This is still early days.
Let’s all work together.
Thank you.. .. and thanks to HiQ for bringing us together.
Hinweis der Redaktion
(c) Bersin and Associates
10 human capital trends for 2015
Percentage of respondents who say the topic is “important” or “urgent”
1. Culture and engagement – Create meaningful work (87%)
2. Leadership – Develop global leaders at all levels (86)
3. L&D – Reinvent the learning experience (86)
4. On-demand workforce – Engage all workers (80)
5. Reskilling HR – Align HR with business goals (80)
6. Performance management – Shift from evaluators to coaches (75)
7. People analytics – Need long-term commitment (75)
8. Simplifying work – Focus on what matters (71)
9. Machines as talent – Look for opportunities (57)
10. People data – Leverage inside and outside data (52)
Today’s global organizations must navigate a “new world of work” that has turned traditional assumptions about talent management upside down.
In this new world, the barriers between work and life have been all but eliminated.
Talent is in high demand, and many organizations cannot keep up.
Millennials will soon make up 50 percent of the workforce—and they have different values than previous generations do.
From a macroeconomic perspective, the world has entered a period of stronger economic growth.
Data and analytics are key to solving many of the top challenges we identify in these trends: engagement, leadership, learning, and recruitment.
Still too few organizations are actively implementing people analytics capabilities to address complex business and talent needs.
Three in four companies (75 percent) believe using people analytics is important, but just 8 percent believe their organizations are “strong” in this area—almost no change over 2014.
Why is this?
Leading companies are using analytics to gain a competitive advantage by understanding all elements of the workforce, including to:
Understand and predict retention
Boost employee engagement
Expand talent sources and improve quality of hires
Profile high performers in sales and customer service
Yet, our survey confirms that most organizations have been slow to get started, showing very little progress in implementing analytics. In fact, this year’s study shows that there has been little year-over-year improvement in analytics capabilities.
People analytics, a capability built over years, is one of the biggest differentiating factors for high-performing HR organizations today. Without early, substantial investments, it is difficult to get traction. Companies must therefore make a serious commitment to this discipline, search for robust solutions from their core system vendors, and hire people into HR who have an interest and background in analytics and statistics.
In the last 1800s a mechanical engineer by the name of Fredrick Taylor started the datafication of HR. He applied scientific principles to the business of making steel, and performed what are now called “time and motion studies.”
By carefully measuring what workers did, he found that laborers who carried typical pigiron billets which were 75 pounds each were less productive overall than those who carried billets which were 45 pounds. Why? Because the ones carrying the heavier loads had to rest more and ultimately produced less work.
His book, which you can download and read for free, is a fascinating scientific look at the data behind work. Not only did Taylor unlock many secrets to labor productivity, but he also started to understand that people don’t only work for money, but also for psychological reasons – giving early birth to the industry now known as Industrial and Organizational Psychology.
So the origins of using data in HR started more than 110 years ago.
In the early 1900’s when Sigmund Freud was unlocking the secrets of our ego and id, a brilliant psychologist by the name of Carl Jung, who was a friend of Freud, figured out that psychology had a big impact on work.
Jung, who is now credited with the original thinking behind many of the tools we use to assess people, came up with the idea of “social intelligence” – the fact that we actually are all a little different and the way we interact with others can be measured and used to help improve the workplace. Jung created the concept of “types” of people, which was later turned into the MBTI or Myers Briggs assesment in the 1940s – now the most widely used personality assessment in the world. Which is basically a data driven decision-making tool.
At the same time a psychologist by the name of Hugo Munsterburg, who is credited with starting the field of I/O psychology, studied the performance of trolly car operators in Philadelphia and actually created a simulated trolly car to study performance. He learned that selection of people was among the most important things testing could do – and set in place an enormous industry of data collection using testing and assessment.
Hugo Munsterberg publishes Psychology and Industrial Efficiency (1913) Worker Selection – The Trolly Car Drivers
The real explosion of testing started in world war 1, when the US army came up with a test called the “alpha test.” The idea of the “alpha” was to see whether a typical rural young soldier, who may not have been taught to read or write or use math, could learn to shoot a gun.
The alpha test was a simple IQ test and was used to help decide who made it into battle. More than a million young men were tested and this set off a huge data collection process which then moved into business.
Of course this approach to testing then made it into the busienss world and starting in the 1950s companies started many types of testing and data collection about people.
The old “in box test,” which may wife actually took when she got promoted at Pacific Bell, was intended to see how well you could handle the workload of executive or management life. These tests were numerically coded and statistics were used to figure out how well you could manage as a leader.
In the early days these tests and the pre hire testing data was stored in notebooks and analyzed by analysts. But then in the 1960s something big happened: computers.
The birth of the mainframe computer set off a new era of data science in HR. In fact the first application of the mainframe was the US census, which in some ways is an HR data science project.
Within ten years of the birth of mainframes, companies like IBM, ADP, Tesseract, Integral, and later many others started to process payroll records and then store employee data in the computer. Which soon led to huge databases about people.
Nobody was using the data very strategically at that time (except for the Army and a few leading companies) but soon the testing industry, which was evolving on its own, started to develop the concepts of a “pre-hire assessment.”
In 1978 when I graduated from Cornell I interviewed at Procter and Gamble as well as at the US Navy Nuclear Program and took a battery of such tests. In both cases I felt like I had been through a very rigorous evaluation.
So the data collection process in HR got even more advanced and then in the early 1980s another innovation occurred: the emergence of the applicant tracking system.
Originally applicant tracking systems were used to store the deluge of resumes which appeared on fax machines, but soon these software companies realized they could scan the data and actually score candidates.
SO here again, data about people become even more valuable.
We had no idea that these simple systems would later become as powerful as they are today – and this evolution, which started only 25 years or so ago, created the enormous market for pre-hire assessment data, leadership assessment data, testing data, and other forms of people-related data.
The academic world exploded with research and during the 1970s and 1980s and beyond we built an entire industry of psychologists trained in advanced testing and statistics.
One final point. There is an unrefutable history that every business process goes through a 10-15 years maturity of becoming “datafied” once it becomes strategic to the business.
IN the 1970s and 1980s supply chain and integrated financials become strategic and companies spent billions of dollars building ERP systems and financial and manufacturing analytics systems which are now widely used around the world.
In the 1980s and 1990s a similar transition took place in marketing, customer relationship management, and now advertising. These business functions now are very data-driven and we have tools and service providers (and an industry of experts) that know how to collect, manage, and analyze data about customers, buyers, and advertising.
Today, with talent as the #1 priority on the minds of CEO’s, the same pressure is being applied to HR. So in my mind the trend is inevitable.
Our research showed that the hardest part of this process is levels 1 and 2: aggregating, cleaning, and rationalizing data so we have business and HR data in one place.
We just completed two years of research in this area and found that yes, a small number of HR organizations (14% in fact) are well ahead of the curve and have effectively “datafied” their operation.
These unique companies are seeing tremendous improvements in business performance: they are two times better at recruiting, twice as capable of building the right leadership pipeline, their HR organizations are typically 3X more efficient, and their stock prices rose 30% higher than the average of the S&P 500 over the last three years.
The question one has to ask is who are these companies and how did they get here?
Let me start with a little history, to try to explain why the topic of data science is so fundamental to HR itself.