Break Out of the Silo to Get Started in Talent Analytics

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A recent article by renowned analytics consultant and author Gene Pease tells us Analytics are a Decision Maker’s Best Friend. Pease was writing about how we generate a tremendous amount of what he calls “dark data” that exists in each of our separate talent platforms but don’t use it to make the right decisions in other talent management activities.

Pease rightly tells us we need to mine that data to improve our decision-making. He points out that even at the time we hire a candidate, we know a lot about our people, but “rarely is that data used in the analysis when making talent investment decisions.

People analytics with Chasma HRBox

 
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 But CEOs don’t trust HR data and trust even less using data to make people decisions. In a recent Fortune Knowledge Group™ study,

  • “Nearly two-thirds (65%) of executives believe subjective factors that can’t be quantified (including company culture and corporate values) increasingly make a difference when evaluating competing proposals. Only 16% disagree.
  • A majority (62%) of executives say it is often necessary to rely on gut feelings and soft factors.
  • A majority (61%) of executives agree that when making decisions, human insights must precede hard analytics.”[2]

That presents a tough challenge. How do we get the attention of a CEO who has built a company on making “intuitive” decisions about people? How can you convince a CFO that you need to increase spending on talent analytics when there are so many competing priorities?

In our experience, the best way to break down the barriers is to get out of the silo. Talent leaders need to use the data they produce to perform their functions better, but they will fare much better by getting out into business operations to look for problems to solve.

We don’t mean you shouldn’t solve talent problems. What we are saying is that the talent problems you have are not HR problems – they are business problems. If you have a problem keeping engineers, that is an issue in engineering, and the head of engineering is the one responsible for KPIs affected by the turnover.

The same goes for other functions in the business – but here’s our little secret: many of those functions are very experienced in analytics and have the tools at their disposal. Engineering is obvious, but marketing also has the capability. If you have a robust marketing function, they have been studying human behavior for years. Finance has been using predictive modeling for decades.

We are not telling you to crash their party, but what you can do is talk with them about solutions that may help their issues and ask them to contribute in analyzing the data. In the engineering scenario, the data that already exists in recruiting, performance management, learning and development, attendance data, and surveys can uncover the factors driving turnover.

Start with a small project. By measuring and reporting on the impact a small intervention can have on business operations, you can develop the momentum for a new vision of talent analytics. It may take time, but harnessing the data you generate will impact on the future of your organization.

To learn more about developing talent analytics, download our free e-book: The Datafication of HR: Operational Metrics to People Analytics. Find out how you can start the conversation about people and performance by impacting business results.

References:

1. Pease, Gene. "Analytics Are a Decision Maker's Best Friend." Chief Learning Officer. July 28, 2016.

2. Only Human: The Emotional Logic of Business Decisions. Fortune Knowledge Group (Time, Inc.). 2014.

Predictive Talent Analytics for the Bottom Line

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We often read that companies have poured billions of dollars into employee engagement programs over the past few years with little to show for the effort. Every day we get an offer for another webinar, white paper, research study, or blog article about employee engagement.

With human capital analytics now becoming mainstream, the clamor has increased. An entire industry sprang up around employee engagement, to the point that it seems impossible for a CHRO to know where to start. We have technology vendors, assessment providers, mood monitoring systems, work management systems, and a wealth of local, national, and global consultants. And let us not forget the new disciplines like Engagement Diagnostics Specialist.

We are not saying well-implemented engagement programs do not work, or that those of us in the business don’t provide valuable service. Dozens of case studies prove otherwise. Good programs succeed, but it takes strategy, discipline, and a road map.

Navigating the Market

Josh Bersin and his team have created an Employee Engagement Vendor Market Navigator to help CHROs navigate the provider maze. The model takes work to fathom, but it helps us understand the contributions each of the specialties and platforms make. It maps the resources and expertise needed to enhance productivity, monitor organizational health, and effect change.

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All the resources Bersin recommends would be helpful, and might be doable if these factors are in your favor:

  • the organization has the resources,
  • your CEO is not a skeptic,
  • the CFO understands the value of the investment, and
  • you have a strategy and a well-rounded, experienced team.

For the rest of us, we recommend a more measured approach. We agree with Bersin that the place to start is with a strategy, but a CHRO can’t go down the analytics road alone.

First, stop trying to chase employee engagement. To many people, employee engagement is HR jargon. Engagement is not a business outcome, and a direct impact on the income statement is hard to determine. You will not get support for what business leaders perceive to be a nebulous idea. Follow the money trail to build credibility.

