assessed whether or not they have achieved the monthly/quarterly/yearly targets. Based on the figures derived from current sales, big data analytics can determine ways to help a company scale more efficiently. Even banking as a whole can be checked to know what works and what doesn’t.

Most data that’s gathered and stored is used for routine documentation, planning and forecasting. Companies with a lot of data are growing in their ability to use data to support strategic initiatives and create predictive models under different scenarios. A more data-driven supply chain also allows them to more easily benchmark within the company compared to competitors who don’t efficiently use data analytics. For example, companies can use predictive analytics to accurately forecast how their supply chain might evolve, while prescriptive analytics can be used to develop risk-mitigation strategies to fix identified weaknesses.