5 questions to ask before starting a Big Data project
Between the weekly reports on plant performance, supplier KPIs and inventory levels, more data may be the last thing supply chain managers want to crunch.
Yet every day, more data keeps coming: the world creates 2.5 exabytes of it every day (that’s 1 billion gigabytes), according to IBM. But it wasn’t always this way. According to IBM‘s calculations, 90% of the world’s data was created in the past two years alone, and reports abound on how businesses are using it to save millions of dollars and improve efficiency by double digits in ways previously never conceived.
When budgets tighten, it’s no surprise that executives turn to the promise of Big Data to increase efficiencies. After all, many companies have spent over a decade bringing in or upgrading data processing systems, transitioning to the Cloud and/or implementing sensors. Now, supply chain managers are being asked to use that data … it’s easier said than done.
Recognizing the challenge of starting a project in the dark, Supply Chain Dive spoke with Suresh Acharya, Head of JDA Labs, for a step-by-step guide on Big Data application.
“Nothing has to be daunting, there is a way in which one can do it,” he said, pointing to five questions supply chain managers must ask themselves before starting a new project:
Perhaps the biggest problem executives have when trying to apply data is by not having a case to solve in mind. When starting a new project, supply chain managers should have both a specific business problem to solve (say, out of stock inventory is too high) and be able to quantify it (a 5% reduction will lead to X million dollars in savings).
“If you go from the data to figure out what business problem you’re going to solve that’s really putting the cart before the horse,” Acharya said. “What you want to be able to say is: This is what I want to solve, and is the data that I have – or intend to collect or can buy or subscribe – going to help me solve this?”
“So, make sure that you have a business case, that you’re trying to solve a business problem,” he added.
Thinking of a Big Data project as a problem to be solved, rather than a project to be completed, may reveal that the data currently available may not be the information needed to solve the problem.
“If you’re going to look at inventory or out of stock, do you have data around inventory? Do you have data around point of sales, or orders, or whatever those things might be. There should be an alignment in terms of the business problems you’re trying to solve and the data sources that you have,” Acharya told Supply Chain Dive.
