4 levels of analytics you need for better decision making

When we think about data trends, we think about the big catch phrases like machine learning, big data, AI and the like. But at the core of it, data is all about helping you make smarter, more well-informed decisions.

What would be the point of things like predictive algorithms and big data if they didn’t lead organizations to make smarter, better, well-informed decisions? But it's not just access to data that helps you make smarter decisions, it's the way you analyze it. That’s why it’s important to understand the four levels of analytics: descriptive, diagnostic, predictive and prescriptive. 

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.

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. 

Imagine going to a doctor where the only thing they do is look at you, make the observation that “oh, yeah, you look sick,” and then leave the room. That's not going to do much for your health. We need to be able to understand what is causing the sickness. The doctor should make the observation, diagnose you and then give you a treatment plan to help you feel better. It’s the same thing with analytics: you make an observation, identify the descriptive analysis and move forward to the diagnosis.

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.

However, the reality is that currently most of your organization isn’t spending a lot of time with predictive analytics. Leaders are spending most of their time in descriptive and diagnostic, but predictive is a very important part of the puzzle. Every organization needs a workforce that can speak the language of data, and the language of predictive 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.

Think of prescriptive analytics as taking all other levels of analytics to prescribe things you should be doing; the data and analytics show you the way. Thomas Matthew, chief product officer at Zoomph describes it well:

"Prescriptive analytics builds on predictive by informing decision makers about different decision choices with their anticipated impact on a specific key performance indicators. Think of traffic navigation app, Waze. Pick an origin and destination and a multitude of factors get mashed together, and it advises you on different route choices, each with a predicted ETA. This is everyday prescriptive analytics at work."

Think of the first three levels of analytics: you have your description of what has happened, followed by diagnosing why, and then you end with predicting what will happen. Now, imagine you allow the data and analytics to inform you what action to take. That is powerful and why it matters for businesses.  

All four levels create the puzzle of analytics: describe, diagnose, predict, prescribe.  When all four work together, you can truly succeed with a data and analytical strategy. If the four aren’t working well together or one part is completely missing, the organization’s data and analytical strategy isn’t complete. 

These four levels of analytics need to permeate throughout an organization in order for data literacy to be effective. Additionally, teams need to have better skills which allow them to tap into each level as best they can. The ultimate hope is that those decisions tie back to the most important business objectives and goals.