We’ve covered a few fundamentals and pitfalls of data analytics in our past blog posts. In this blog post, we focus on the four types of data analytics we encounter in data science: Descriptive, Diagnostic, Predictive and Prescriptive.
Note: This blog post was published on the KDNuggets blog – Data Analytics and Machine Learning blog – in July 2017 and received the most reads and shares by their readers that month.
When I talk to young analysts entering our world of data science, I often ask them what they think is data scientist’s most important skill. Their answers have been quite varied.
My message to them is that their most important skill will be their ability to translate data into insights that are clear and meaningful to a non-quant. The Swedish statistician Hans Rosling is famous for this. It’s an often overlooked skill. The following TedTalk by Hans Rosling sheds some light:
On this theme, it would be worth unpacking some of the tools used to help individuals understand the role of analytics in helping develop valuable insights. One such tool is the 4-dimensional paradigm of analytics.
Simplistically, analytics can be divided into four key categories. I’ll explain these four in more detail below.
Also see our guide to using machine learning in business, where we explore how to use machine learning to better tap into your business data and gain valuable, informing insights to improve business revenue.
The Four Types of Data Analysis are:
1. Descriptive Analytics: What is happening?
This is the most common of all forms. In business, it provides the analyst with a view of key metrics and measures within the company.
An example of this could be a monthly profit and loss statement. Similarly, an analyst could have data on a large population of customers. Understanding demographic information on their customers (e.g. 30% of our customers are self-employed) would be categorised as “descriptive analytics”. Utilising useful visualisation tools enhances the message of descriptive analytics.
2. Diagnostic Analytics: Why is it happening?
The next step in complexity in data analytics is descriptive analytics. On the assessment of the descriptive data, diagnostic analytical tools will empower an analyst to drill down and in so doing isolate the root-cause of a problem.
Well-designed business information (BI) dashboards incorporating reading of time-series data (i.e. data over multiple successive points in time) and featuring filters and drill down capability allow for such analysis.
3. Predictive Analytics: What is likely to happen?
Predictive analytics is all about forecasting. Whether it’s the likelihood of an event happening in future, forecasting a quantifiable amount or estimating a point in time at which something might happen – these are all done through predictive models.
Predictive models typically utilise a variety of variable data to make the prediction. The variability of the component data will have a relationship with what it is likely to predict (e.g. the older a person, the more susceptible they are to a heart-attack – we would say that age has a linear correlation with heart-attack risk). These data are then compiled together into a score or prediction.
In a world of significant uncertainty, being able to predict allows one to make better decisions. Predictive models are some of the most important utilised across many fields.
Here are the Top Pitfalls to avoid in Predictive Analytics.
4. Prescriptive Analytics: What do I need to do?
The next step up regarding value and complexity is the prescriptive model. The prescriptive model utilises an understanding of what has happened, why it has happened and a variety of “what-might-happen” analysis to help the user determine the best course of action to take. A prescriptive analysis is typically not just with one individual response but is, in fact, a host of other actions.
An excellent example of this is a traffic application helping you choose the best route home and taking into account the distance of each route, the speed at which one can travel on each road and, crucially, the current traffic constraints.
Another example might be producing an exam time-table such that no students have clashing schedules.
Conclusion
While different forms of analytics may provide varying amounts of value to a business, they all have their place. To find out how data analytics could bring further value to your company, please drop us a mail to arrange for a chat.
