Pixentia is a full-service technology company dedicated to helping clients solve business problems, improve the capability of their people, and achieve better results.
The world has been waiting for HR to jump on the analytics bandwagon. Executives said HR didn’t have the skills and mindset for data-driven people management. Industry wags declared people analytics stuck in neutral, and only a tiny percentage of companies could gain real insights from their data.
Before we pass judgment, consider the way the business intelligence industry delivered analytics—a complicated, expensive process managed and operated by IT. Getting useful information to the people who needed it required a long process of gathering requirements and building data models. Delivery could take months. Too often, the analysis arrived much too late, resulting in lost opportunities on many levels.
Human capital software vendors provided reports and dashboards, but the information looked backward. It didn’t support decisions for future action. Data was locked in functional silos. Turning it into useful information for the business still required a cumbersome BI process.
Can we blame HR for not jumping on that wagon?
Two forces reshaped analytics over the past four years: embedded solutions and the democratization of data.
Embedding reports and graphs in business software is not a new idea, but the balance of power has shifted from IT to business users. Now, instead of trying to compete with analytics providers, business software vendors embed state-of-the-art products in their applications. Modern solutions enable providers to configure the front end of analytics solutions to match the usability of the host application. They work seamlessly as an integral part of the business platform.
These embedded solutions contain the tools necessary to connect them to any other business application. Many business application vendors offer pre-built connectors. Standardized custom connections use an application programming interface (API), JavaScript Object Notation (JSON), and representational state transfer (REST) that developers and data analysts understand. Vendors like Workday provide tools any business user can deploy using a few menu-driven steps.
Embedded analytics changed the business model. Instead of requiring companies to buy licenses for a small number of individual users, analytics providers license their solutions to business platforms, making analytics possible for anyone who uses the applications. Executives have search-driven analytics to answer their questions and predictive models to plan future initiatives. At the other end of the organization, an employee who wants to ask for time off using a mobile device has the information and tools they need on-screen. In between are citizen analysts and managers who can develop their own data models to drive better decisions at every level.
The analytics evolution doesn’t mean you need to rip out everything and replace it. The strengths of centralized business intelligence are data governance and security. Distributed data analytics will require new ways of thinking. Every user will have a role in an organization-wide data management, and each user will need to become part of the security solution.
The proliferating market gives us as many deployment models as vendors. No one provider has everything a business needs. It might be best to resist the urge to immediately start evaluating solutions until you have completed an analysis of your current situation and future needs.
Only after a thorough scan of your business model, competitive environment, and data requirements will you be ready to approach a solution. Here are four recommendations to get you started.
References:
Almost all human capital management platforms today provide tools for advanced reporting. Vendors are responding to demand for information, and competition is driving them to improve their reporting functions. This article is about how to use that capability to create momentum for data-driven HR.
According to the Bersin Talent Management Maturity Model, 
organizations in the Advanced Reporting stage of analytics maturity are proficient in these activities:
At this level, we turn the focus from internal operational reporting to helping business leaders make decisions that impact results. The measurements change from "What happened?" to "How will this affect the business?"
Operating at the advanced level requires that your operational reporting is functioning well. At minimum, we recommend you have these practices in place:
Developing your advanced reporting will close four gaps that exist in many organizations today: an HR credibility gap, an analytics culture gap, an analytics skills gap, and a funding gap.
Many business leaders today perceive that HR data is not credible and is not aligned with business needs. Having lived through the era of legacy ERP systems and clunky, disconnected applications, we understand the influence of history.
Advanced analytics is an opportunity to overturn the perception. If you deliver fast, accurate information to the people who need it, the attitude will change.
We in the business world have a lifetime of making gut decisions, and we place a high value on people with “good judgment.” Using data to improve decision-making can feel like we are giving up control.
Create a data-driven culture by valuing and practicing the principle of making decisions with the best available information. Understand that when a logic-driven decision doesn’t “feel” right, there may be factors at work we don’t understand. You can allow human judgment into the process without giving up the value of logical analysis.
The right path it is to fold analytics culture into organizational culture, including decision-making as a rigorous, data-driven process.
There is no quick fix to the skills gap, and if you slept through your economics and statistics learning, you need a refresher. We don’t recommend you rush out to hire a data scientist. If you are only now refining your ability to deliver accurate information for decision-making, you are not ready to make that leap. However, you need to assemble a team with the following capabilities suggested by Dussert and Volini,[1] whether it is inside or outside your enterprise:
If you already have these skills in your organization, seek to engage them as business partners. If you don’t, you can engage an analytics consulting partner while you build your team skills.
