Council Post: The Four Pillars Of Data And Analytics Strategy

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Dr. Velkoski serves as Director, Data Science at the National Association of REALTORS® and Adjunct Professor at DePaul University.

It has been nearly eight years since data scientist was declared the sexiest job of the 21st century. As senior professionals demonstrating the talent and creativity necessary to transform raw data into deep, intuitive knowledge, data scientists were poised to revolutionize decision-making and strengthen organizational performance. 

That, largely, hasn’t materialized. According to PwC’s 22nd Annual Global CEO Survey, organizations continue to struggle to extract actionable intelligence from data. Those that participated in the survey highlighted a lack of analytical talent, data silos and poor data reliability as the main causes for the absence of progress. In an effort to keep up with the proverbial Joneses, perhaps we’ve rushed into developing data and analytics initiatives without fully considering the broader strategic implications of those efforts. 

As leaders, we must take a step back to think about the nature of our data and analytics strategy and how it fits into the bigger picture of our organizations. But, where do we begin? I propose a framework that I call the Four Pillars of Data and Analytics Strategy: data literacy, data acquisition and governance, knowledge mining, and business implementation. Each pillar builds upon the others to ensure we’re able to maximize value, while minimizing risk, in data and analytics initiatives. 

Literacy refers to competence. To excel in the modern era, organizations must develop competence in data. We need to educate our workforce, including members of business-line functions, to widely demonstrate an ability to read, navigate, examine and support positions with data. The reason this is important is that we often presume that data literacy is relevant to the domain of the data scientist, but not to that of other functions, which are, most often, those responsible for decision-making.

There are several ways in which we can build data-literate organizations. The most common approach is to establish lunch-and-learns, develop training courses, offer coaching opportunities and provide access to formal educational programs. According to research by Gartner, it’s also important for us to think outside the box and leverage games and quizzes, or otherwise incorporate more creative ways to teach data literacy. Without competence in data, particularly in business-line functions, collaborating on data- and analytics-related opportunities will be difficult, and creating value via those opportunities nearly impossible.

Data is a foundation for developing insight-driven organizations. In fact, much of the recent progress that has been made in our ability to extract insights from data has resulted from acquiring new, more relevant and higher-quality data, and not from improving algorithms. As such, we shouldn’t assume our data assets are fixed. Instead, we should focus on understanding the totality of the data available to us and whether it is the right data to help us achieve our goals and objectives. 

How do we determine the scope, quality and accessibility of our data assets? Methods in data governance and management can help us cross the finish line. Important actions include taking an inventory of our data assets, defining business semantics and a shared business language, identifying the flow of data from system to system, and managing policies for data accessibility and compliance. 

Data acquisition and governance allow us to identify new sources of data, determine the best way to store and organize it, value it, and ensure it is available to those who need it. 

Although data is central to developing insight-driven organizations, it alone is not enough to deliver real-world business value. The knowledge we derive from data, particularly the insightful stories that explain it and what it represents in practice, is the means by which we make it useful. 

The state of the art in this domain is to take advantage of techniques in artificial intelligence (AI). AI has been broadly defined as the science and engineering of making machines that exhibit intelligence — an ability to make decisions, and achieve goals and objectives in the world. Machine learning (ML), which powers “intelligence” in AI applications, helps machines extract patterns from data. 

Through a combination of AI-based services and solutions, data science functions leverage knowledge mining to automate understanding, make informed decisions at scale and otherwise capitalize on data. 

We spend so much time talking about the data, as well as the analytical solutions we build with it, that we often forget about the importance of taking action. Therefore, throughout our journey, business implementation is perhaps the most important pillar to reinforce. Simply put, business implementation refers to augmenting decision-making: using insights derived from data to inform our workforce and empower leaders to incorporate data and insights into the decision-making process. It also refers to making decisions at scale.

It is by way of implementing solutions throughout the business that we close the loop on data and analytics initiatives and, ultimately, deliver real-world business value to our organizations. 

Data literacy helps us set the stage for identifying relevant data and analytics opportunities and implementing insights throughout the business. Data acquisition and governance help us determine the scope, quality and accessibility of our data assets and identify new data that can power our analytical efforts. Knowledge mining helps us capture insightful stories from data and make data actionable. Business implementation helps us take action by incorporating learnings to transform people, processes and decision-making. Taken together, the Four Pillars of Data and Analytics Strategy promises to help us realize the full potential of data and analytics.

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