If Data is the New Oil, We’re About to Bust

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You’ve heard it before: Data is the new oil. The oft-quipped adage regained traction last year when Intel CEO Brian Krzanich repeated it in a Fortune interview. When enterprise execs and AI experts say data is the new oil, they mean it’s fuel for our information economy; the single largest driver of innovation.

And the proof is all around us. You’d be hard pressed to find a company that doesn’t capture and mobilize data to some extent. Imagine running an ad campaign without metrics. Think about trying to service customers without any data about their behaviors and preferences. Data is now powering all major business decisions to the extent that it feels impossible to imagine economic momentum without it.

Oil was the fuel that recreated our industrial world: powering the automobile revolution, changing the way we built cities, and creating a trillion dollar industry. It put Houston on the map. Data is on track to do the same. It’s revolutionized our phones and changed everything from the way we experience retail to how we receive our healthcare. The trail of money tells a similar story: The big data and analytics industry is projected to reach $203 billion in two years.  In the same way the gusher age of oil transformed Texas, data has fundamentally changed the way we do business.

Our ability to create, store, share, and gather data built the information economy, as much as refining oil created a new industrial economy. And now, our ability to derive insights from vast amounts of data, using ML algorithms and supporting AI technology, is powering the intelligence economy. And while the technology is more advanced than ever before, in many ways this economy is just as wild and lawless as the age of oil.

The Wild West of Data

A study from Stanford found that there has been a “14X increase in the number of active AI startups since 2000.” Clearly, shanty shops offering cheap data insights trying to pass as AI abound. These small companies are jumping on a trend and looking to make a quick buck. And like their oil-era counterparts, they’ll soon go the way of consolidation or closure.

More dangerous are the big AI vendors that are operating under the boom mentality and so aren’t being as strategic or transformative for their customers as they should. These vendors know that their customers have data ready and waiting — and they want to bring AI to transform it. These businesses don’t know how to build or deploy a solution themselves, and so they bring in a vendor, hopeful that they’ll gain a personalized solution and a skill in one fell swoop. However, instead of offering customers either of these things, too many vendors instead deploy  a broad-application solution.

In the long run, this hurts everybody looking to utilize (and monetize) data. 31% of enterprises have said that they want to implement AI into their business in the next 12 months. But if vendors don’t service these companies properly, they’ll not only hurt their customer’s business — they’ll kill their own. The data boom will turn into the data bust

Coming Up Empty

Part of the problem is that businesses are in a desperate situation. They’re drowning in data. Whether they’re a small business or a multinational corporation, businesses took the message of data as a fuel too literally and just started drilling. Though they may now have the data, they can’t refine it — or even make sense of it.

Instead of building a data pipeline, enterprises have inefficient data lakes. This problem bleeds into businesses’ success with AI. Narrative Science, in a survey of 200 executives, found that 71% had an innovation strategy which they were using to push investments in new technologies like AI. Despite these well-intentioned strategies, Gartner has found that 85% of big data or AI projects fail. We’ll continue to see these kinds of numbers until AI vendors realize that it’s not just about the technology. It’s about the implementation.

If data is the new oil, then our current AI strategy is like trying to use a divining rod to drill. The vast majority of enterprises don’t understand how to meaningfully plug AI into their business processes. Due in no small part to the high promises of the AI industry, companies assume that all it takes is a deployment of AI to begin seeing value across the organization. And if AI were correctly oriented from the start, this would be true. However, one of the largest barriers to outcomes is that AI is bought and then slotted into an organization as quickly as possible — without any real understanding of the problem, or if there’s a problem at all

Instead of treating AI like a tool as a real as a derrick, which requires precise setup and intentional use, the industry treats it like catch-all service. The fact is, it’s not enough to have AI integrated into your business if it’s not plugged in to your actual needs. All of the power of the technology, as well as the data that underwrites it, goes to waste when improperly deployed. It’s little wonder we’re coming up empty as an industry. We’re drilling in the wrong spots.

The Value of Intelligence Realized

So how do we take the lessons of precision from the industrial era and apply them to the age of intelligence? The first step is to align your business needs with your AI strategy. AI is predicted to boost profitability by an average of 38% by 2035, but we can only achieve that value if AI is tackling real problems in your organization

Before considering an AI solution, identify the challenges your organization is facing. Do your customers need better self-service options? Do you need more intelligent internal systems? A good AI vendor will not only ask these questions, but they’ll be able to help you identify the answers.

Data certainly has the promise of becoming the next major industry, and with AI as its vehicle, the future seems just as lucrative and legendary as the rise of “Big Oil.” But we need to know how to utilize it. Otherwise we’ll bust before we even have the chance to boom.

About the Author

Tracy Malingo is Senior VP of Product Strategy of Verint Intelligent Self-Service, a division of Verint, where she provides strategic and operational vision on the company’s extensive and innovative conversational AI-suite.

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