Many CFOs are questioning the value of analytics after significant investments didn’t pay off. We recommend an entirely different approach: think big, start small.[2] Use small successes to show the value of the information you bring to a decision. Those small successes will help you build the momentum for larger efforts.
The best advice we can give for getting started is this: don’t go it alone. Use the assets and expertise that already exists in your organization. Gather them together and take these five steps to success:
In a future article, we will discuss how you can use statistical analysis to solve business problems. If you want to learn more about how you can use analytics right now, read our free e-book:
The Datafication of HR: Migrating from Operational Metrics to People Analytics
Find out how you can start the conversation about people and performance by impacting business results.
References:
1. Dussert, Bertrand and Erica Volini: (Webinar) The informed Executive: Improving Organizational Agility Through Workforce Analytics. Oracle Corp. Feburary 4, 2016.
2. 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.
When the HR analytics wave first hit, it seemed the blogosphere, research houses, and vendors were shouting from every street corner that we all needed to hire data scientists right now or end up on the ash heap of history.
Those with deep enough pockets to risk it jumped in, only to become disillusioned. There were a lot of successes, but many weren’t seeing the game-changing impact they expected. CFO's questioned why they weren’t seeing ROI.
The early adopters found it is not all about technology and data wizards. Embedding people analytics in the organization takes hard work. There were some big mountains to climb.
The early adopters are much better at it now, and vendors have sprung up to fill the gaps. Enough time has passed that we now see correlation and causation between effective use of analytics and organizational performance.
It’s time to get on the fast track – but how do you do it with a limited budget?
If you don’t have a data management strategy and a data governance team, that is the place to start. Instead of concentrating on cleaning up bad data, focus your efforts on finding the cause of bad data to stop creating it. Often the solution is a simple as getting data creators and data users to talk to each other.
Some years ago we dealt with a case where an HR service center had high rates of rework as they processed employee transactions. We first estimated the cost of the rework, then brought the data makers and data users together. It was an easy change once the originators saw the cost estimate. They also made quick work of establishing better communications.
Start with a small project that will have a significant impact. For example, analyzing the KSAOs of high-performing employees and matching candidates to those criteria will reduce new hire attrition. Assessment expert Stephen Pollan, CEO of Assessment Technologies Group, told us the reduction in turnover can be as much as 50%.
You can use your small project to show your business leaders how people analytics can help, and the quantifiable costs of attrition can give you a good start.
According to Glassdoor, the average salary for a data scientist is $113,346. When you add in benefits and other costs, the total is around $160,000. It would be nice to think you can find a single person with all the right attributes, but we are doubtful.
Data scientists fall into two groups: those with a mathematics and statistics background and others with a technical background. You will need both skill sets. Here is the competency mix you will need:
Some of the skills will already be in your organization. Marketing or Finance may be able to lend a data analyst. You can probably convince a senior visionary leader to serve as a sponsor. Your CIO may have the data management and infrastructure knowledge, but how about capacity? Most CIOs we know are already overworked.
You may find it cost-effective to team with an analytics consulting firm. There are many factors to consider in deciding whether to outsource your people analytics initiative. Cost may be the most important thing to consider, but basing the decision on cost alone will be a mistake. Work with a vendor who complements your skills set, has experience working in your industry, and who has demonstrated the ability to deliver on your requirements.
Consider also how quickly you can get up to speed. You may be able to speed your ramp-up time and save cost by doing so. We encourage you to talk with analytics providers to understand what they can and can’t do for you.
In recent years, interest in people analytics has been more hype and hope than results. Most organizations are still doing what they have always done, but the promised revolution in HR shows signs of life.
Eighty-five percent of organizations in an August 2016 New Talent Management Network survey said they were performing some people analytics, and 69% of those who are not stated that they would start within the next 12 months.
Bersin by Deloitte reported a 29% increase from 2015 to 2016 in the number of organizations using people data to predict business performance, and Josh Bersin seems optimistic, a contrast to his gloomy outlook in 2015.
"In 2017 we will see analytics move from a niche group in HR to an important operational business function."
Josh Bersin: “Predictions for the Year Ahead,” January 19, 2017.
Embarking on the analytics journey may seem like an impossible task, but we want to encourage you to get started. All the organizations we work with have started with a small initiative and grown their capabilities over time, and sometimes a small initiative can create astonishing results.
You don’t need a huge budget and a world-class analytics team to make an impact on business results, but you need the right team. People analytics is more than the data in your HR database and talent management technology; much of the information you need for productive people analytics lives outside HR applications. You will need to form alliances inside and outside your organization.