Form the Right Partnerships

Assuming you need to overcome internal resistance, the place to start is with the right alliances. Your three new best friends should be Marketing, Finance, and a business leader with a problem.

If anyone knows analytics, Marketing does. They have been studying consumer behavior for years, and can predict who will buy what and when with uncanny accuracy. The predictive analytics they use will be much like what you will use to predict employee behavior.

The CFO has been using analytics to predict the value of investments in the company for decades. This initiative is an opportunity to add value to the business on the CFO’s terms.

Most business leaders have financial objectives they must meet, usually expressed as key performance indicators, (KPIs). For example, if you can predict with accuracy which candidates will be better salespeople, you can improve sales, reduce attrition, or both. A large medical device company cut sales attrition by 1 percent and saved $30 million in turnover costs.

Solve a Business Problem

If you can help a business leader solve a problem that results in better numbers on the balance sheet, you are well on our way to changing the way HR does business. In the best case, that business leader will command the resources necessary to get the work done.

Every situation is different, but we can recommend a general framework for your analytics project.

  1. Engage a data analyst. If you don’t have expertise in the organization, hire an experienced consulting company. (It may be time to upskill HR.)
  2. Isolate a business problem.
  3. Agree on the metrics that measure the outcome.
  4. Determine the analytical method you will use to determine correlations, causation, and predictions.
  5. Assemble the data. Much of the data about employee performance is in systems other than HR.
  6. Analyze the data. Be sure to include data quality assessments and validations
  7. Discuss the findings with your executive team.
  8. Implement the decisions of your executive team.
  9. Track, assess, and validate the results.
  10. Communicate the outcome, with emphasis on financial data.

A small success that affects financial results will lay the groundwork to help you improve and enlarge on your efforts. In time, predictive analytics will be the foundation for more informed decision making and a more productive workforce.

9 Things to Consider when Choosing a Talent Analytics Partner

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As business leaders come to value their investment in people, they are turning to HR data analytics to guide their decisions. They are asking HR leaders to take on analytics as an essential component of their portfolio.

In spite of what you may read in the blogs, your world will not end next week if you don’t implement advanced predictive analytics today. It will take time to build a robust analytics capability. What is most important is to start with a strategy and build a solid foundation.

Start from Where You Are

Our priority in developing analytics capability in any organization is a cohesive strategy and structured data governance. But before you can frame a strategy, you need an honest assessment of current capacity.

The Bersin by Deloitte Talent Analytics Maturity Model gives us a framework for the assessment. A typical organization is capable at several levels depending on the function and need. Your marketing analytics maturity may be at the top level while talent analytics is only beginning to get off the ground.

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Almost all of the people we talk to about people analytics are at the operational reporting level. What that means is they are using ad hoc reporting to manage day to day activities. The focus is on what has happened in the past and what is going on now.

You are where you are, and it might be exactly where you need to be. However, if your business needs to improve performance, planning, and decision-making, it may be time to lay the groundwork for growth. The right approach at the basic level is to strategize, standardize, and integrate operational reporting, then introduce advanced reporting methods.

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The Analytical Skills Gap

Most HR organizations don’t have the analytical expertise to move up the growth curve. People don’t gravitate toward professional work in human resources because they love math and statistics.

Lack of analytics skill leaves HR with three alternatives:

  • Reskill. You may have people in the organization with analytical expertise and the desire to learn, but it could take time.
  • Reorganize. Businesses around the world are rethinking their organizations, and this is an excellent opportunity to change the skill mix in HR. However, competence in talent analytics is a new body of knowledge. Skilled practitioners are scarce and expensive.
  • Retain a Partner. The quickest and most cost-effective path may be to hire a talent analytics consulting firm. Size and capability range from single consulting data analysts to massive global companies. Choose an organization that matches the scope of your plan.

We recommend all three. Hire a good partner who can help you design and execute your strategy while your team’s capability grows. That will put you on the fast track.

Select the Right Partner

A consulting partner must have a broad range of skills in technology, human capital management, and statistical analysis, and the ability to form a solid partnership.

