Enterprise AI is Stalling

When yet another AI project fails, fingers are typically pointed at the usual suspects—talent shortages, data quality, a lack of organizational readiness. But the truth of the failure is deeper than that. We’re trying to build a modern AI program on the backs of old tools and techniques that just can’t support the weight.

We’re capturing and storing more data than ever before, and impactful AI rests on making good use of all of that data. It’s crucial that before we start on any AI initiative that we take into account the interconnectedness of the business, assess the quality of the data, and consider enough of the right data points to ensure a usable prediction or prescription. This takes thorough data exploration.

But the way we’re exploring the data hasn’t really changed in the last 50 years. We’re trying to explore ever expanding and interwoven data sets using the same tools and techniques that we use to create dashboards and reports. In fact, in our rush to get AI into the business we may be limiting our exploration more than ever before. We’re starting off on the wrong path from the beginning and every step in the process takes us further off course.

According to Gartner, most organizations are stuck in experimenting with AI, struggling to create a stable AI strategy.

Where is data exploration going wrong?

With all of this data at our disposal we should be able to investigate problems from every angle, but we don’t. Every analysis starts with a hypothesis that is used to narrow the scope of the exploration. From the data points we include in our data set, to the questions we ask of the data, to the conclusions that we spot, the hypothesis is in the driver’s seat.

Given the limits of the analytic tools we had, it’s no surprise that analysts and data scientists have taken to using informed hypotheses to limit the scope. But hypothesis-driven exploration has always injected some risk and, as AI has amplified the potential uses of data, these risks have been amplified as well.

Because exploring every relevant attribute is too time-consuming, the hypothesis is used to determine what is included, and what gets left out.

Given the time and effort data exploration takes using traditional BI tools, it’s not a surprise that data scientists and analysts have leaned on hypotheses to keep scope in check. It’s also just human nature–you have to focus your attention somewhere and it’s hard to set aside your theories to explore with an open mind. Even data scientists who develop AI algorithms to explore data will be directing their exploration using hypotheses to some degree or another.

But there are real risks to allowing a hypothesis to direct exploration:

• Missed Opportunities
  You could be looking in the entirely wrong direction—what the problem is or how you should go about solving it.
• Underwhelming AI or Meaningless Insights
  Your exploration may yield some insight, but if it’s not insight about the real issue then any action you take will have weak or non-existent results.
• Overlooked Risks
  You may have left out data that pointed to big issues, or that could have left you to draw completely different conclusions. That means the real problems are left unchecked.
• Biased AI
  Conversely, limiting exploration could allow some data sources to have an outsized impact on the results.

When you build on a narrow foundation, your entire project is at risk of collapsing.

Examples of AI Gone Wrong

AI failures can lead to catastrophic losses, lawsuits, or reputational hits. Getting it right from the beginning is critical!

• Zillow’s home-buying debacle shows how hard it is to use AI to value real estate
• Payout for Estée Lauder women ‘sacked by algorithm’
• The Dutch Tax Authority Was Felled by AI—What Comes Next?
• Is Airbnb using an algorithm to ban users from the platform?
• Sexist AI: Amazon ditches recruitment tool that turned out to be anti-women
• When It Comes to Health Care, AI Has a Long Way to Go

The Talent Crisis

The lack of data scientists is hindering AI innovation and all the more reason to invest in Intelligent Exploration.

The number of data science job openings in 2022 is over 70% higher than in 2021¹ —and the year isn’t over yet. In short, data science teams are maxed out. They can’t afford to go down rabbit holes looking for the next big AI opportunity when they’re maxed out on basic requests.

Intelligent Exploration amplifies the power of your data science team. As author and data scientist Tobias Zwingmann expressed in a recent webinar, AI can be a powerful co-analyst. Use it to find opportunities, root out bias, and test results. You’ll get better results and put them into the hands of users while they’re still relevant.

Hear more from this conversation

Intelligent Exploration can explore vast amounts of data at once so you can explore all relevant attributes and ensure that you’re getting to the truth of the matter.

Unlock Potential with Intelligent Exploration

It’s time to change how we interrogate data. Our world is interconnected and we can’t afford to narrow the scope of our exploration. Intelligent Exploration—the use of AI to explore and visualize, and mine it for insight—must be the start of any data-driven initiative.

Intelligent Exploration creates a more complete picture and facilitates better decision-making because:

• It can comb through complex datasets so that there’s no need to impose our own thoughts or assumptions on what data should be included.
• Intelligent Exploration doesn’t just look at more dimensions, but it can look at the many possible relationships between those dimensions.
• It cuts through the noise of complicated data and pulls out the significant insight, so data science teams know where to focus, instead of spinning cycles trying to find the ‘Aha!’ insight.
• AI has no preconceived ideas about what trends or relationships it will find, keeping human bias out of data exploration. This means that Intelligent Exploration may find relationships that people won’t even think to look for.
• Data quality is the single most important factor in an AI model. Every model that has gone off the rails has failed because it relied on either the wrong data or weak data. Intelligent Exploration is ideal for assessing data quality effectively and efficiently.

How Virtualitics’ Intelligent Exploration changes the game

When you explore your data using tools that restrict your breadth and depth of exploration, you’re automatically limiting the value you’ll gain from your efforts. You also miss out on opportunities for AI because you can’t see what relationships are most impactful.

Most organizations are using BI tools meant for reports and dashboards–providing snapshots into the current state–to do complex exploration intended to uncover opportunities to be predictive and prescriptive. Virtualitics is designed to do the kind of complex exploration on which organizations can build successful AI programs: