How to make sure your big data solution actually gets used

So you’ve sold the business value and return on investment for your big data solution to upper management, and the project is up and running. Should this be all there is?

The question you should be asking is how the project will project stand the test of time and continue to be used productively in company operations. If a project doesn’t successfully cement itself into the business for the long term, you get shelfware.

This is not where you want your project to end up.

One use case that really demonstrates the value of operationalizing big data and analytics is in the track and trace functions of a food supply chain.

To gain visibility and automate steps at every stage of the food pick, pack, ship and deliver process, food producers, shippers, warehouses and retailers use handheld devices, barcode scanners, hands-free, voice-based technology and even sensors placed on pallets, packages and refrigeration compartments in trucks. These sensors track temperature, humidity and tampering of the containers for perishables and other goods, and also issue auto alerts to supply chain managers as soon as one of these conditions is violated. Everyone in the food supply chain knows where every shipment is. Along the way, big data is collected in a central data repository where queries and reports are subsequently run to assess how well the supply chain is performing.

Best of all, big data handling technology has been successfully operationalized for the long term. It has become part of the daily business, and there is buy-in from everyone.

How did this big data get operationalized? By identifying insertion points for big data and analytics that made work easier and delivered the business value that was promised. For successfully operationalized big data and analytics ROI, there is now a continuous payback that goes well beyond the 12-24 months that initially was targeted as a timeframe for payback on investment.

What are the best ways to ensure the long term use of your big data and analytics projects?

1. Always remember that usability is as important as capability

If an end user can’t pick up the technology intuitively and understand how it works to their advantage in the work environment, the device could end up sitting on the shelf. Meanwhile, the user will go back to doing things the way they’re used to doing them.