The Book Look: Quantifiably Better

BookLookQuantifiably Better, by Steve VanWieren, is a short book that delivers a big punch. There are no complex formulas or theorems. Instead, Steve explains why it’s important to put a number behind human resource trends within your organization. He also shows the thought process of quantifying employee productivity and satisfaction through simple formulas and many examples. His analogies and stories make a point while being entertaining; any book that connects analytics to the movie Forest Gump gets my attention! In fact, I would guess that with his baseball player statistics knowledge, he was probably an avid baseball card collector like myself. (Maybe he was the one I traded that 1979 Pete Rose too…)

In a nutshell, Quantifiably Better is written for human resources professionals as an aid to quantifying employee trends. Here’s a quick summary of each chapter:

  • Chapter 1 covers several key principles including the DATA-INSIGHT-ACTION cycle. This cycle is all about collecting data, anticipating what can be solved with this data, and then solving it. Several examples are provided.
  • Chapter 2 explores the seven C’s to measure data quality: Certainty, Coverage, Completeness, Consistency, Currency, Commonality, and Chance. Sample formulas are provided for each one of these that I found simplistic yet useful. I encounter six of these seven C’s on a regular basis during my data modeling consulting assignments. Chance, though, is one that I often face as a publisher, wondering how good the data I am using to make decisions is, and how good those decisions I am making are…
  • Chapter 3 explains the process of manipulating data and gives examples. I like how Steve says we need to create ways to measure things that we thought could not be measured. He provides an example of using surveys in combination with reading facial expressions to determine whether employees are happy.
  • Chapter 4 covers data monitoring and the transformation of data into “smart” data. There is a balancing act between analyzing too much data and too little data – stay away from “analysis paralysis” and set up limits so that only the important results are studied.
  • Chapter 5 focuses on the data and analytics maturity model. Prioritize what is most important to your management and work on that first.
  • Chapter 6 covers employee engagement, turnover, and motivators. Motivation attributes are discussed and ways of creating and using them are explained. Be flexible in the approach taken – each employee requires a personalized experience with their manager.
  • Chapter 7 talks about leadership and the ITEM model. Item stands for: I – IDENTIFY the problem, T – TARGET specific people, E – EXPERIMENT with action, and M – MEASURE the value.
  • Chapter 8 covers the pitfalls to avoid and contains some important advice such as, “Never care about less than 1% of anything.”
  • Chapter 9 provides a project plan you can use. Use the project plan to initiate the right conversations with your leadership team.

Although I do not work in human resources, much of this content resonated with me, and I can see using some of the approaches and formulas for measuring my own performance (both as a data modeler and as a publisher).

Until the next column!

 

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About Steve Hoberman

Steve Hoberman has trained more than 10,000 people in data modeling since 1992. Steve is known for his entertaining and interactive teaching style (watch out for flying candy!), and organizations around the globe have brought Steve in to teach his Data Modeling Master Class, which is recognized as the most comprehensive data modeling course in the industry. Steve is the author of nine books on data modeling, including the bestseller Data Modeling Made Simple. Steve is also the author of the bestseller, Blockchainopoly. One of Steve’s frequent data modeling consulting assignments is to review data models using his Data Model Scorecard® technique. He is the founder of the Design Challenges group, Conference Chair of the Data Modeling Zone conferences, director of Technics Publications, and recipient of the Data Administration Management Association (DAMA) International Professional Achievement Award. He can be reached at me@stevehoberman.com.

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