“You can’t manage what you can’t measure,” as management consultant Peter Drucker famously said. As we advance further into a data-centric era, it is difficult to overstate the importance of a proper data analytics practice in providing this measurement. However, to remain competitive, it is not enough to simply collect data in the hope of generating value, as this can ultimately lead to additional cost and missed opportunity.
Start with a well-defined problem and a diligent approach
Regardless of the industry or the size of the enterprise, it is crucial to begin with a well-defined problem and then figure out how to best obtain data to support a solution. With more data almost always being preferable to less, this process can sometimes feel burdensome – with one survey finding that data preparation accounted for about 80 percent of the work of data scientists. Yet, a diligent approach will pay dividends. Often, the data exists in many disparate systems, and successfully implementing a data-centric architecture leads to drastically reducing the time and effort for accessing, aggregating, and analyzing the data necessary to drive your outcome.
Unfortunately, most data analytics practices, from Fortune 500 to mom-and-pop, fail to follow an outcome-based approach. In a recent survey, 77 percent of respondents said that “business adoption” of big data and AI initiatives continued to represent a challenge for their organizations.
Most traditional methods focus on capturing and storing large quantities of data rather than high quality data, and attempting to identify the opportunity that exists within the existing data. This creates a siloed structure within an organization, leading to inefficiencies in data discovery, potential for redundant data, and a lack of appropriate governance.
Another drawback is that typically reports are generated based on initial assumptions, and as the project continues, there tends to be little continuous improvement or validation of these reports. An ongoing objective is missing from many projects, and leadership often lacks visibility, failing to provide feedback to the analyst. As a result, there is insufficient analysis to drive decisions around the organization’s key objectives.
Use data analytics to solve a problem
A better approach is first to identify a problem that could be solved leveraging data and analytics. Targeting a specific outcome – such as revenue growth, indirect costs, customer growth, or customer retention – has the added benefit of increased visibility within an organization. This approach highlights impacts on key objectives (aka outcomes). Often, leadership is aware of the problem or initiative. This can grant additional support from multiple avenues, one of which is continuous feedback into the model.
Once the problem has been properly identified, is it important to define the solution. For example, if an organization is struggling with costs, what is the best statistical model to identify the most effective action step? Rather than deciding at this point, further investigation of the desired outcome is typically required. If, after digging deeper, a large expense is found to be related to customer acquisition, further investigation could lead to uncovering a customer churn issue. At this point, framing the solution is a realistic possibility.
Too often, organizations accrue additional costs for storing low-quality data and attempting to organize and leverage the data, evaluating whether it can useful. It is also common to discover a disconnect between the production of reports and the needs of stakeholders. However, concentrating the data effort on the desired outcome provides guidance for data collection and increases the value of data collected. Focusing on the outcome also allows for a more efficient process for execution, measuring the results and gaining feedback from the target audience. This facilitates a more natural evolution of the solution and a continuous improvement process.
Achieving real, measurable outcomes from your organization’s analytics practice is pivotal to remaining competitive in today’s data-centric landscape.
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