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What are descriptive analytics?

Descriptive analytics can be used to describe events that have occurred in the past and are used to assist in analyzing historical information.

Descriptive analytics are more common as the methods are not as advanced as predictive analytics. Even though most finance and accounting teams don’t even know what descriptive analytics are, they are likely using a form of descriptive analytics in their daily activities.

Some very common examples of descriptive analytics include total number of users, period to period sales or expense changes, revenue per user, or changes in pricing.

Basically, companies have loads and loads of data. Many companies know how to use this data at a very high-level, but very few know how to use historical data to improve their business. The role of data analysts is becoming increasingly important as companies have more data than ever. Data analysts help companies collect and classify data using data aggregation and data mining techniques.

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  • What are predictive analytics?

    Predictive analytics use statistical techniques and forecasting models to predict possible future outcomes. There are a variety of approaches and techniques, but they can be grouped at a high-level into regression techniques and machine learning techniques. Many business are starting to rely on predictive analytics to identify patterns in historical and transactional data to understand where risks and opportunities are present. If used properly, predictive analytics can be used to predict the future and help companies minimize risk while capitalizing on opportunities. Basically any industry or business can use predictive analytics to minimize future risk and capitalize on potential opportunities. Predictive analytics are used in social networking, child protection, pharmaceuticals, healthcare, mobility, insurance, sports (including fantasy sports), business management, and marketing.