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.
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