”First find out questions relevant to your business and after that start utilizing big data and analytics successfully.” One should really pay attention to figuring out the questions that could bring useful results. For example, in “The Hitchhiker’s Guide to Galaxy” the poorly formulated ultimate question of life, the universe, and everything received an answer of “42” despite the use of a supercomputer surely capable of handling big data in all its forms.
First things first, analytics itself does not bring value for business; it is the knowledge acquired through analytics and put into use that brings the value. There are four steps of analytics, all of which have different kinds of questions they can answer to. These steps also vary in the amount of value they bring to the business. The steps of analytics (adapted from Gartner 10/2014) are illustrated in the picture alongside their linkages to data and operations. Examples of relevant questions to be asked of data are described under different steps of analytics.
Data Collection
It is important to understand, what data a company has in the first place; i.e. what you are measuring and what you should measure. Equally important is to understand that having data is just the beginning. Data collected can be either a valuable gold mine or a showcase of wasted resources. If you do not start climbing the steps of analytics, the latter will be true.
Analytics for Past
Describing past events, i.e. descriptive analytics is the most common step of analytics and many companies perceive analytics to be just that. Once you have the data from company operations from e.g. last month, you want to know what has happened. With such information, you understand better, how well the company has performed. You might also spot pain points and high-performing areas through data.
Analytics for Diagnosis
The second step of analytics aims at increasing understanding of why things have happened the way they have. Typically, in e.g. manufacturing, a myriad of factors may have affected the outcome. In such an environment, finding out the true causes can be very cumbersome. Consequently, various rules of thumb are used in trying to fix acute problems leaving finding out the real causes almost non-existent. This leads to the mode of putting out fires; fires that could be put out for good if one would first understand the causes and fix them instead of the symptoms.
Analytics for Future
The third step of analytics, also known as predictive analytics, is the key in enabling the transition of company operations from reactive to proactive actions. It is so much easier to prepare for the future when you know what you will be facing. The question that many want to get an answer to is “what will happen?” Not everything can be predicted accurately, but in a repetitive environment, such as company operations, many events reoccur and are predictable despite the first impression.
Analytics for Action
At the fourth step of analytics, the value for the business is even higher. Through data, it is possible to support decision-making by giving suggestions for future actions, i.e. what you should do. In certain application areas, it is also possible to automate decision-making as well, although one has to be very careful in attempting to do that. For instance, Google’s driverless car is no small project. At the fourth step of analytics, a company has come a long way from understanding what has happened in the past to understand what to do in the future in order to become even better.
Operational Excellence
As was stated before, analytics itself is not enough; you also need knowledge and most importantly, actions. The biggest value for business comes from situations, where you understand not only the big picture of operations, but also what is going to happen and act accordingly in a proactive manner. Therefore, in order to achieve high-quality operations, company representatives have to ask themselves one more question: Based on the knowledge acquired through analytics, what are our best practices?
