The field of big data analytics keeps getting…bigger.
Once a practice left solely to data scientists and business analysts, data analytics now touches more parts of the business and supports more job roles than ever before. Here on the APEX of innovation, we’ve covered how big data analytics is redefining corporate functions, the latest strategies for effective data monetization, and the role the CEO plays in driving analytics adoption.
As the field expands, it’s important for the C-Suite and employees from across the business to understand the different types of data analytics approaches out there, and the different types of outcomes they deliver to the business. Below we look at the four most common types of data analytics and how they can help your company:
- Descriptive Analytics: This traditional type of data analytics focuses on using historical data to better understand the current state of things, such as business results and performance. To that end, descriptive analytics is used to make comparisons between time periods or other parameters. For example, financial reporting often uses descriptive analytics to provide updates on past performance versus current performance. This includes using figures, such as revenue or product shipments, and making month-over-month or year-over-year comparisons to track changes in results.
- Diagnostic Analytics: According to the Gartner Glossary, “Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question, ‘Why did it happen?’ It is characterized by techniques such as drill-down, data discovery, data mining, and correlations.” By examining data anomalies, such as a spike in customer service calls or an increase in sales conversions, companies can better understand why a particular business condition exists and then determine the best actions to take to address it.
- Predictive Analytics: This approach to data analytics focuses on determining what may happen next—based on past trends and behaviors. Predictive analytics uses artificial intelligence and machine learning to analyze large amounts of data to help determine the next best action to take in a given scenario. Examples of this include determining future sales based on past purchasing patterns, forecasting inventory levels, and identifying products that customers are likely to purchase at the same time.
- Prescriptive Analytics: According to Tech Target, “Prescriptive analytics is the area of business analytics dedicated to finding the best course of action for a given situation.” Building on the predictive analytics approach, prescriptive analytics makes more sophisticated decisions on what the next best action is to take, offering multiple options and the potential implications of each. For example, business leaders can examine various “if-then” scenarios to help make smarter decisions, such as how to better capitalize on an immediate product opportunity or competitive vulnerability.
To learn more about the impact on data analytics on people, see this recent APEX of Innovation post on Data Analytics for Everyone.