Tag Archives: data vs intelligence

More data doesn’t always equal better decisions

There is currently a lot of interest in Big Data. We can now capture and store immense amounts of information about almost any process, include how humans behave. Buying and browsing habits drive internet advertising at a personal level. Face-recognition software allows companies and authorities to track people’s movements and activities. GPS tracking on cell phones shows where we have been, and when we were there.

In the supply chain world more data has often been seen as an advantage. The thinking is that the more we know, the better our decisions will be, since we have more facts to use as the basis for our choices. In general this is true. However there are at least 3 factors that can reduce the value of the data.

First, in many cases we have reached the point where there is more data than we can effectively analyze. Time constraints often require that decisions be made before all the data is available. So rigorous analysis has become a luxury. And standardized reporting has replaced ad hoc reporting.

Second, more data can hide valuable data. It’s not more facts but the right facts properly interpreted that allow for good business decisions. This is where experience can trump data: the numbers may look good, but if your gut says the decisions being made are not right, it’s worth taking another look at the numbers.

Finally, data is subject to interpretation. An instock figure of 99% may be good for a commodity item, but not for a seasonal item that is approaching the end of its annual cycle. And the way data is presented can skew how it is interpreted. If a presentation looks too good to be true, ask to see the raw data and have the presenter to walk you through the process used to arrive at the final data. It may not be as objective as is appears. And too often politics rather than data drives decisions.

In short, more data will not automatically lead to better decisions. Sound decisions require data, clear thinking and time. And in many cases these are shortchanged. It’s no wonder then, that people are often disappointed in the results of decisions made only on the volume of data available.


Data doesn’t always equal intelligence

Data drives many modern supply chain processes. Measurement of the time, equipment and costs involved in these processes allows for optimization and improvement. What can be measured can be improved.

It is easy to collect lots of data. What is hard is to know what data is valid, and how to interpret it so that it can be used to improve the processes. More data doesn’t always equal a better process. In fact, there is a point where collecting more data actually increases the noise in the data and makes it harder to interpret.

Data properly validated and interpreted equals intelligence. And intelligence must be action-able. It must tell us what we need to do differently.

For example, collecting lots of data on vendor lead time variability might seem like a good idea. But by itself this data won’t tell us the whole story. We need to know about all the factors that could impact this data, and decide which factors are significant and manageable, and which factors are outside our control. Penalizing a vendor for poor lead time performance based solely on data measurements may hide the fact that our own processes are keeping the supplier from improving. I know of a case where the vendor’s lead time increased dramatically because the company that was ordering product from them was also arranging the freight pickups from the vendor, and was not scheduling these correctly. Not the vendor’s fault here.

So remember that data by itself frequently won’t tell you how to improve your business. Only the intelligent interpretation of the data will allow you to see where you can take action to improve your processes.

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