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.

2. The use case must be process-engineered

Just how is the technology going to be placed into a business process in the warehouse or in finance, or in marketing, to provide the benefit everyone is looking for? Years ago, lending software companies began to produce “decisioning” software that rendered automatic decisions on whether or not to grant an applicant a loan. The software was (and still is) used by lending officers. Decisioning software has been so successful over the past 30 years because its creators knew exactly where to insert the technology and the analytics into the lending process, and lending officers found the software easy to use.

3. Build applications that have a future

Successfully operationalized big data and analytics projects will continue to expand. Users will want enhancements, and the company might even find new ways to use a project. To ensure that your project has this room for long-term growth, you and your end users should define and agree on future versions of the project that will move the company forward.

4. Integration must check out

One way of ensuring a future for your project is to make sure that it uses standard software interfaces so it can easily integrate with your existing systems and also with systems from other vendors that your company might choose to bring onboard. Compatibility and integration should both be addressed in your initial design spec.

5. Compliance must be there

Data privacy guidelines are just beginning to be defined for big data types like IoT. In other big data types, there are guidelines for photos, social media posts, etc., that you might be storing for individual customers. Whatever the big data compliance issues are for your industry, compliance must be addressed upfront in any big data project. Once the project is installed, continuing compliance should be checked on an annual basis, since any project falling out of compliance risks extinction.

“Many companies are behind when it comes to adopting new technology,” said Kevin Phillips, CEO of Voxware, which makes voice, AR, and analytic solutions for distribution operations. “In most cases, it is because they don’t have a good understanding of where they need to get, or how technology can help them get there.”

While this was certainly true when companies first started their big data projects, it continues to be true once these projects are completed and installed in the trenches.

Not enough companies are continuously monitoring the value of their installed projects–whether they are big data/analytics or something else. This is where waste can start piling up–and it is an area of neglect that neither IT nor end user managers can afford.

Also see:
How to deliver ROI on big data projects in 6 months
A perfect illustration of how the big data value chain works
How to avoid big data project failures: Your 5-step guide
The Big Data & Analytics Master Toolkit (TechRepublic Academy)