  • First, find a consultant who seeks to understand where you are and where you want to go. Choose one who knows the wisdom of taking one step at a time.
  • Assess the cultural fit. Glitches are inevitable, so you need to be sure you have a partner who will work well with you when an issue arises.
  • Choose a candidate who patiently answers your naïve questions. Arrogance or condescension should alert you to potential problems ahead.
  • Experience in your industry will be helpful. Seek a consultant who understands the talent challenges in your line of business.
  • Ask what technology tools they use and why. The best candidate will start with “it depends” and explain how certain situations demand different tools.
  • Explore experience in talent management. Do they use professional practitioners or a technical consultant who read a book?
  • Inquire about statistical methods and why they choose the methods they do. For example, there is no one-size-fits-all way to perform regression analysis. Hundreds of methods exist, but some analysts only use the tool they know.
  • Request an explanation of how they will determine what methods to use to establish the validity of their models and why.
  • Nobody has perfect data. Ask what they will do to cope with messy and incomplete information.

Human capital analytics is fast becoming a business necessity. Choosing the right partner will help you advance your capability to make decisions that will have a direct impact on your business.

How to Overcome the Talent Analytics Skills Gap

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One of the most pressing talent shortages today is in Human Resources. The skill set required to advance down the path of people analytics is scarce and expensive. Finding the skills to get actionable intelligence from data is hard enough – then we add the requirement for deep talent management expertise.

Did we hear someone muttering about purple squirrels?

Data Scientist Skill Sets 

A data scientist needs much more than scientific and analytical skills. The competency requirements touch a broad range of disciplines.

  • strategic thinking and the skills to communicate the vision to all stakeholders at every level;
  • data visualization expertise and the ability to tell a compelling story;
  • business acumen and knowledge of business performance and growth;
  • statistical, mathematical, and programming knowledge to isolate and capitalize on relevant information in a sea of data (most of which is irrelevant);
  • knowledge of technical infrastructure, including cloud integrations;
  • experience in data governance and security; and
  • skills in data preparation, profiling, cleansing, and transformation.

Expecting any two people to have all of these attributes is unrealistic, let alone one. It will require the combined effort of several people.

You may have all the talent you need already in your organization. You may need to acquire the talent. In many cases, the best approach is to partner with an analytics consultant.

In their recent book on People Analytics in the Era of Big Data, Jean Paul Isson and Jesse Harriot give us the concept of a People Analytics Center of Excellence. Its purpose is to create a team of data evangelists.[1] What we call it is putting the right people in place.

 

Build a Talent Analytics Team

Isson and Harriot speak to creating an analytics team that comprises technical expertise, scientific and business skills, analysts and storytellers. We add three more core competencies:

  • Functional expD14_HCM_People Analytics_LPertise in talent management, encompassing the entire range of talent management activities and concepts.
  • Functional expertise in every area of the business. Many business entities have shared talent challenges, but each also has its unique needs. An analytics team should never have a working session about any segment of the organization without one or more people inside that group. This rule works two ways. Not only can you learn about the particular requirements of that business unit, but you also have an opportunity to co-opt them into the change management effort.
  • Change management expertise. The most important change in transforming from an IT-driven reporting structure to a user-centric analytics model is not the technical work. It is the cultural shift: a seismic change in the way people gain, analyze, and use information.

Align to the Business

The first order of business for a talent analytics team is developing a strategy and aligning to the organizational objectives. Ideally, the team will have a mandate from the executive suite. Sometimes, they will not. Many executives don’t trust HR data, and like even less using anything but their judgment to make people decisions.

Working with the executive suite requires a deft touch. Success in analytics begins with asking the right questions. Most executives are not analytics visionaries. They are problem-solvers. Frame your discussions from the perspective of impact on the business.

The best approach may be to start with a small initiative with significant impact to show the value of using data to influence decision-making. Many organizations have had success with improving turnover rates by using analytics to alert them when people are likely to leave. Others are improving quality of hire with data-informed assessments.

Take the Long View

Many initial forays into analytics can have a quick payoff, but the work has only begun. The goal is to create a culture where decisions are made on the best available information by the people closest to the problem or opportunity.

References: 

1. Isson, Jean Paul, and Jesse Harriott. People Analytics in the Era of Big Data: Changing the Way You Attract, Acquire, Develop, and Retain Talent. Hoboken: John Wiley & Sons, 2016. Print.

2. Only Human: The Emotional Logic of Business Decisions. Fortune Knowledge Group (Time, Inc.). 2014. 

Pixentia is a full-service technology company dedicated to helping clients solve business problems, improve the capability of their people, and achieve better results.

Guide 16 - Building the Business Case for People Analytics