The Analytical Concepts Every Professional Should Know.
By Vanessa Richardson
While many professions do not require expertise in data analytics and statistical measures, being an educated and regular consumer of data helps us to get a clearer picture of three things – ‘what has happened’, ‘what is happening’ and ‘what might happen’. Statistics and analytics are not simply about deriving insights from the past, they also allow predictions about future outcomes and the optimization of business resources. Statistics have become so prescriptive that if analyzed properly have the ability to significantly drive and support an organizations decisions and needs.
Tom Redman, author of Data Driven: Profiting from Your Most Important Business Asset, there are essentially four analytical concepts every manager should familiarize themselves with – randomized controlled experiments, A/B testing, regression analysis, and statistical significance. Simply taking the time to learn and understand the basics of these four concepts can drastically improve direction. If used appropriately can even help predict human behaviors.
Randomized Controlled Experiments – in short, this aims to reduce bias. Randomization minimizes selection bias and provides a solid control group. This type of data collection is considered to be the gold standard, especially when new products are tested. Many when they hear this terms think of clinical trials however; it is particularly effective when gathering data on things like whether a customer service intervention, improved customer retention, or whether a new (costly) piece of software is more effective than one currently in use.
A/B testing – this is the most common type of testing done and at the most basic level is just way to compare two versions of something to see which performs better. A/B testing is used to evaluate everything from product design to marketing. The critical piece in A/B testing is making sure your test ran long enough to give you enough data to properly analyze.
Regression analysis – once you have the data you need; regression analysis can help make sense of it. Of course, ways in which to analyze are endless but linear regression continues to be one of the most important. It uses math to determine if there is a relationship between to variables. For example, is there a relationship with the month of January and the number of gym memberships sold. Regression analysis assists in determining which factors matter most, which can be ignored, and how do they interact with each other.
Statistical significance– this is exactly what it means. However, it is also the concept that is most misused and misunderstood. It is during this phase when statistics are meant to either prove or disprove. Statistical significance is what both science and politics relay on. It is what should help you quantify whether your results and are likely due to chance or the exact factors being measured. It is during this phase when experience and savviness come into play and when you can become creative in your conclusions.
Data analytics is ultimately about making informed decisions. It does not matter what your area of expertise or role. They are merely designed to help us do achieve the goal of staying ahead of ourselves.
Integrated People Solutions is an executive placement firm in Denver, Colorado, and is part of the Kennedy Executive Network
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