Being Data-Centric Won’t
Transform Business – Here’s What Will

By Greg Silverman, July 30, 2019

Over the last decade, organizations across industries have been on an analytics journey with the aim of helping business decision makers reduce uncertainty on what to do next. From dashboards and visualizations, to data pipelines and reports, teams have invested heavily in promising new methods and tools, and yet the majority of companies still report an inability to truly deliver on business needs. In fact, despite data and analytics being a top investment priority for CIOs, Gartner research reports only 9 percent of organizations have reached a “transformational” level of maturity in data and analytics.

So what’s holding data and analytics back from becoming truly central to business strategy? In short, it’s complicated. Business decision makers are getting different reports with different answers, making it hard to trust the insights—in part because analytics are siloed, restricted by rigid corporate structure and workflow. Meanwhile, the bulk of reports are still backwards-looking, with static inputs and outputs; they give business leaders a good sense of what happened in specific, past scenarios, but not what to do next. Bottom line: Despite the wealth of data available, the majority of business decision makers receive reports that aren’t actionable. They’re left guessing which method, and what next step, is the best choice.

 

In order to be successful with data and analytics, the goals are clear. Many talk about the importance of generating forward-looking insights, taking a business-first approach, building a data-driven decision making culture and building a more agile analytics process—but aside from organizational overhaul, how can companies take on these challenges? I believe the first step to change is looking at data in a new way. In order to be successful, leaders must shift from data-centric thinking to an outside-in approach to predictive analytics.

 

Here’s why an outside-in approach—also called market-centric analytics—has the potential to transform business:

“In order to be successful, leaders must shift from data-centric thinking to an outside-in approach to predictive analytics.”

Instead of starting with data, an outside-in approach requires that organizations start with the person and market they’re trying to understand. An outside-in approach pushes business leaders to unpack the dynamic relationship that consumers have with their business, asking questions like: Who interacts with your company? How? Why?

The world is full of uncertainty—new events happen, markets change—but people are the most unpredictable variable of all. When you think about it, it’s people that make up markets, and people aren’t always rational actors. Understanding how people think, feel and make decisions—how they react in various, sometimes unexpected situations—is immensely powerful in reducing uncertainty about the future.

Organizations are turning to agent-based modeling to simulate the actions and interactions of consumers, to assess their impact on a given system. When combined with behavioral economics and network science, this is called market simulation, which allows teams to better predict the outcome of a given business action on both their brand and their competition. Ultimately, a deep understanding of a market is at the cornerstone of any successful business strategy—and that understanding starts with consumers.

Market-centric analysis offers a holistic approach

Historically, analytics has been largely data-driven, data-first. Businesses begin with a single question and immediately look for the right data to generate an answer. This process is a common one, but the output is narrow, often leaving business users with more questions than answers. Where for a long time, technology limited organizations to this kind of analysis, recent advances in computing power and machine learning have made it possible to simulate how entire markets behave.

By accounting for how various types of people make decisions along with competing brand activity, market simulation gives business decision-makers the ability to look at the big picture first, rather than looking at a specific question in isolation. This process brings multiple data sources together, accounting for the cause and effect of numerous variables. Ultimately, this allows the analytics team to more accurately answer not just one, but hundreds, of what-if questions.

Data-driven insights and domain expertise are often at odds, leaving business decision makers to either set aside their instincts or act entirely on their intuition. This can lead to missed opportunities or decisions that can be costly if incorrect. What’s more, misalignment between insights and expertise often generates distrust in analytics. Analytics practices can be a black box, but by allowing for the incorporation of domain knowledge, or datasets of differing granularities, a market-centric approach gives business users more insight into the data powering a model.

 

The outside-in approach also presents opportunity for collaboration, and ultimately, the most impactful, trustworthy insights are rooted in interdepartmental partnership. Analytics teams and business users that work together to incorporate industry knowledge into models are more likely to produce meaningful insights that demonstrate a deep understanding of the market and its dynamics.

 

The leap from data-driven to a holistic, outside-in approach requires a company-wide culture that embraces open communication and collaboration. But there’s also a need to rethink our approach to analytics, putting people and the way they influence each other at the center. Analytics teams can drive this shift by working closely with their business counterparts to understand company priorities. Ultimately, this cultural shift will result in more strategic business decisions that deliver real ROI.

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