A Cautionary Tale: 3 Reasons HR Analytics Projects Can Lead to Frustration and Failure

3 Reasons HR Analytics Projects Can Lead to Frustration and Failure
Human Resources

Nothing frustrates me more than to see the expertise, experience and time of HR professionals wasted. And in today’s working environment, I see frustration and failure all too frequently in Analytics projects.

When I say frustration and failure, I refer to the type of Analytics project we have all been involved in. We have pored through oceans of data and done hours of spread sheeting and analysis, and in the end the leaders we have presented our analysis to have put it to one side or seemed confused or unimpressed by our efforts. Somehow we have missed the mark. Or the leader takes one look at our analysis and demands something different – other numbers, more numbers, different charts, flashier graphics – take your pick. And we head off into another round of more effort and increasing frustration.

Why does this happen? Is there too much data? Sometimes. Too much complexity? Possibly. Too little time? Maybe. When these and other analysis challenges arise, it can often lead to the frustration that we have invested time and effort for little or no return. And our managers or leaders are equally frustrated because they are not taking away the insights they need to make informed business decisions.

To make the most efficient use of our time and to increase our chances of success, I’m going to briefly outline three problems that can cause HR analytics projects to stall or go completely off the rails and hopefully point you in directions to ensure that this doesn’t happen to you.

The three reasons for frustration and potential failure that I will explore are:

  1. Poorly defining the problem we are solving and the outcome we want
  2. Not understanding the story the numbers are telling us
  3. Too much data

These problems are all avoidable, if we are aware that they may occur. I will outline the problems and some ways to overcome them below, but this is a very brief article. If you want more insight and practice in these areas of analytics, please join us for the Queen’s IRC HR Metrics & Analytics program. In the program, we provide in-depth strategies, tools and templates, and real world applications to help you to directly address these challenges.

For now, let’s look at these three common reasons HR Analytics projects fail.

1. Poorly defining the problem we are solving and the outcome we want

The purpose of analyzing data is to help us make decisions to solve problems, capture opportunities or monitor and manage potential risks. In the best scenarios, we use data to run a more effective and successful business or organization.

And while it may be obvious to say, data doesn’t make decisions, people do. We do analysis to help ourselves and our leaders make better, more informed decisions.

Thomas Hobbes, the philosopher, once stated: If we agree definitions, we end most arguments. To this, I will add: if we agree definitions, we get to better outcomes, we get to them faster and we get to them with much less effort and frustration.

Too often requests for analysis are poorly-defined or not defined in collaboration with the person who will be using the analysis to make decisions.  “Why does it take so long to hire? Bring me numbers on recruiting!” “Money’s going to be tight this year end. Analyze the data and tell me how much money we should allot for bonuses this year.”  “I want to know if all that money we spend on training is delivering value. Bring me some numbers.”

We can all bring opinions on these issues, some of it informed, some not, but we will each have different ideas on what defines time-to-fill positions, a “reasonable” bonus structure, and value for money in training initiatives. In order to carry out effective analysis, we need to agree on the definitions of the key elements of a project, report, or dashboard with our sponsor or leader before we launch into gathering data and conducting our analysis.

To create effective analysis, we need to define a number of terms or elements. I’ll note them briefly here:

  • The problem we are solving.
  • The outcome we are looking to achieve.
  • The scope or population we are addressing.
  • The timeframe we want to analyze – both the historical past and predictive future.
  • The terminology we are using.
  • The relevant measures that will tell us the story of what is happening and will help us to make informed decisions.

Analysis in most organizational settings is done by one person or a team of people, in this case HR, to provide insight to another individual or team of individuals. In most cases, this will be your boss or a project team or your executive leadership team.  If you sit down for a brief meeting with the individual or team that needs the analysis – it doesn’t have to be long, an hour or less usually does the job – and agree the relevant definitions, there is a dramatically better chance that the analysis will be done in less time, insights will be more focused and meaningful, and decisions will be more realistically informed and easier to make.

Albert Einstein once said: If you have 20 units of time, spend 19 of them defining the problem. If the problem – and the other terms or elements identified above – are defined together by HR and the business leader who owns issues and the decisions, then the odds dramatically increase of using data in an intelligent, time-efficient way to come to better decisions and more sustainable solutions.

