If you know that data and your ability to use it to gain advantage is critical to your business success, yet you are confused by the torrent of advice on how to get ahead, then you might find this short story heartening.

Last month I ran a series of round tabes with Dr Eric Topham of The Data Analysis Bureau at the Industrial Data Summit that was organised by The Manufacturer.

Now you might remember Eric from a previous article we wrote in this blog on “Before Data Analytics, Think of the Problem to Solve”. Eric and myself started working together, mainly because as a Data Scientist, he was fed up with his clients coming to him with a Data Lake and asking for insight. When you are working with these mathematical technologies in the real world, it simply does not work like that. At least not just yet. What he was looking for was some indication of the business problem being solved, ideally with some KPI’s that the organisation is trying to influence.  Without this focus, smart people (and algorithms) can waste alot of time going round in circles.

We had an opportunity to try out this concept on nearly 30 people at the Industrial Data Summit, and guess what: We had such amazing feedback that we were encouraged to put together a 1 day seminar which we will hold at the end of June. For more see this  link.

Essentially we have developed a very simple management process to get started. We don’t pretend that this is the complete story, but it is a way for you to get started and move through the jargon in 3 simple steps. This is the feedback from the round tables:

1. Articulating the Business Problem

Almost without exception, we all concluded that it is important to start with the business problem in mind. When working with data, it is important to not only understand the business value in your immediate area of responsibility, be that the factory or customer; it is important to look across the whole value chain from component to end user. Often data in one area of the chain, can have significantly more value associated with it in another part of the ecosystem. We also discussed looking beyond the immediate product or service, but try to understand your potential impact on the Total Cost of Operation. We called this the Value Iceberg.

In the discussions we observed, there were varying levels of completeness of the business problems described. However in nearly all cases the orders of value (i.e. pounds!) were often missing. Understanding the relative values of the different components of the Total Cost of Operation is critical to identifying priorities. The chart below summarises the discussion.

2. Define the Data Problem

The next challenge we discussed is how to turn the business problem into a business data hypothesis. This would describe an expected or speculated relationship that we hope to determine through analysis of the data. For example, the hypothesis for a predictive maintenance solution might be: ‘We can identify the failure patterns for hydraulic system as well as general machine performance using pressure, oil contamination, temperature and humidity data from the PLC, such that we can predict failures and recommend corrective actions’.

Why is this important? Data Scientists cannot tell you patterns that interest you without knowing the area of interest! Hence converting the business problem into a hypothesis is a key part of the process and applying the scientific method which is question led and iterative.  But the hypothesis does not have to be correct. It is very likely that it will change as more knowledge is gained about the data being analysed or definition of the business problem evolves. One must expect a certain amount of iteration from business problem to data problem as our knowledge expands, and this in turn helps deliver optimal business value.

The next step to defining the data problem is to understand your data maturity and approach. We do this using four parameters, also known as the four Vs:

Volume:How much data do I have to analyse

Variety: How many different data types am I looking at?

Velocity: What is the speed at which the data is being acquired?

Veracity: How accurate, complete and robust is the data?

The 4V’s together with the Business Data Hypothesis will help us define the most suitable analytical techniques that we should consider using.

Now that the data problem is defined, managers can understand where they may have organisational and infrastructure gaps for their project, and from this be able to identify the first steps of their roadmap to a data solution.

3. Pilot then Scale

It is important that these early steps include a pilot of the solution. The goal is to quickly understand if our solution is likely to be successful, and the actions to be taken to scale up across the organization.

The interesting thing is that this approach sounds similar to ‘Lean’ Thinking and the Plan – Do – Check – Act (PDCA) process. So don’t be intimidated, you probably know more about making data work for you then you realise!

If you are located in the UK and are interested to know more, Si2Partners have worked together with Hennik Edge and T-DAB, to offer a 1 Day Seminar in the UK on June 28th to look at how to move from Business Problem to Data Solution. Use this link to find out more.

Nick Frank is Managing Partner at Si2 Partners, a consultancy helping clients leverage services to win in industrial markets. Nick is an expert in Service Transformation, specifically helping organisations use technology to find new value within their customer’s value chain, facilitating bootcamps to help teams solve challenging problems, and business assessments to kick start the change process

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