How Big Data Can Make a Big Difference in HR
Big data has become a
necessity for many businesses, but some human resources managers don’t rely on
it because they see their role as something different: connecting to the
employees and the company. In their book, The Data Driven Leader: A Powerful Approach to Delivering
Measurable Business Impact Through People Analytics, Jenny Dearborn and
David Swanson say failure to incorporate data into the HR function can be
costly to managers and the company. Dearborn, chief learning officer and senior
vice president at SAP, discussed the book on the Knowledge@Wharton show, which airs on SiriusXM
channel 111.
An edited transcript
of the conversation follows.
Knowledge@Wharton: Do you see this book as a teaching tool for
companies and their human resources department?
Jenny Dearborn: Yes. It really is designed for executives across
all the different functional areas, but especially for HR. It’s HR
professionals who are supposed to be monitoring and coaching and encouraging
the right behavior for leaders across the company.
Knowledge@Wharton: When you think about how leadership and HR work
together, what links big data between the two?
Dearborn: Historically, HR departments have been run by
wonderful people who are great people-people. They are great at the human
interaction. They’re great at being empathetic. They’re wonderful at caring
deeply about how people feel, and that’s fantastic. But to be a competitive
differentiator moving forward, we need to move beyond that. We need to use all
of the tools available in order to be more effective. Every other functional
area in a business is using all of these resources, all of the data and
insights. HR needs to use that, too, for their primary responsibility, which is
to groom the leadership skills across the company.
Knowledge@Wharton: Does this require HR professionals to change their
point of view about their roles in the company?
Dearborn: Absolutely. It really is taking HR departments by
surprise, which is part of the motivation for writing this book. It’s trying to
give my peers the tools they need to keep up and be effective. One of the
pieces of research that I cite in the book is that in 2016, for the very first
time, more than 50% of the newly appointed chief human resources officers did
not come from HR.
If you started at the bottom in HR, you’d
think, “With time, I’m going to get to that top job.” Now, more than 50% of the
time, that’s not going to be somebody who has started at the bottom. It’s going
to be somebody who came laterally from the head of marketing or operations or
sales or finance or pretty much anywhere else. The No. 1 reason why is the lack
of expertise in data and analytics.
Knowledge@Wharton: Do you expect to see HR departments bringing data
scientists into their operations?
Dearborn: That is the No. 1 requested new job that all HR
departments around the world are looking for. At the top of their list is
somebody who can drive the data and analytics for their department, so every HR
department is looking for data scientists. It’s unusual to go to a university
recruiting event, go to the data science or statistics department and say, “Hi,
I’m in HR. Do you want to come to HR?” The undergraduates are scratching their
heads, but it really is the trend.
Knowledge@Wharton: What are the roadblocks for HR in terms of getting
access to data?
Dearborn: Oftentimes, the internal data in an organization is
kept in lots of different places, so it is not consolidated neatly. I’ve never
known of any company where all the data is consolidated neatly. You have sales
data in the sales department. You’ve got customer interaction data in customer
service. You’ve got productivity numbers all over the place. The coming
together of all of this information is where the power is.
Each of these groups is going to hold on to
the data they have because it’s a sense of power for them. They’re concerned
with: “If I give you this information, how are you going to use it to
potentially make me look bad, make me look like I missed a trend or that I
wasn’t doing my job as well as I could have?” There’s a lot of searing
skepticism about giving over raw data to a central group and saying,
“Triangulate this. Put some algorithms on top. See what you come up with.”
People are quite reluctant to share.
Knowledge@Wharton: You point out that executives don’t always make
decisions based on the data points that are provided to them. Why not?
Dearborn: My hypothesis is that most companies have all the
data that they need, they just don’t know how to use it. They don’t know how to
put it together or what questions to ask. They don’t really know what they’re
looking for.
Most companies have tons of information about
their customers, about which accounts are more productive, which accounts are
high margin, and which accounts are a complete waste of time because the return
isn’t there. Companies know this. But they don’t have the time or the discipline
to take a step back and ask themselves tough questions like: What are we doing
here? What is our purpose? What are our goals? What are we trying to achieve?
What is the best way to get there?
Knowledge@Wharton: Companies have all of this data at their fingertips
and don’t really know what to do with it, which is a big problem. It’s also a
little scary, considering the concerns about the use and protection of data.
Dearborn: There are significant concerns around data privacy.
What are you going to do with this information? What is it going to say about
me, about my behavior, about my buying patterns, about who I am? How is this
information going to reveal something that maybe I don’t want to have revealed
to my customers, to my employer? There are also a lot of concerns about
employer’s rights in all of this. Some countries have very strong rules and
regulations around data privacy, and other countries are less restrictive. It
really is kind of a wild west right now.
Another strong theme in the book, and in a
lot of the speaking that I do, is around the importance of diversity in the
data scientists so that we can make sure the questions being asked of the data
— and the decision-makers of how artificial intelligence and machine learning
are being used — are really representative of a diverse perspective across
society.
There is a very narrow group of people who
are making really powerful decisions using data. It would be better for all of
us, in corporations and society, if it was more open and more transparent
around how the data is being used, what decisions are being made and if a
diverse group of people was engaged in that decision-making.
Knowledge@Wharton: How has your understanding changed you as an
executive at SAP?
Dearborn: It has made me significantly more empathetic to the
rights of everyday employees and everyday people.
I started this journey wanting to get more
information so that I could prove the value of the work that I was doing. I was
in charge of a huge department. We had a significant charter to go roll out
learning and development in a corporation. Someone said, “How do you know what
you’re doing is actually making a difference?” I decided to dig into data and
information to get facts to support that what I’m doing is actually making a
difference and making a positive contribution to my corporation.
That was the start of my journey. The more I
got into it, the more I said, “Wow, this is really powerful.” I now have
insights into people’s behavior, people’s choices.
The more sophisticated your analyses are, the
more you can start to predict the future. You can say, “I believe this employee
will be successful in the future. There is a 90% confidence rate that this
particular employee will make quota at the end of the year. I believe this
other leader will likely fail, unless there is some sort of intervention.”
You extrapolate that out, and you can start
to predict behavior. That’s really powerful. It’s a wonderful tool for a
corporation to make sure that they meet their revenue targets. But there are
bigger implications for us as a society. I’d love for us, as humans, to be
having this conversation about the power of this. How can we use this for good?
How can we use this to make the world a better place and improve people’s
lives?
http://knowledge.wharton.upenn.edu/article/how-big-data-can-make-big-improvements-in-hr/
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