Predictably irrational by Dan Ariely
Really enjoyed this book. As the cover puts it …. “In a series of illuminating and groundbreaking experiments, behavioural economist Dan Airely demonstrates how expectations, emotions, social norms and other invisible, seemingly illogical forces skew our reasoning abilities. Not only do we make astonishingly simple mistakes every day,,but we make the same types of mistakes. We consistently overpay, underestimate and procrastinate. We fail to understand the profound effects of our emotions on what we want, and we overvalue what we already own. Yet these misguided behaviours are neither random nor senseless. They’re systematic and predictable."
Not going to argue with that - fascinating read,
The book is full of examples of intriguing experiments that have been run. In one case undergraduate students were recruited to take part in an experiment. In the first part they were to solve some anagrams. When the'd done this they were told that the experiment had a second part and that they needed to go down the corridor to another room to complete it. What was actually being studied though was simply how long it took them to walk down the corridor to the second room. Some of the participants were given words to unscramble that could be associated with “elderly” - US experiment so examples included Florida, bingo, ancient etc. The people primed with the elderly words had a considerably slower walking speed to the next room than the control group that was not primed in this way!
In another example they explore how satisfaction with your food in a restaurant can be best assured by being the first person to order! ( That way your order is not influenced in any way by what people before you have said).
Big Data by Bernard Marr
The world is getting smarter and big data is at the core, we increasingly leave a digital trail and this can be analysed by increasingly smart analytic software. “Big Data” is often talked about and the huge volumes of information that is being gathered. Arguably though the value is not in the volume but rather in the things that can now be done with that data.
The book provides a SMART framework for Big Data
- Start with strategy - get clear on what you want to achieve, and what questions you want to answer
- Measure metrics and data - understand different sorts of data ( structured vs unstructured, internal vs external etc), think though what sources of data you need to answer your questions
- Apply Analytics - use the appropriate analytic tools to process the data
- Report your results - think through how the data will be visualised, lots of new ways being developed that can be used to enable people to see the data.
- Transform your business and decision making - gain fresh insights into your customers, internal processes, people.
The author has also written “Big Data in practice” - a collection of 45 case studies showing how companies are applying Big Data and analytics to their businesses. Given my focus on the application to HR it was interesting to note that none of his 45 case studies are from that area.
The HR Scorecard Linking People, Strategy, and Performance by Becker, Hustlid and Ulrich
The book focuses on how HR professionals can take a more strategic view of HR and its contribution to the success of the organisation. Issues of alignment and mapping how HR contributes to the business strategy are covered. Important to note that as HR Scorecard is developed this is not a one off activity but rather something that will need to constantly evolve as the needs of ht business change.
They draw a distinction between Lagging and leading indicators. Lagging indicators reflect what has happened in the past, e.g. financial indicators. Leading indicators, unsurprisingly, are things that you can measure now which are predictors of future outcomes, examples might include current customer satisfaction as indicator of future sales. Using lagging indicators is easier but they compare it to trying to drive a car by looking in the rear view mirror.
Another important point they make is the issue of using available data rather than relevant data to drive decisions. Will be tempting to use the data that we are already collecting to base decisions on but this may not be the data that you need. Using convenient data rather than relevant data to drive your decisions may not be a formula for success.
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