One of the primary criticisms of HR over the past few years has been the lack of analytical skill in HR. Finance and Marketing have become analytical pros while HR lagged. Few of us current or former HR practitioners came into the profession with an analytical mindset. Even those of us who studied analytics in psychology, sociology, or economics left statistical analysis behind so we could do “people work.”
You need not become a data scientist, but you do need one. You will also need other skills you don’t have in your organization. If you can’t borrow them from Finance or Marketing, a consulting partner can give you a way to control your costs and still get all the help you need. Data scientists and technical skills are expensive. It is often better to rent than buy. Find a partner who understands your need.
A people analytics team requires expert knowledge in every area of human capital management, but many of the skills required rarely reside in HR.
industry, this skill includes an understanding of how your firm makes money capitalizes on opportunities as they arise.Success begets success. As you impact the business, you will create a demand for more insights. It will take time to get there, by having the fight team in place will give you a good start.
References:
1. "Still Under Construction: The State of HR Analytics 2016." New Talent Management Group. August 2016. Retrieved November 14, 2016.
If you are a professional in any HR function, we are sure you know of the pressure to develop people analytics capability in your enterprise. The world has changed, and we are in a war for talent. Every business function is being digitized, and the pressure is on Talent Management to join the 4th industrial revolution.
The global economic climate is forcing business leaders to strive for new ways to improve productivity. Yet employees have become less loyal and harder to engage. Their commitment is to their development, not an organization. Talent is in high demand and very mobile.
The key to the future is the ability to acquire, analyze, and act on better business intelligence about people and work. The improvement effort begins with a thorough assessment of where you are on the journey to managing with data.
Most of the analytics maturity assessments we see are variations on the model developed by Bersin by Deloitte over the past decade and a half. This model describes in a general way the characteristics of the talent analytics function at each of four levels in a progression from simple reporting to predictive modeling.
Level 1 – Operational Reporting: reactive reporting of operational and compliance measures.
Level 2 – Advanced Reporting: proactive reporting for decision making.
Level 3 – Advanced Analytics: statistical analysis to solve business problems.
Level 4 – Predictive Analytics: development of predictive models and scenario planning.
If you are just now starting out on our analytics journey, we want to help you build the foundation on which you can build a strong data-driven culture. The first building blocks you need are in your operational reporting. As the Bersin model explains, the focus on that level is on data accuracy, consistency, and timeliness. If you have not achieved those benchmarks yet, we strongly urge you to take a step back and get your operational reporting under control. Don’t build on a shaky foundation.
The first step is to form the right team. Building a people analytics capability takes a broad range of competencies and skills. No one person in your organization will have all these skills; you will need a cross-functional team.
No one has perfect data. A lot of hands touch information on its way to the permanent record, and people make mistakes.
To move forward, you will need to have data that is clean enough to support decision-making. Focus first on operational data. Work with your data management expert to triage your data and assess the level of effort to clean it. If you need help, an analytics consulting partner will have the tools to clean the data.
In an extreme case, or if it is time to upgrade your business platforms, it may be prudent to warehouse your dirty data and start fresh. In some areas, like recruiting, it will be easier than others.
Every minute you spend creating recurring reports is wasted. Taking the time to automate reports will pay off when you can take on higher-level analytical work. If your HR platforms do not support automation of the recurring reports you need, it is probably time to upgrade.
Automate also your reports to government agencies and regulatory bodies. Federal government entities and almost all States have the mandate to accommodate automated routine reporting.
If your HR functions not integrated, now is the time to consider taking on that task. Not only will you reap the rewards of integrated data, but you will also break down silos and communication barriers that might still exist.
The solid foundation you build in operational reporting will support your move into better decision-making. In an upcoming article, we will show you how to develop more advanced decision-making tools using the data you have.
References:
1. "Talent Analytics (with Maturity Model and Framework)." Bersin by Deloitte. Accessed August 08, 2016.
Most of the barriers to adoption of people analytics have been overcome. Modern technology platforms provide robust analytics. An entire industry has sprung up, with helpful tools to cleanse, prepare, and manage data. HR leaders understand that they need not be data scientists—they only need the expertise on the team, and it doesn’t have to be full-time help.
One barrier that remains is what Douglas W. Hubbard calls the illusion of intangibles.[1] When we talk with people about measuring what is important to the business, they voice confusion in what to measure and how to measure it. Our answer is that we should measure what is relevant to the business. However, that doesn’t move the conversation forward until we agree on what measurement means.
What do you think of when we ask what measurement is? If you are like most people we talk to, you think of exact numbers: using a tape measure, computing values, or collecting scores. If that were all there is to measurement, we wouldn’t be able to measure much of anything in business.
