At Last People Analytics is Ready for You

People Analytics

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?

The Evolution of Analytics

Two forces reshaped analytics over the past four years: embedded solutions and the democratization of data.

Embedded Analytics

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.

Evolution of Business Analytics

The Democratization of Data

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.

A Delicate Balance

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.

  • Evaluate business goals and the information you will need to make timely decisions, independent of your current analytical model. Keep in mind that missing a target is important, but missing opportunities can be devastating.
  • Understand your current solutions and the analytics roadmap of your business platform providers. Do you have a BI tool that nobody uses? Does your software vendor offer advanced analytics or do you need to connect it to another solution? In each case, consider what information you need that resides outside your functional area.
  • Develop learning paths for data users at all levels. The better people understand their data, the more useful their analytics will be. For example, knowing how to join tables is an essential skill. It doesn’t take a data scientist—a business user can learn the basics in half a day.
  • Unless your organization already has experience in analytics deployments, it might be prudent to engage an analytics partner to help get you started. Having someone on your team who has experienced the pitfalls and overcome them can be priceless.


1. “Magic Quadrant for Business Intelligence and Analytics Platforms.” Gartner. February 4, 2016, revised February 8, 2016. 

How Advanced Reporting Can Build a Bridge to People Analytics


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, Talent_Analytics_IB.jpg organizations in the Advanced Reporting stage of analytics maturity are proficient in these activities:

  • Proactive reporting for decision making.
  • Measurement of results and comparison with trends and                        benchmarks.
  • Delivering customizable self-service dashboards to decision-                  makers.

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:

  • Your data clean and well-organized.
  • You have at least begun to integrate talent management platforms with each other and with core HR systems.
  • You have automated your recurring reports so you can devote time to more advanced reporting and analytics.
  • Your organization has a cross-functional data team.

Using Advanced Reporting to Bridge Analytics Gaps

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.

The HR Credibility 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.

The Analytics Culture Gap

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.

The Analytics Skills Gap

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:

  • strategic thinking and industry expertise to understand and communicate the vision to stakeholders at every level,
  • experience and expertise in each business function,
  • data visualization expertise,
  • skills in data management and governance, and
  • technical infrastructure and systems management capability.

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.

The Analytics Funding Gap

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.

How to Get Started with Advanced Reporting

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:

  1. Assess the information you have. Study the data models in your talent platforms and how the data can help business decision-making.
  2. Benchmark your talent metrics against industry norms to get an indicator of where you might begin, but don’t let benchmarks alone drive your decision. Your business leaders should do that.
  3. Conduct a needs assessment. Work with line of business leaders throughout the organization to understand their information needs.
  4. Use the tools in your talent platform or data warehouse to develop the reports your people need. Once you have data outputs that meet their requirements, automate the reports.
  5. Where it makes sense to do so, integrate data feeds into other business platforms for reporting.
  6. Use your needs analysis to develop talent dashboards for employees, managers, and executives. If your talent platforms do not have the capability to configure and deliver dashboards, there are many solutions available. Before you jump into a solution, call us. We would be glad to talk with you about our experience with the leading analytics platforms.

Next Steps

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.


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.

3 Tips to Help You Get Started in People Analytics


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.

  • Putting together teams with the right skills at a cost most business can’t even consider. Data scientists are scarce and                      expensive.
  • Getting data to the right place, at the right time, and in usable condition. Everybody loves to cook, but no one likes cleaning            up the mess. We hear nightmare stories of multi-million-dollar data cleanups.
  • Learning how to ask the right questions. Some early enthusiasts advocated amassing vast depositories of data and applying          statistical methods to see “what the data would tell us.” Sort of like looking for answers in a library with no catalog.
  • Changing the way people make decisions. Way back when we were becoming HR professionals, our mentors taught us risk            avoidance, best practices, personal relationships, experience, and gut instinct.

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?

Get Control of Your Data

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 Small

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.

Borrow or Rent the Expertise

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:

  • Strategic thinking, with knowledge of the business and the industry.
  • visionary leadership, with the ability to communicate to stakeholders at every level, including board members;
  • data visualization;
  • master storytelling skills;
  • statistics and data modeling;
  • data management and infrastructure;
  • expertise in each business function.

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.

Recommended Reading

How to Get Control of Your Disconnected Data

How to Build Your People Analytics Team

How to Build Your People Analytics Team

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.

Start Small and Grow Your Team

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.

What Skill Set Do You Need for People Analytics?

