1.Descriptive analytics Descriptive (also known as observation and reporting) is the most basic level of analytics. Many times, organizations find themselves spending most of their time in this level. Think about dashboards and why they exist: to build reports and present on what happened in the past. This is a vital step in the world of analytics and decision making, but it's really only the first step. It’s important to get beyond the initial observations and dive into insights, which is the second level of analytics.
2. Diagnostic analytics Diagnostic analytics is where we get to the why. We move beyond an observation (like whether the chart is trending up or down) and get to the “what” that is making it happen. This is where the ability to ask questions about the data and tie those questions back to objectives and business imperatives is most important.
3. Predictive analytics Predictive analytics allows organizations to predict different decisions, test them for success, find areas of weakness in the business, make more predictions—and so forth. This flow allows organizations to see how the first three levels can work together. Predictive analytics involves technologies like machine learning, algorithms, and artificial intelligence, which gives it power because this is where the data science comes in. Now, when we incorporate the importance of not just predicting, but using data science, statistics, and the third-level of analytics combined with the first two levels, organizations truly can see success with their data and analytical strategies.
4. Prescriptive analytics Prescriptive analytics exist at a very advanced level and is the most powerful and final phase, and truly encompasses the “why” of analytics. It’s when the data itself prescribes what should be done. Data-driven decision making is tied most closely to predictive and prescriptive analytics, even though these are the most advanced.