2. Not understanding the story the numbers are telling us

A very wise and senior HR leader that I know explained to a group why he liked the term “Human Resources”. He said that organizations had to use their Resources wisely – money, capital, systems, plant & equipment, patents, etc. – and so they had to know if they were being effective in their decisions and investments. This, he said, is the Resource part of Human Resources, and tends to be what the organization measures and analyzes (particularly the money ($$$!) part). But in HR, he said, it is our job to balance the Resources side of the equations with the Human side of the equation.

The point here is that, as HR professionals, we need to deeply understand both sides of the equation, the Resources ($$$!) part and the Human part, if we are going to be able to provide wise counsel and timely insights.

And this brings us to stories. Any insightful analysis is a combination of Numbers & Stories. If you stand back for a moment and consider any analysis you have been conducting, every number has come from a combination of human decisions and human actions. And every solution to improve those numbers will come from human beings understanding the story that underlies the numbers and making Human and Resource decisions to improve that story.

Numbers are the language of business. To understand the story in the numbers, we need to understand the business, how the business uses its resources and the numbers that measure and score the results. Our recommendations and efforts need to help the business to drive successful, sustainable results. To do this, we first need to recognize that a successful business leader focuses on three key issues: How do I grow revenue? How do I reduce costs or make sound investments? And how do I identify and manage the risks that are inherent in making more money and spending less?

Revenue. Cost. Risk. Any HR practice or issue can be linked to one or more of these business results, these business stories. What is the impact of sales training on raising revenue? How can we reduce risk if we fill empty positions sooner? How will that impact our revenue? What will be the impact of multiple retirements? What are the levers or drivers that will encourage employee engagement and greater productivity? How can we restructure a department or division to take out cost? If we understand the business issues – and define the key terms with our business leaders (see above!) – then we can begin to understand the numbers and how change initiatives and HR practices can help to move those numbers in a positive direction.

Numbers and stories. We need the relevant numbers and we need to understand the story that those numbers are telling us.

3. Too much data

Data is best used for two purposes: to identify opportunities for improvement and to monitor and manage risk. The reason we measure and analyze is to make better decisions or to identify areas of unacceptable risk.

Here is the ever-present danger that exists in today’s working environment. Because of the proliferation of intelligent data-capturing technologies, we are, figuratively speaking, DROWNING in data. And some days it feels like we are literally drowning in it.  To compound the problem, because the sources of data and the amount of data keep expanding, we lose confidence in the numbers we are looking it. (And I can already see other hands waving in the back: What about corrupt data? Incomplete data? What about systems that don’t talk to each other? What about huge gaping historical holes in the data? Or departments that input incomplete data or no data all? These are all very real problems that each organization needs to address, but for right now, for this article, we will only deal with having too much data. One tsunami at a time…)

So, how do we address the challenge of too much data? Remember, not all data is created equal. Data and analytics do not solve our problem – they help lead us to insights so that we can make informed decisions on how best to move forward. I have two recommendations before you start to wade through the oceans of data. Go back to the two points discussed above:

Recommendation #1: Work closely with your business leaders to define the problem they are facing and the outcomes they need.

Recommendation #2: Understand the story of what the numbers are telling you so that with the business leaders you can choose the relevant data to measure and analyze.

In the end, all three problems are connected. If we haven’t defined our problem and our desired outcome, then we don’t know what story the data is or might be telling us. Without having the clear, agreed definitions and understanding the story, we can’t determine what data is relevant and what isn’t and we quickly become overwhelmed by the amount of data available.

I encourage you on any project, but particularly where analysis is required, to work closely with your business leaders. Together with them define your terms, understand the story, determine the relevant data and from your analysis find the insight and benefits to help them make sound business decisions. And in the end, I can only hope that you can profitably use these ideas to avoid frustration and failure!!


About the Author

Jim Harrison

Jim Harrison is an international consultant and facilitator focused on strategy, sales and talent management for mid-sized to large organizations, including government, public service and healthcare organizations. He started his career in financial services, working as a money trader for RBC/Dominion Securities.  He has over 30 years’ experience in consulting, training, and executive coaching. He works with clients in North & South America, Europe, Australia, and Asia, and regularly facilitates strategy and training sessions for such well-known companies as IBM, Accenture, PwC, KPMG, Deloitte, Fuji, AGFA, TD Bank, AT&T, Deutsche Bank, and HSBC. Jim received his B.Sc. degree in Finance from Florida State University and a Master’s Degree in English from the University of California, Irvine.

Jim teaches on the Queen’s IRC HR Metrics and Analytics and Linking HR Strategy to Business Strategy programs.


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