Making the most of big data
Implementing big data correctly is just as important as collecting the data itself and this part of the conversation is often over-looked in favour of discussing the eye-catching figures. Whether you are already using big data to shape your processes or are still investigating its potential, here are some tips that can help you make the best use of big data.
How it works
Before discussing how to make the most of big data, it is important to understand how big data works. After-all, it’s difficult to see the limitations of big data without understanding its inner workings. A great analogy for big data compared to traditional data is the difference between a pedometer and a modern activity tracker like a Fitbit. A pedometer provides a single point of data, number of steps. From this information, you may be able to work out the number of calories burned, but it doesn’t tell you much about your overall health. A fitness tracker collects not only the number of steps but data like heart rate, time inactive, sleeping patterns and other biometrics. This data is then combined and weighted to give you a much better indication of how healthy you are.
By collecting lots of data and then combining it, you are able to gain a much greater level of insight. In the supply chain, this can be used to enhance decision-making processes by eliminating inefficiencies, measuring success rates and providing more accurate forecasts.
Don’t forget the people
The problem with big data is the bigger the data set gets, the more the people involved in the data get lost in the process. Imagine the number of people involved in the KPIs you examine every day in your supply chain, and this is a single data set. When you combine data for big data analysis, you reduce a number of people’s work into one number.
Companies will use this data to measure the performance of their staff and this can become problematic. One all-encompassing figure can mask the underlying issues. For example, if you combined data to measure customer satisfaction you might push your engineers for a better performance when the inefficiencies are elsewhere.
With any data set it’s important to remember the people involved and treat a data set as a guide rather than a method of judgement.
Change the model
With the rate of data growth, it’s irresponsible to create an algorithm and then forget about it. Processes in supply chains change; new data sets become available and things that you previously took for granted can become unimportant. Take the fitness tracker example above. Early Fitbits didn’t measure heart rates, but they were added in later when the data proved useful.
A good big data model should be flexible. This is especially important in forecasting. Anything you can add to a model potentially makes it more accurate, so keeping it static will just harm your business in the long run.
See data for what it is
We may have more data than ever before, but it is still important not to lose sight on what data really is. Big data codifies the past, it does not invent the future. The more data you have, the more accurate it becomes, but the future is still unpredictable. Big data should be used to guide you down the correct route, but not replace the intuition that made your business a success.
Data is not going away and ignoring big data carries a big risk. The potential for large data sets to change the way you view your business is huge and it is an area where you cannot afford to fall behind your competition. Accenture found that embedding big data analytics in operations leads to a 2.6x improvement in supply chain efficiencies.
Big data analytics, however, still leaves you with choices to make. You choose what data to pay attention to, how to treat your staff and when to update your model. Big data might be shaping the future of the supply chain, but it is still up to you to define how.
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