Why Mainstream Organizations Struggle With Becoming Data-Driven and How Decision Management Can Help Them Achieve Value Faster

James Taylor

Traditional Fortune 1000 companies are waking up and smelling the coffee. After watching data-driven technology leaders like Amazon, Facebook, and Google rapidly gaining market share and valuation, they’ve come to realize that they too need to start investing in data and artificial intelligence (AI) solutions to stay competitive. NewVantage Partners, a strategic advisory firm, reports that 99% of Fortune 1000 companies say they have been investing in data and AI initiatives—for as long as three years and to the tune of more than $50 million in most cases—but they are struggling to derive value from their attempts at digital transformation.

Industry thought leader Randy Bean, writing for the Harvard Business Review, provides some insights into why this is in his article, “Why Is It So Hard to Become a Data-Driven Company?” As he points out, progress toward digital transformation has not only been slow, it is actually behind the curve compared to last year—in spite of the financial commitment behind the effort.

Bean cites the survey findings:

  • Just 29.2% of companies report that they achieved transformational business outcomes.
  • Only 30% say that they have developed a well-articulated data strategy.
  • And this year, 24% of participants thought their organization was data-driven—a decline from 37.8% in 2020.

This begs the question: Why are these organizations going backwards, despite their best intentions? Perhaps, not so surprisingly, it’s not about the technology. It’s about cultural resistance, says Bean, which is what executives of mainstream companies have reported for five years. In this year’s survey, 92.2% say that cultural challenges are the primary obstacles to transformation. Bean points out that these challenges revolve around organizational alignment, business processes, change management, communication, people skill sets, and resistance or lack of understanding to enable change.

Bean has three practical remedies for corporate leaders who find their organizations in this situation:

  1. Define critical business problems or use cases and focus data initiatives on solving or improving the outcomes. This will enable quick wins to demonstrate value and build credibility.
  2. Think of data as a river that flows throughout the organization, and critically examine how this business asset is managed, consumed, and used.
  3. Be patient, and commit to stay in it for the long haul. Digital transformation is a gradual process that requires a major cultural shift that can be helped along with data literacy and data awareness programs.

In spite of the setbacks, organizations are making big strides and are taking digital transformation seriously. As Bean points out, appointments to the role of a Chief Data Officer increased from 12% in 2012 to 65% in 2021. And 81% of executives surveyed in 2021 feel optimistic about deriving more value from their data and AI initiatives.

These are all positive signs, but there’s a lot of work yet to be done. That’s where Decision Management can offer a big boost.

Data-driven decision making is key

Bean makes a valid point about defining business problems and desired outcomes. We at Decision Management Solutions believe that, to truly become data-driven, you need to begin with the decisions you wish to improve and get a sense of what that means for your business before you look at any data. All too often, we’ve found that companies lead with the data and try to develop data products rather than decisioning products. Putting attention on decision-making and on what a better decision looks like keeps the project focused on creating business value, not on using data or adopting the latest ML or AI technology. For a deeper dive into this topic, check our white paper, “Building an AI Enterprise.”

Aim for continuous improvement

We agree with Bean that value is going to come over time rather than as a single “big bang”. But rather than just being patient and letting the process unfold as it may, the winners in this game can explicitly focus on gradual, systematic improvement in decision-making. To start with, they don’t necessarily have to aim for the ultimate solution that will provide optimal results. The best plan is to keep it simple, show it works, understand how your decision-making works, see how data can be used to improve results, and then progressively build on the wins, fine-tuning along the way.

Begin with the decision in mind

Our take on the accessibility of data across an organization is a little different. We find that this has little value unless you first assess the quality of decisions you’re making with that data. Just because everyone is using data doesn’t mean they are using it effectively or to drive positive business changes. It’s critical to identify the decisions being made, measure the results, and continually improve upon them.

And, while data literacy programs are certainly a good starting point, you actually need to change the mindset in your organization to be about making better decisions—not just steeping your stakeholders in data. By understanding their decision-making first, they can make the best use of the data to improve it.

Its time to put DecisionsFirst™

For more information on how you can put decisions first, accelerate your transformation to a data-driven business and watch the wins add up to significant, positive business outcomes, contact us.

 

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