Think about these examples:
Business leaders are mostly risk-averse. If you can reduce the uncertainty that a business initiative will fail, you are providing a valuable service. Likewise, if you can show them that by spending $40,000 on a talent management initiative that has a 95% certainty of improving business results by $2,000,000 over five years, you are likely to get approval. You don’t need analytics to give you absolute measurements to make decisions. You only need to reduce the risk.
“Measurement. A quantitatively expressed reduction of uncertainty based on one or more observations.
- Douglas W. Hubbard, How to Measure Anything
We express the certainty of future events as a probability. Statistics can tell us what happened, how, where, when, and why. Probability says with a degree of certainty what will happen.
One failure in people analytics is the billions of dollars spent trying to measure and improve employee engagement. There have been successes, and correlation analysis show us that companies with high engagement also have high profitability. However, in the aggregate, most of the investment has been wasted.
If you are asking in a survey how people feel about the workplace, are you getting actionable information? Do good feelings cause better performance?
If you ask instead what people and their managers do, you get useful information. Gallup reported in 2015 that managers account for 70% of the variance in employee engagement. We can show that specific behaviors affect employee productivity and retention.
So, it might be better to measure employee development. No, you say. It’s too “fuzzy.”
Stop and think what activities and behaviors make up employee development. We can track coaching sessions, feedback, learning opportunities, and participation in learning. We can use these measures to understand the probability that improving managers’ ability to coach and develop their employees will have an impact on retaining productive people.
The perception of difficulty in measurement can create significant barriers to action. We can overcome them by taking a systematic approach to a decision. Hubbard recommends asking these questions:
At that point, the decision comes down to whether the value exceeds the cost, but there are many ways to control costs.
The purpose of analytics is to reduce uncertainty in business decisions. Benchmarking, “best practices,” and gut instinct informed by experience can lead you to a decision. When you are embarking on important initiatives that affect your entire organization, it pays to understand the risks and to reduce them as much as possible.
Let’s form your team and get started.
References:
1. Hubbard, Douglas W. How to Measure Anything: Finding the Value of “INTANGIBLES” in Business. John Wiley & Sons, Inc. 2014.
2. Hubbard.
Pixentia is a full-service technology company dedicated to helping clients solve business problems, improve the capability of their people, and achieve better results.
Sometimes reading the news and survey reports on people analytics give us the feeling that there is a hustle going on. We know the information we generate in our human capital management platforms can help us make better predictions than using gut instinct. We see it happen every day in our work. But sometimes the dire prediction that your business will be marginalized if it doesn’t make a total investment in people analytics right now seems like hype.
Leading companies around the world have made a big leap into analytics, but CFOs are wondering when they will see a return on the investment. We see dozens of quick hits where businesses had significant returns on a single initiative. However, in a 2016 study by The Economist Intelligence Unit, only two percent of respondents have had the organization-wide impact they seek.
From our perspective, that’s normal. Business leaders and managers have been making people decisions based on gut instinct for their entire lives. If you have built a successful career on your personal judgment of people, it’s hard to trust that an algorithm will do a better job of predicting performance than you do -- even when the evidence presents itself.
Finance and Marketing have embraced predictive and prescriptive analytics with excellent results, but that data is about money and customers. It is easy to think of them as theoretical constructs.
When we are making decisions about people, it is personal. Changing your culture to data-driven decision making about the individuals in your organization will take time.
Re-reading People Analytics in the Era of Big Data by Jean Paul Isson and Jesse Harriot reminds us of the principle that guides our work: think big, start small. Isson and Harriot use that phrase to help us understand that trying to get support for a large initiative may be impossible, but the results of small success will begin to change minds.
You will not become a people analytics powerhouse overnight, nor should you try. As our experience has shown us, we can get started with solving a single business problem to build the momentum toward becoming data-driven.
Small successes like these start the conversation about using people data to improve business results.
We recommend to HR and L&D leaders who haven’t yet been invited to the analytics party to partner with the people responsible for business KPIs. Take on a small initiative to move an organizational performance indicator. Work with your business partners to solve their problems by combining their data and yours.
That path is the fastest way to get started on changing your culture. You don’t have to make everyone a data guru or statistician. You only need to create a culture where people make decisions with the best available information. The rest will come.
References:
1. McCann, David. "CFOs Frustrated with Return on FP&A Investments." CFO. July 17, 2015. Accessed August 09, 2016.
2. The Economist Intelligence Unit. "Broken Links: Why analytics investments have yet to pay off." ZS Associates. 2016.
3. 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
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