Mar 01, 2015

Be Intentional

No matter what business publications you read, not a week goes by without seeing an article about the value of data in business. In DCR TrendLine, we’ve published many articles about the benefits big data analysis provides for hiring managers and executive leadership. Similarly, decision making has been a major subject of study in recent times, possibly due to the explosive growth of big data over the past ten years.

Not surpisingly, “data-driven decision making” has become a key buzzword across a range of industries, and is considered a promising application of data science. Foster Provost and Tom Fawcett published a well-regarded article entitled “Data Science and its Relationship to Big Data and Data-Driven Decision Making”, defining “data-driven decision making” as “the practice of basing decisions on the analysis of data rather than purely on intuition.” A study from the MIT Center for Digital Business found that organizations driven mostly by data-based decision making had 4 percent higher productivity rates and 6 percent higher profits.

Benefits of Using Data in Making Decisions

Benefits of Using Data in Making Decisions

Source: Teradata

According to the Wall Street Journal, decision making lies across a broad spectrum. At one end are operational decisions, which are generally structured, routine, and short-term oriented. On the other end are strategic decisions that tend to be complex and unstructured because of the uncertainty and risks that typically accompany long-term decisions. In between are different kinds of decisions, including non-routine ones in response to new circumstances beyond the scope of operational processes and tactical decisions to deal with adjustments to longer term strategies.

Since IT and data analysis have a structured nature, they have long been applied to automate routine, day-to-day operational decisions. As companies gather more data and analysis becomes more sophisticated, these decisions can be made with little or no human intervention. However, beyond automated operational decision, there are many situations where human intervention is required.

Strategic decisions are often geared at seeting the long-term direction of a company. The use of big data in helping with strategic decisions is still in early stages, so experts recommend that executives should use a framework to facilitate them in seeing things from new viewpoints and to aid in the assimilation of complex concepts. A good solid framework helps leaders determine the context for making strategic decisions in an ordered manner.

Steps to Make Data-Driven Decisions

According to Priyanka Jain, CEO of Aryng and former Head of Business Analytics at PayPal North America, data –driven decision making requires coordination between analysts and stakeholders. Her recommended framework for decision-making consists of 5 general steps.

  1. Understand what the real business question is, including the context and the impacted segments.
  2. Create an analysis plan with a hypothesis, and decide what methodology to use (correlation analysis, profiling, predictive anlaysis).
  3. Collect data.
  4. Gather insights.
  5. Make recommendations, both for the technical aspects and the soft aspects, such as building alignment and communication.

Data-Driven Decision Making in HR

Talent analytics is a key driver in software acquisition for the HR function. HR organizations are being called on more often to provide instant statistical data and analysis to support strategic business decisions. As HR organizations aim to advance their analytics capabilities, they are increasingly looking for new technologies. According to the research of Bersin by Deloitte, HR users consider analytics and reporting capabilities as the number one criterion in their buying process. 

“Organizations are struggling to build their internal analytics skills and are looking for answers in technology. In fact, half of human capital management technology buyers cite analytics as the top driver of their purchase decision. However, technology alone is not the answer. HR organizations need to know how to glean from what may be a morass of statistical data the information they need to make meaningful change.” ~Karen O’Leonard, Vice President of Benchmarking and Analytics Research at Bersin by Deloitte