Is this the “Moneyball” moment for HR & Reward?

Dec 12, 2021 | Analytics

 

 

The movie Moneyball tells the real-life story of how a new manager at baseball club Oakland Athletics, Billy Beane used analytics to make massive improvements in the performance and results. Guys with spreadsheets and maths degrees showed their approach beat that of scouts who for decades had based their assessments of players on their intuitions and seasoned judgements. The Moneyball experience showed that in management thinking, reliance on intuitions alone is like medieval thinking. Analytics is now getting lots of traction in HR, and a rush is on for HRDs to get their teams skilled up. Metrics and people analytics are used in recruitment, L&D, Reward, Diversity and other areas.

But hang on a second – analytics, analysis – what’s the difference?

 

 

Differences between Data Analysis and Data Analytics

 

 

Data analysis is really a subset of data analytics. In data analysis, you investigate, clean, and transform the data in the hopes of finding useful insights, and to make recommendations to help with making decisions. You might use tools like Excel, Tableau Public, Open Refine and others. With analytics, you use data, statistical methods and computer-based modelling to get actionable insights and make better decisions from the data. Analytics is supported by Excel, R, Python, Tableau Public and others. Here are some of the main differences –

 

  • Data analytics involves data collection, then inspecting the data, whereas Data analysis is more about defining data, investigation and cleaning the data without removing outliers or removing any N/A values, transforming the data to produce a useful outcome.

 

  • Being skilled in data analytics implies knowledge of tools to deliver the required actions on the data sets. These include e.g. Python, R, SAS, Tableau Public, Excel and there are many others

 

  • In data analytics, there are various steps on the journey – business case evaluation, data identification, data acquisition, data validation and cleansing, data aggregation and representation, data analysis, data visualisation, and using the results of the analyses. Within these activities is data analysis, which has its own cycle – data gathering, data scrubbing, analysis of the data, and interpreting the data to determine what it’s saying, and to draw conclusions.

 

  • It’s often said that data analysis is based on assessing the past, whereas data analytics is more about predicting the future. So if you are working out what is probable in the future with people analytics, you’ll want to use data analytics. In data analysis, you use a past dataset to understand what has happened so far. Both are useful, and if we can, we’ll want to use both approaches, as one can inform the other.

 

The following table captures some main differences –

 

Point of comparison Data Analysis Data Analytics
Scope Specialised subset of data analytics which organisations use to analyse data and derive insights from it. Usually one time period per analysis General analytics which organisations use to make decisions from data which are data-driven. Trends over multiple time periods
Tools commonly used Tools include Tableau Public, Open Refine, KNIME, RapidMiner, Google Fusion Tables, Node XL Most often SAS, R, Tableau Public, Python, Apache Spark, Excel
Steps/sequence Data gathering, data scrubbing, analysis of data and interpreting data accurately to get a clear understanding what your data want to say. Life cycle includes Business Case Evaluation, Data Identification, Data Acquisition & Filtering, Data Extraction, Data Validation & Cleansing, Data Aggregation & Representation, Data Analysis, Data Visualization, Utilization of Analysis Results.

 

Use/Applicability Perform e.g.  descriptive analysis, exploratory analysis, inferential analysis, predictive analysis and take useful insights from the data Find masked patterns, anonymous correlations, employee preferences, trends and other necessary information that can help to make more effective business decisions
Example Suppose you have a mass of employee engagement data from the past 12 months, and you want to understand what happened so far – use data analysis for that Suppose you have a mass of employee engagement data from the past 12 months, and you want to predict future talent retention – use data analytics for that

 

 

The problem with data analysis is that too often people are asking the wrong questions. Analytics asks different questions for instance –

 

Analysis Analytics
Typical question 1 Do psychometric tests have validity for recruitment? How often have psychometric tests helped this company make effective hiring decisions? Which tests and which employee groups?
Typical question 2 Does our onboarding process help retain talented employees? How often does our onboarding process help retain talented employees?
Typical question 3 Are sales bonuses effective? Do time-limited sales bonuses work well when targeted on particular products/services?

 

 

Why is Data Analytics valuable?

 

 

Asking the right questions can lead to valuable, actionable insights through applying data analytics methods. Obviously, this has to be far better than asking the wrong questions, or relying on the intuitions of experienced managers. These have their place of course, but the time is fast approaching when managerial experience and “intuitions” and “gut feelings” alone will be equated with “flying by the seat of your pants”. Bracket your assumptions. Data and data analytics confer not only credibility and legitimacy to your advice, but help you to surface new insights. They can help you find value where it was previously hidden in plain sight. This clip from Moneyball puts it well –

 

 

 

 

There’s a mountain of hidden value in most data sets that you can tap into. And a lot of it goes against intuitions and “received wisdom”. For instance, it would be valuable to know that sales professionals deliver better results when the company designs time-limited and specific incentives for distinct products and services. Data analytics can help to unearth the best mix of incentives, and help Sales Directors and HRDs to come up with the most effective combination. Analytics might show that using kickers and sales accelerators selectively and with certain teams yields better sales results than when used more broadly. You could speculate on the applications and value that analytics can deliver all day. But the important learning here is that the data can often show that the intuitions of even very experienced managers and directors are wrong, in a way that has direct bearing on how they build their teams. It’s about finding value.

 

Or maybe you assumed that employee promotion for younger workers means higher engagement and lower employee turnover. But when you conduct data analytics across the company, you find the opposite is true, because they don’t plan on waiting around for another 3 or 4 years for the next promotion, so they move elsewhere. You need to run the analytics before you understand the logic of trends like that. Analytics allow HR professionals to find new ways of adding value to the business.

 

This is really at the core of data analytics. Reviewing what actually happened over a large number of (in this example) sales prospecting calls allows the Sales Director to check their intuition. Actually, delivering this kind of analytics work can be quite an easy and straightforward task for experienced people analytics professionals. The possibilities for commercial applications across HR and Reward are very exciting, and this is only the start. Building up the tools and applications for analytics that are more nuanced, repeatable and actionable opens up a range of powerful possibilities.

 

 

 

 

Who is doing people analytics?

 

 

This stuff isn’t new – it’s been around for many years. It’s just that some HR teams got ahead of the curve sooner than others. In this piece, I’m gathering some facts and trends, and putting together a vision of possibility, to show you that there’s an entire category of questions in people management, HR and Reward that have received relatively little attention, have enormous potential to improve company results, and are easy to answer when the right tools are available. In practice, too many people in HR coast on their “judgements”, “intuitions” or “gut feelings” – these self-imposed limitations say something about the state of much of the HR profession. People Analytics can be transformative for the company.

 

The more progressive organisations are already forging ahead and innovating in this space. For some inspirations, have a look at these articles compiled by David Green. Of course, the possibilities for analysing your data along previously unused dimensions. The great thing is that people analytics is that you’re aiming for actionable insights – each finding about your company performance allows you to draw up recommendations for improving along that dimension. You can come up with concrete action plans, informed by the data insights. Improving your company via people analytics allows you to determine the actual (often very specific and unexpected) steps you should take.

 

Analytics have been around for a while, but I feel that we’re almost at the beginning. HR teams looking to give their companies a competitive edge would do well to encourage their managers to think less about “eternal management truths” and more about the patterns that emerge from the analytics conducted on real people performing real jobs.

 

If you want help with Data Analytics, or need practical advice, get in touch via the Contact page.

 

 

 

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