Data and Analytics Lessons Every Organizational Leader Should Learn

By Mike Schmidt, Director of Industry Communications -

When it comes to navigating the landscape of data and analytics today, success has never hinged solely on the ability to collect information.

Companies that have proven themselves to adept at leveraging data into actional insights, driving business value and - most importantly - improving in the face of organizational change, are poised to get ahead in what now can best be described as the age of artificial intelligence.

"Those who are practitioners or experts in the field of data and analytics must understand that you must be the ones to help your organization figure things out," said Kira Barclay, founder and managing partner of Black Antler Advisory & Consulting, who presented last month at AEM's Market Share Statistics Conference in Milwaukee. Barclay spoke on a number of strategic considerations that organizations of all types and sizes should know and understand to excel in data and analytics.

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Put Strategy First

The explosion of AI in recent years has led many organizations and their leaders to invest in and adopt new technologies. In doing so, some have neglected the foundation of a successful approach to data and analytics: a clear, business-driven strategy.

"For many people within an organization, your business strategy is primarily being established outside of your control," said Barclay. "You've got a CEO. You've got a Board of Directors. You've got an executive team, and they've said, 'Here is how we want to run our business, and here are our goals, and likely there are some clear initiatives.'"

However, it's critically important for companies to align data initiatives with overarching business goals and resist the temptation to chase the latest technological trends, especially without a well-conceived strategy established and in place.

A practical guide for leaders to not lose sight of the big picture includes:

• Assessing: Understand the current state of data, analytics, and AI in one's business.
• Ideating:Generate creative solutions to monetize data and support critical business objectives.
• Mobilizing:Put plans into action by leveraging technology, building processes, and inspiring people.

These are not easy tasks, but they are worthwhile ones for organizations looking to ensure their data efforts remain focused on delivering tangible business value.

Set Reasonable Goals

There's no overstating the importance of establishing clear and realistic data-related goals for building and sustaining progress. Barclay's presentation highlighted "5 Stages of Analytics Maturity," adapted from Davenport and Harris's seminal work, "Competing on Analytics." According to Barclay, organizations must assess where they stand-ranging from analytical beginner to analytical competitor-and set goals that reflect their current capabilities.

In addition to setting reasonable goals, managing expectations as they relate to data and analytics-related success is also vitally important. Company leaders should work to:

• Communicate the slow-and-steady nature of analytics maturity
• Setting achievable milestones
• Celebrating incremental victories
• Supporting and advancing a culture of continuous improvement

"If you don't take the 'bull by the horns' and set reasonable goals for your organization, someone else will set them," said Barclay. "And that someone else that will set them probably has no idea if they are reasonable or valid, or if they even make sense."

Ask the Right Questions (and Solve the Right Problems)

When organizations go about trying to advance or improve, a common issue that arises is problem definition. With that said, Barclay encouraged company leaders to spend most of their time seeking to understand problems instead of blindly advancing potential solutions.

"As organizations, are we measured on activity?" asked Barclay. "Or are we measured on progress. So, solve the right problem. And if I had an hour to solve a problem, I'd spend 55 minutes thinking about the problem, and about five minutes thinking about the solution. When you do the opposite, it tends to lead to some really funky things."

And as it relates to data, organizational leaders must cultivate a habit of asking probing questions to ensure analytics initiatives address genuine business challenges. This discipline prevents wasted effort on solutions in search of problems and ensures that resources are allocated to projects with the highest potential impact.

Closely related to problem definition is the need to resist starting with a solution and then looking for a problem to solve. Barclay cautioned organizational leaders against "solution-first" thinking, which can lead to misaligned projects and missed opportunities. Instead, leaders should maintain a relentless focus on business problems, allowing solutions to emerge organically from a deep understanding of needs.

"Competence and wisdom are two very different things," explained Barclay. "Competence is the ability to solve the problem. Wisdom is knowing which problem to solve. And I know a whole lot of really competent people who are terribly unwise, as I'm sure many of you do as well."

Quality Over Quantity

It almost goes without saying that it's not simply enough for organizations to possess data. Who knows it, what it means, and where it resides are just as important.

That's where data product thinking-treating data as a product with defined domains, governance, and usability-comes into the picture as a best practice. By consolidating data onto common platforms, implementing lifecycle management, and prioritizing use cases, organizations can unlock the full potential of their data assets and avoid a "garbage in, garbage out" approach to analytics.

"I would say this is leading thought," said Barclay. "Some organizations are really starting to invest a lot in data product thinking, do a lot with it, and create tremendous value. But for many other organizations, it's very difficult for them to wrap their heads around this idea."

Experiment Often, Fail Fast

Innovation thrives in environments that encourage experimentation and rapid iteration. Barclay said she advocates for the "speedboat" model-small, agile teams that run pilots and proofs of concept. These teams can move quickly, test hypotheses, and learn from failure.

However, scaling successful pilots requires partnership with IT, which provides the infrastructure and rigor needed for production-grade solutions. Therefore, leaders should embrace a "fail fast, fail often, fail agile" mindset, allocating time and resources to experimentation while maintaining a clear path to scale.

"This means taking a step outside the normal 'stuff' and doing a lot of quick experiments," said Barclay. "And when you fail fast, just move on. And if you do it 10 times, one is going to be brilliant. It's a low percentage, but you must take those risks."

Manage Technology Hype Cycles

The analytics and AI landscape of today is characterized by waves of hype and disillusionment. Referencing Gartner's Hype Cycle, Barclay urged leaders respond by "riding the wave" rather than fighting it. By understanding where technologies sit on the curve-from the "Peak of Inflated Expectations" to the "Plateau of Productivity"-organizations can make informed decisions about adoption and investment.

"Ride that wave," said Barclay. "If you can lean into it, you can leverage enthusiasm for certain technologies in order to get other projects done, that will pay off."

The Bottom Line

Managing expectations internally as they relate to data and analytics is critical. Leaders must educate stakeholders about the realities of technology adoption, balancing enthusiasm with pragmatism.

There is no single "correct" organizational structure for analytics. Whether centralized or distributed, the right model is the one that works for each organization individually. Most importantly, though, all stakeholders should remain flexible and adapt structures to evolving needs and opportunities.

"Ultimately, success comes down to meeting leaders where they are at, and pushing and pulling from there," added Barclay.