IBM

When You Have an Analytic Hammer, Every HR Challenge Looks Like a Nail

Back in 2014, IBM announced a new consulting practice offering several new technology services that would apply big data and analytics processes to human resources problems.

From the above-linked article:

One service, predictive hiring, would use large volumes of behavioral assessments and other employee data to better understand the traits that are characteristic of top performers, and then comb through candidates to identify potential hires.

A predictive retention service would analyze workforce data — exit interviews, for instance — to identify those employees most likely to leave.

What struck me when I first read this article is the flawed assumption that job applicants bring high-performance traits with them through the door, or they don’t. And if an employee is looking to leave the company, it’s due to some shortcomings on their part.

It sounds like an example of Maslow’s law: If your only tool is a [analytic] hammer, every [HR] problem will look like a nail.

confirmation-bias-at-workplace

“We’re here today to identify the unique traits of our top performers.”

Since existing leaders will decide what the “traits that are characteristic of top performers” are, they may well end up defining the ideal employee profile in their own image – a clear example of confirmation bias.

Analyzing the traits of perceived top performers who have a long history with the company runs afoul of survivorship bias. From David McRaney’s blog-turned-book You Are Not So Smart:

You must remind yourself that when you start to pick apart winners and losers, successes and failures, the living and dead, that by paying attention to one side of that equation you are always neglecting the other…

When a company performs a survey about job satisfaction, the only people who can fill out that survey are people who still work at the company. Everyone who might have quit out of dissatisfaction is no longer around to explain why. Such data mining fails to capture the only thing it is designed to measure…

The reality is that workers’ attitudes in the workplace can and do change significantly over time in response to the organization’s own traits, for better or for worse, depending on whether the work environment is proactive or risk averse, collaborative or politically charged, collective or exclusive.

Liz Ryan, former HR VP and founder of Human Workplace, hits the nail right on the head (bad hammering pun; sorry not sorry) in her article:

In order to hit our goals in any organization, we need to build positive energy in the workplace. We need people to be excited about their work…

Can you measure that excitement level? You can’t measure it, but it will show in the results that you do measure, from customer satisfaction to turnover to earnings per share. Anyone in your organization will be able to tell when the excitement level is high, low, or nonexistent. We’d have no trouble reading the energy waves at work if we remembered to stay human on the job.

To her credit, Liz Ryan doesn’t pull any punches in rejecting impersonal, technocratic measurement of employee engagement. I doubt the predictive analytics described above would fare any better with Liz than the hollow ritual of the annual employee survey:

If we really care what our employees think, it’s easy enough to find out… We could ask them how they’re doing… We can be human at work…

We don’t have to insult our employees by having them fill out surveys so the people charged with employee engagement can go to the leadership team and say “Look! The employees are 68% engaged. Look how well I’m doing my job!”

Give up the employee engagement survey, drop the junk-science patina on stupid HR practices and learn how to be human at work. You’ll be amazed how the team’s energy will power your success once you let it start flowing.

Analytics, when created and used appropriately, can be a powerful force for success, but there are also many new technologies that help actively engage employees and cultivate employee positivity and productivity. Gamification platforms are just one such example. Here in Boston, for instance, the WeSpire platform engages and energizes employees around company sustainability and social responsibility programs.

“The best way to predict the future is to create it” is an old saw, but it still rings true – especially when leaders choose to seek out genuine, human interactions and build an energetic, collaborative work culture, which should yield much better employee outcomes and improved individual and team performance.

P.S. – On a related note on hiring decisions, what often passes as “common wisdom” within the HR function really isn’t all that wise. Well said, Natasha Bowman!! ⭐️⭐️⭐️⭐️:

HR-Try-Something-New

 

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Back to the Future of Business Intelligence with H.P. Luhn

When was the term “business intelligence” first coined? You might assume it was first conceived in the late 1980’s; coinciding with the initial emergence of companies offering visual analytic software, but the term was first used decades earlier by visionary IBM technology scientist Hans-Peter Luhn in his groundbreaking 1958 research paper, A Business Intelligence System.

Hans-Peter Luhn’s life work at IBM did not include quantifiable, structured data. That realm of BI would not become practical and cost effective until the advent of mainframe systems later in the 1960’s. Rather, H.P. Luhn focused on the unstructured content of his day: documents, letters, research reports and manuals. So too, the business intelligence system Luhn envisioned focused on computer-automated document processing: auto-abstraction, auto-indexing, selective dissemination of information, and information retrieval.

It’s plain to see that Luhn was well ahead of his time, envisioning critical technology components that set the stage for knowledge management and enterprise search today. And now, Luhn’s insights are more relevant to today’s business intelligence than ever before.

For example, Luhn demonstrated a keen awareness of the roles communication and collaboration play in the effective use of business information. The system would “channel a given item of information to those who need to know it” and find co-workers “whose interests or activities coincide most closely with a given situation,” using action point profiles of each person’s interests and activities. Luhn’s BI system would therefore quickly answer three vital overarching questions: what is known, who needs to know, and who knows what.

Who knows what - 3 key questions

Now over six decades later, it is remarkable how well Luhn’s insights remain a very solid, results-oriented description of BI. What is also striking to me is how early the silos separating unstructured content and structured data emerged.

Business analytics analyst and consultant Seth Grimes, who has written and presented on Luhn’s contribution to BI, sums up this issue of structured and unstructured silos well:

[For decades,] business intelligence detoured around the estimated 80 percent of enterprise information locked inaccessibly in textual form … So BI thrived crunching numerical, RDBMS-managed data… and delivered findings via tables, charts and dashboards that focus more on numbers than on knowledge.

~ Seth Grimes, BI at 50 Turns Back to the Future

Today it is widely recognized that BI based on structured data alone is not enough. Structured data and unstructured content must be integrated and harmonized together to create a complete analytic picture capable of revealing new breakthrough business insights. Again quoting Grimes, “in the last few years, BI has headed back to the future foreseen by Luhn in 1958.”

What is known? Who needs to know? Who knows what? These three timeless, mission-critical questions Hans-Peter Luhn asked in 1958 require unified information — data and content — to fully answer. And these key questions have become easier than ever to effectively answer and act upon by leveraging new digital transformation technologies capable of integrating and presenting analytic-ready data drawn from all relevant data sources, whether structured or unstructured, internal or external.