Broadly defined, data analytics is a field concerned with collecting, organizing, and analyzing data to gain insights and make informed decisions. Data analytics offers a variety of tools and techniques that can be applied to a wide range of fields and industries, with applications ranging from improving efficiency and productivity to identifying new opportunities for growth and innovation.
There are four basic types of data analytics within the larger field: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
Descriptive data analytics involves using data to understand and describe what has happened in the past. One of the most accessible types of data analytics, descriptive analytics requires only rudimentary statistical software like Excel to manipulate and visualize data so that key performance indicators (KPIs) can be assessed and trends can be identified.
In retail, descriptive analytics is used to calculate year-over-year growth in sales, which can help a company better understand its performance and identify any patterns and trends in consumer demand.
Diagnostic data analytics is the process of using data to understand why something happened, which is why this type of analytics is alternatively called root cause analysis. To understand the root causes of trends identified in descriptive analytics, diagnostic analytics will go deeper into internal data sets to gain a more nuanced view of a trend or look to external data sets that might suggest correlations between a business’s performance and environmental factors. In the latter case, data mining might be employed in which machine learning algorithms search big data sets for patterns and associations.
To continue with our example from retail, if descriptive analytics identifies year-over-year growth in sales of certain products, diagnostic analytics might then be employed to better understand whether there are links between the kinds of products that are performing well and broader customer sentiment, perhaps by mining social media data.
Predictive data analytics involves using data to make predictions about what may happen in the future. Predictive analytics makes extensive use of machine learning and other statistical techniques to build models that output forecasts from inputs of historical data. Predictive analytics is especially useful for risk assessment and identifying new areas of opportunity.
After determining which kinds of products perform well given a certain customer sentiment through diagnostic analytics, our example retailer might employ predictive analytics to better understand the chances of similar kinds of customer sentiment recurring in the near-, mid-, and long-term.
Prescriptive data analytics involves using data to determine the best course of action for the future. As with predictive analytics, machine learning is crucial for prescriptive analytics. With complex machine learning algorithms, a data analyst or data scientist can turn large amounts of data into concrete recommendations for future action — or even automate actions to occur in response to certain inputs.
In retail, a common place you’ll find prescriptive analytics at work is in online product marketing. While predictive analytics can predict the likelihood of certain kinds of customer sentiment returning in the future, prescriptive analytics can employ data mining to identify current consumer sentiment and automatically send personalized marketing emails to segments of a retailer’s customer base it thinks is best poised to respond given its sentiment analysis.
Now that we’ve covered the four main types of data analytics, we’ll move on to discussing how data analytics compares to two fields it’s frequently confused with: business analytics and data science.