Master datasets and leverage them to make better business decisions

According to IBM, 90% of worlds data was created just in the last 6 years – 90%! Fuelled by the internet generation, each and every one of us is constantly producing and releasing data. Be it ourselves to companies to capturing customer information and sales transaction. The volumes of data make up what has been designated ‘Big Data’. This massive data sets are piling up year on year.   The problem is how to leverage this data to make better decision?

There are three (3) simple stages one needs to unpack to:  Define the Problem, Solve the Problem, Communicate Actionable Results Clearly.

 

 

 

1. DEFINE THE PROBLEM

 

 

The first, DEFINE THE PROBLEM. Speak with any startup or design thinker and they would say fall in love with the problem not the solution. In fact, Albert Einstein was said If I had only one hour to save the world, I would spend fifty-five minutes defining the problem, and only five minutes finding the solution. Coming up with solutions is easier. Solving the right problem and defining can be challenging. This is no different with big data projects and trying to leverage it to make better insightful decisions.

Framing the problem is about defining the business question you want analytics to answer and identifying the decision you make as a result. Its an pretty important step. If not don’t frame the right problem, no amount data or analysis in the world is going to get you the answers you are looking for.

Defining the problem is split into two parts, framing the problem (what your solving to frame) and reviewing previous findings (what worked or didn’t work) to help you refine the problem.

Framing the problem involves asking yourself “Why is that a problem?” . Toyota famously created the “five why’s” technique. Its about understanding the root cause of the problem. Designs companies like IDEO use phases with the words “How Might We…” to help frame the problem.

reviewing previous findings, involves finding out what worked in the past and why things didn’t work before. This also helps refine the problem.

 

 

 

2. SOLVE THE PROBLEM

 

 

The second stage is SOLVE THE PROBLEM. This often thought to be the primary one. This stage where you starting collecting the right variables (i.e. data fields), collecting sample data to test/play with, and doing some basic analysis to test assumptions quickly. This is also similar process as Cross Industry Standard Process for Data Mining, commonly known by its acronym CRISP-DM.

CRIP-DM is a data mining process model that describes commonly used approaches that data mining experts use to tackle problems.

 

 

800px-CRISP-DM_Process_Diagram

 

 

We at HIVERY we use similar simplified version called DEP – Discovery, Experiment/Pilot and Deployment.

 

 

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3.COMMUNICATE ACTIONABLE RESULTS CLEARLY

 

 

 

The third and final stage is COMMUNICATE ACTIONABLE RESULTS CLEARLY.  If you want anything to happen as results of stage 1 & 2, you got to communicate your results effectively. If a decision maker do not understand the analysis done or what the results means, he or she won’t be comfortable making a decision based on them. With our “communication-challenged” world, communicating sophisticated analytical results effectively and simply makes the world of different.

 

 

A good data visualisation books on this topic is called Storytelling with Data: The Effective Visual Communication of Information by Cole Nussbaumer Knaflic.

 

 

Data needs to be to engaging, informative, compelling. Human often use stories to communicate effectively and help create memorable knowledge transfer.