How to Avoid New Digital Technology Buyer’s Remorse

How to Avoid New Digital Technology Buyer's Remorse

How to Avoid New Digital Technology Buyer's RemorseThe original bright, shiny object that got everyone into a lot of trouble was the apple in the Garden of Eden.

As Adam and Eve could now tell those in the C-suite, those bright, shiny objects — such as new technology — can  look good but come with a whole lot of problems.

While new technology often promises to drive innovation and bigger profits and give companies a leg up on the competition, it also can become a giant headache and a financial drain. Consider the research that shows that while innovation spending reached $600 billion in 2012 among the top 1,000 spenders, only one-quarter say they’re very effective at innovation.

At the same time, it’s true that many companies have successfully used technology to help boost their innovation and improve the bottom line.

So what’s the key to avoiding buyer’s remorse and using technology to drive innovation?

Xena Ugrinsky, senior vice president of analytics and cloud at Booz Allen Hamilton in New York, says that while there are many who just want to “crack open the checkbook” and pay for big data and new technology to light a fire under an organization’s innovation, many are also getting more skeptical and “peeling back the media hype.” Such companies are taking the time to scrutinize those who are truly achieving innovative results and how they reached such a goal.

That’s critical, she says, because organizations that want to avoid buyer’s remorse must stop just “grabbing the credit card” to pay for the latest bright, shiny object. It’s more important for leaders to take the time to understand what makes sense culturally and strategically.

“We’ve got organizations who did the ‘big bang’ – they built this data system that no one is using,” she says. “It just goes to highlight that you can’t do technology in a vacuum. You’ve got to be strategic.”

Among her suggestions to avoid overspending and under-delivering:

  1. Communicate clearly. “You’ve got to make sure everyone in the organization is speaking the same language,” she says. “If not, stop everything and agree on a vernacular, so everyone understands what certain terms mean.”
  2. Consider the culture. What is the organization’s analytics maturity? Are there people already within the organization who can plumb the data you need – and do it quickly? “This doesn’t always have to be a multi-million dollar exercise,” she says. Consider the silos within the organization – are there roadblocks that will prevent success?
  3. Move it along. “How information flows through an organization is very important to using big data,” she says. “Are you getting the right information to the right people at the right time?”

At the same time, Cesare R. Mainardi, an adjunct professor of strategy at the Kellogg School of Management, advises that there is an easy way to determine whether an organization is headed for digital success:

  • Are you using technology in a way that stays true to who you are, that is uniquely “you?”
  • Are you leveraging technology to build your distinctive capabilities, not your IT functionality?
  • Are you using technology to put your customer at the center of everything you do?
  • Are you going digital in ways that leverage the strengths of your company’s culture?
  • Are you using technology to cut costs so you can free up cash to invest in transformational digital projects that will help shape your company’s, and even your industry’s, future?

Ugrinsky says that companies can get a better handle on what they need by building a “data puddle” before they build a “data lake.”

That means before investing millions of dollars or collecting data without a clear goal, an organization starts small by letting internal analysts collect data and urging leaders and teams to ask questions along the way. Then, an organization may want to “enrich the data puddle” by bringing in outside customer or financial information, she says.

The ultimate goal is to learn how to collect the data that will be useful, and to help more people understand how to ask the right questions so the quest for information doesn’t turn out to be a wild goose chase.  If at some point a data scientist is needed, Ugrinsky advises a “rent-to-own data scientist,” in the beginning.

Data scientists will look at data and look at correlations and see something that wasn’t obvious previously,” she says.

Still, companies must realize that there is a “50/50 chance this person is going to go in the wrong direction,” she says.

That’s because data scientists often don’t have the business background or experience in the organization to understand when things are going off the rails. It’s why data scientists “need to work hip-to-hip with someone who has 20 years of business experience – someone who has lived it,” she says.

Further, because bright, shiny objects are always going to be a temptation, leaders must question technology vendors who may over-promise and under-deliver, she says.

“No one is looking at this holistically. If you’ve got a vendor who isn’t willing to work with others, then that’s another reason not to write a check,” she says.  “To do this the right way, everyone has to work side-by-side. You want someone who is motivated to your success.”

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