Analytics have now permeated into all levels of any company’s organizational hierarchy, creating exciting new opportunities but also some interesting challenges.
Not all businesses are equipped with data scientists and analytics experts to help them navigate what can seem to be a flood of information. The challenge companies face is making data accessible to a range of stakeholders who have little to no experience in data science so that organizations can derive insights and the greatest value from them -- without overwhelming staff.
There are several steps businesses can take to foster a culture that habitually and successfully maximizes the benefit of analytics at all levels.
Analytics can seem intimidating simply because of the massive amount of data that is at our fingertips. I often liken it to an author working on a novel: the hardest step is usually getting started. Being inundated with data from every angle makes it difficult for an everyday user to get started gleaning insights from analytics.
In order to overcome this challenge, it is important first to define the question to investigate -- essentially starting with the first step of the scientific method. By defining the top priority question, the focus is narrowed, eliminating swaths of data that do not contribute to answering the question. This first step is the most important, and the key question can often be determined by the people on the front lines in the organization on a daily basis. They have much greater insight into the question or questions that need to be investigated first.
Even though the people working on a particular question may not be analytics experts, it is essential to understand the source of data and the methodology used to collect and organize it. This is where communication becomes critical. Think of this as research that authors do to equip themselves to best write their novel.
When presenting results either internally or externally, many questions are sure to emerge about how a particular insight was produced and what data was used to produce it. In order to answer these questions, business leaders must work to bridge the gap between themselves and data scientists -- what the MIT Sloan School of Business calls an “interpretation gap.”
Ask questions until everyone involved is clear that the data being produced are applicable to practical, real-world scenarios. This will produce more reliable results that business users can lean into with greater confidence.
Companies must remember that analytics are not a substitute for the intimate, first-hand understanding of a business, referred to as domain expertise. The Boston Consulting Group defines domain expertise as “superior knowledge and insight into a business or category,” and goes further to state that domain experts “use this insight to spur innovation, to see through complexities, and to imagine what could be.” Data are not a replacement for domain expertise but rather complementary to it.
This distinction is key to avoid falling into the trap of viewing analytics in a vacuum. If something doesn’t seem like it adds up in the data, apply domain expertise and ask questions until you have a clear understanding. As Florian Zettelmeyer of the Kellogg School at Northwestern University points out, “Knowing what you know about your business, is there a plausible explanation for that result?”
This is where authors combine research with their own experiences to craft a story with a logical plot that will appeal to readers. Companies cannot rely on data or on their own observations alone to guide actions but should use the results of both to form a holistic view of the business or the particular question they are trying to answer.
Success in applying data-driven insights relies on taking action. But it is important for business leaders not to feel the need to create action plans that completely overhaul or revolutionize their organization -- instead focusing on more granular goals.
McKinsey makes this point: “The impact of ‘big data’ analytics is often manifested by thousands—or more—of incrementally small improvements. If an organization can atomize a single process into its smallest parts and implement advances where possible, the payoffs can be profound.”
Too often, companies ignore the value of small improvements and the potential for exponential payoffs that can result from building on the foundation of these minor victories over time.
The real key to success in applying everyday analytics comes down to one thing: engagement. Leaders and their teams that are simply engaged with analytics are already ahead of the field. This is true of any facet of business, but especially so of analytics. The goal in the end is to use data to make better decisions and to increase efficiencies within an organization.
Producing analytics must not be a goal in and of itself. The author does not publish a novel simply to do so, but to delight readers and to make a contribution to society. On an organizational level, analytics are really not so different.