A people analytics team requires expert knowledge in every area of human capital management, but many of the skills required rarely reside in HR.

  1. Visionary Leadership. When you are starting a new initiative, the ability to frame and communicate the future state is an essential skill. A visionary leader can create a picture of the future in every team member's mind, and motivate them to want to achieve it. If that is you, great. If not, you will need a visionary ally.
  2. Business Acumen. More than in-depth knowledge of your company, your business model, and your How to Build Your People Analytics Team_IB.jpgindustry, this skill includes an understanding of how your firm makes money capitalizes on opportunities as they arise.
  3. HR Expertise. Knowledge and experience in every HR function are necessary, but we also recommend high expertise in organizational behavior, at the intersection of psychology, sociology, and anthropology. You will explore how human behavior affects the enterprise and vice versa. Your best ally may be your CFO or another member of the Finance team.
  4. Technology Infrastructure. As you grow your analytics capability, you will need access to data wherever it is: in your software, elsewhere in your company, or outside it. You also need to deliver insights wherever your people or partners need them.
  5. Data Management. If your organization does not have a master data management plan and a data governance body, this is the time to start the conversation. You should manage everything you do with data according to the standards in your organization. If you need to break down organizational silos, unified data management is an excellent way to do it. A robust data master plan will strike the right balance between control and autonomy.
  6. Data science, analytics, and visualization. You may be fortunate enough to have a data scientist or partner who can manage all three roles, but frequently you will have several people in these functions. Your best plan may be to engage the skills on an ad hoc basis while you grow analytics and visualization capability in your HR group.
  7. Marketing. How well your business leaders receive your analyses and recommendations will depend on how well you market your efforts. Partner with Marketing to make sure you deliver with impact.

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.


1. "Still Under Construction: The State of HR Analytics 2016." New Talent Management Group. August 2016. Retrieved November 14, 2016.

2. Bersin, Josh. Predictions for 2017: Everything is Becoming Digital. Bersin by Deloitte. Retrieved January 17, 2017.

Build Your People Analytics on a Strong Operational Reporting Foundation


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.

Assess Your Talent Analytics Maturity

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.


Begin at the Beginning

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.

Build a Team

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.

  • Strategic thinking and industry expertise to understand and communicate the vision to stakeholders at every level.
  • Experience and expertise in each business function.
  • Data visualization expertise.
  • Skills in data management and governance.
  • Technical infrastructure and systems management capability.

Clean Your Data

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.

Automate Your Reporting

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.

Integrate Talent Management and Core HR

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.

Next Steps

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.


1. "Talent Analytics (with Maturity Model and Framework)." Bersin by Deloitte. Accessed August 08, 2016. 

Measuring the Immeasurable in People Analytics


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 is Measurement?

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:

  • Scientists can conclude with high certainty that a planet or moon exists by changes in the orbits of nearby objects. Not only     that, they can deduce its size, mass, and orbit.
  • Your CFO makes or recommends investment decisions based on the probability of a favorable rate of return.
  • Your Marketing Department predicts the behavior of customers by surveying a tiny sample of buyers.

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.

Defining What to Measure

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.

How to Measure

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:

  • What is the decision the measurement will support?
  • What is being measured and how does it matter to the decision?
  • How much do you know about it now?
  • How does uncertainty about the variable create risk for the decision?
  • What is the value of additional information?[2]

At that point, the decision comes down to whether the value exceeds the cost, but there are many ways to control costs.

  • A small sample can tell us a lot about a large population. A valid sample of 370 will give you 95% confidence of an attribute of a population of 10,000 with a 5% confidence margin.
  • We can use inference to estimate characteristics of an unseen population. For decades, customer service centers have been using small samples of observations to improve the skills and behaviors of their representatives.
  • People often give up when measurement involves unknown or uncontrolled variables. Using statistical techniques, your data scientist can determine that a coaching program caused an increase in revenue per employee and not the economy or pricing.
  • You can measure seemingly intangible preferences by how much time, money, or other things people would give up. For example, you can gauge how much employees value a shared-cost benefits program by how much a random sample of employees stands ready to contribute.


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.

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.

How to Succeed in People Analytics: Think Big and Start Small


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.

  • A motorized outdoor sports company needed to reduce new hire turnover in its sales force. A learner dashboard and training analytics solution gave them over 200% return on investment in the first year. Better management of new hire training improved their turnover, but the biggest gain was in sales.
  • Companies in highly regulated industries find they can reduce their risk exposure using training analytics to monitor compliance.

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.


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

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