What are the types of Business Analytics?

Depending on where you look, you may see different posts stating that there are three, four, five or more types of analytics. So how many are there? There are three types of business analytics that are consistent among different sources: Descriptive, Predictive and Prescriptive.


Answers the question: “What has already happened?”

Descriptive analytics is the most basic form of analytics and usually the first type of analytics utilized by businesses. Descriptive analytics attempt to describe what has happened in the past. It does this by looking at historical data presented in a manner that is easily digestible such as reports, charts, dashboards, scorecards, etc. Most of the data that you see today are descriptive analytics.


  • income statements
  • balance sheets
  • most information on your car’s dashboard: mpg, odometer, average speed
  • school report cards
  • fitness tracker applications

Depending on how this data is presented you can begin to identify trends, comparisons and changes from period to period. Descriptive analytics do not tell you anything about future performance or the likelihood an event will happen.


Answers the question: “What might happen?”

Predictive analytics can use most of the same data that make up descriptive analytics and attempt to predict probable futures based upon historical performance. You will notice that predictive analytics uses words like “might happen” or “probable” and not “will happen” and “certain”.

Everything is possible and Nothing is certain

The above quote demonstrates the point of predictive analytics – the fact that we cannot look into a crystal ball and determine the inevitable future, we need a way to determine on a scale from “Will Not” to “Will” how likely something will happen – this is done with probabilities.  Since nobody knows what will happen, probabilities exist to protect you against your guesses about the future.


  • business earnings forecasts
  • other information on your car’s dashboard: remaining mileage based on fuel
  • exit polls to predict election winners

Knowing that an event is likely or unlikely to happen – what can you do to take advantage or hedge against the event?


Answers the question: “What can I do about it?”

All organizations utilize at least one outcome of prescriptive analytics (wether they choose to or not) – that is, deciding to do nothing about a probable event.  Inaction is just as much as a choice as performing a specific action. Prescriptive analytics aims to provide the action steps necessary to achieve results of predictive analytics and how each action will  impacts everything else.

Three Phases of Analytics


A simple way of viewing the phases of business analytics is to compare it to healthcare.

Before deciding to visit a doctor, there are typically symptoms experienced by an individual that can be readily described (e.g. headache, fatigue, pain, etc.) – in other words this is raw data. At this point a doctor can interpret symptoms and/or run additional tests to ultimately make a diagnosis, he is only telling you what disease or ailment that you have. This is Descriptive Analytics.

The next step a doctor can do is to analyze the situation further. From the data obtained from the additional tests or the severity of the symptoms a doctor can make a prognosis – that is a forecast of the most likely outcome of the diagnosed disease or ailment. This is Predictive Analytics.

Lastly, a doctor may be able to explain the possible treatments of the disease or ailment. The treatments may have varying success probabilities but may also have possible side effects. Choosing a treatment will have defined benefits such as treating symptoms – but can also introduce additional factors not previously seen before. This is Prescriptive Analytics.

The previous example is pretty rudimentary, but it demonstrates an important point.  The reason why the patient was able to identify and take action on a perceived problem (symptoms) was by the seeking help of a medical professional who is trained in diagnosing and treating diseases. The patient in this case can be easily compared to any business professional. Analytics professionals can assist businesses identify and take actions on perceived problems, but the outcome relies heavily on the available data.



Business Analytics (Wikipedia)

List of 6 Analytics Maturity Models

A Maturity Model is a tool to assess your organization’s process capability in a specific domain – it is typically divided into levels or stages. The idea is that you cannot move to a higher stage until you have comprehensively met the requirements of all stages below. The goal is to achieve the highest step.

The idea of a Maturity Model is nothing new. One of the earliest maturity models developed, was in conjunction with the U.S. Department of Defense: the Capability Maturity Model, which focused on improving the software development process. The CMM is inspired by the ideas  found in the book Managing the Software Process (Humphrey).  Carnegie Mellon University now administers and markets CMMi (Capability Maturity Model Integration) – a process improvement training and appraisal program.

The Five CMM Levels: Initial -> Managed -> Defined -> Quantitatively Managed -> Optimizing

I first came across the concept of an Analytics Maturity Model when reading Competing on Analytics: The New Science of Winning (Davenport, Harris).

Analytics Maturity Pyramid
Figure 1: Analytics Maturity Pyramid

The model introduced has also appeared in a step-wise notation with definitions at each stage (Figure 1).

Five Stages of Analytical Maturity
Figure 2: Five Stages of Analytical Maturity

This model was developed by the analytics software developer SAS who wrote a white paper on the model.  Similar to CMM, it also has five levels (Figure 2).

Many other organizations have started to offer their own analytics maturity models, each with their own spin. Some offer access to a self-assessment tool, where you can fill out certain information about your company and proceed to be evaluated. Here is a list of five others (in no particular order).

1.  Online Analytics Maturity Model (OAMM) – Cardinal Path

Cardinal Path’s Online Analytics Maturity Model measures your organizations analytics maturity against six areas: Governance, Objectives, Scope, Team & Expertise, Improvement Process Methodology and Tools, Technology & Data Integration. The OAMM is presented in the style of a radar graph indicating a score in each of the six areas (Figure 3).

Cardinal Path's Online Analytics Maturity Model
Figure 3: Cardinal Path’s Online Analytics Maturity Model

2. Adobe Analytics Maturity Model – Adobe

Adobe’s Analytics Maturity Model is focused around their Marketing Cloud product suite and is primarily directed towards web analytics (Figure 4).

Adobe Web Analytics Maturity Model
Figure 4: Adobe Web Analytics Maturity Model

3. Big Data & Analytics Maturity Model – IBM

IBM’s Big Data & Analytics Maturity Model is also a five level model that focuses not only on analytics maturity, but also other areas of the business including: business strategy, information, culture and execution, architecture and governance (Figure 5).

IBM Big Data & Analytics Maturity Model
Figure 5: IBM Big Data & Analytics Maturity Model

4. Data Science Maturity Model – Booz Allen Hamilton

Booz Allen Hamilton’s maturity model is heavily focused on data science – the process by which insight is gained through data (Figure 6).

Booz Allen Hamilton Data Science Maturity Model
Figure 6: Booz Allen Hamilton Data Science Maturity Model

5. Informs Analytics Maturity Model (AMM) – Informs

The organization behind the Certified Analytics Professional program has developed their own maturity model that is quite different from the list above.

Compared to the other models that evaluate your organization’s analytical maturity as a whole (which Informs also does – Figure 7) – it evaluates the maturity of the following categories and factors:

  1. Organization Maturity (Figure 8)
    1. People
    2. Leadership Impact
    3. Measures
    4. Processes
  2. Analytics Capability Maturity
    1. Analytic Framework
    2. Roles and Skills
    3. Analytic Services
    4. Analytic Processes
  3. Data and Infrastructure Maturity to Support Analytics
    1. Health
    2. Access
    3. Traceability
    4. Analytics Architecture
Informs Assessment Summary Score
Figure 7: Informs Assessment Summary Score
Informs Organization Maturity Graph
Figure 8: Informs Organization Maturity Graph

You have seen six different approaches to evaluating your organizations analytic maturity – some take a global view of your organization and place you along a scale, others evaluate you on a more detail level and roll up your results into a final score.

Do you prefer one model over another? Are there other models out there  that you think should have made this list? Please share your responses in the comments below.