In 2016, I had the honor of designing and developing a pay equity technology solution that companies could use to visualize the potential pay gaps that might exist inside an organization. The tool was developed to review gaps in pay based on ethnicity and gender. For most organizations, this was certainly not their first review of pay equity at the organization. However, for some this was an eye-opening exercise as they reviewed the data that showed them how people were being paid and what potential gaps existed.
As developers and product owners, we were excited to provide an insight tool that could expose these potential key issues inside organizations; however, it was clear that the issues being uncovered were not about the analyses themselves, but about the data that underlie the analysis. For most organizations, this analysis exposed that they had a long way to go before reviewing potential pay gaps. The more important part of these analyses was that the data that derive them needed to be cleaned to ensure that the outcomes were more accurate.
This is not a small undertaking. As we have discussed many occasions on the HR Data Labs podcast, reviewing and auditing HR data is a constant exercise that needs to be completed in earnest in order to make sure the analytics gleaned from that data are at all accurate.
So for headcount, turnover, termination reasons analyses; in order to be able to glean any insight, the first step must be data integrity. This could take the form of an HCM settings review, HR process reviews or just a simple HR demographic data audit. Any or all of the above will help ensure more accurate insights, but unless all of them are undertaken the analyses should be used as purely directional instead of insightful.
This is especially the case of Diversity, Equity and Inclusion analyses. One cannot emphasize enough that data integrity is THE key to having not just appropriate analytics but that the conclusions that are reached are of value.
…And, let us be very clear, there are very few analyses that HR can help generate that add more value and purpose to an organization in 2021 than good diversity, equity and inclusion metrics. As Siri Chilazi said on the HR Data Labs podcast Season 2 – Episode 11 “We need to approach DE&I in our organizations with the same seriousness, with the same rigor, and the same data driven approach that we use for all other aspects of our business.”
Let’s say your organization has bought into the idea of building a people analytics capability. Great! First step is to start shopping for tools right? That was my thinking as well. Full disclosure…I’ve spent most of my career working for software companies that build and sell analytics tools. I always believed that it was far more efficient and effective for a company to buy an off-the-shelf product rather than try and build their own or cobble something together from different pieces of technology for the following reasons:
Your organization is focused on its core business, not developing an analytics software product
You may not have the right resources to do this and if you do, building what is most likely an internal, departmental tool is probably not the best use of their time
The initial building is only a part of the cost; the continuous evolution and maintenance of the tool must be factored into the cost
And perhaps what is most important in my mind…how would you even know what to build when you haven’t been studying and following the analytics market? Simply put, you probably aren’t experts because you are experts in your core business, not this
I have been revising my previous thinking. In 2021 we have a very different technology landscape than even five years ago which makes building your own custom tool a viable option. Think about the following pros and cons:
Development tools have come a long way. Low code development environments, shareable toolsets and a wide variety of custom development shops have brought down the cost of building and maintaining custom software compared to enterprise software vendors
Vendors are constantly innovating which gives you capabilities that you may need; upgrading to the new capabilities is up to you and your roadmap
Many times, it’s not the software that is the expensive part of the build, but aligning your data and architecture behind the tools that is the time and resource consuming problem
Tool vendors don’t have a specific customer in mind, they build for the largest possible market. This means you will pay for features you may not need or want
Software costs are high; enterprise software vendors usually have pricing plans that scale with the size of your business (e.g. your ability to pay) rather than the value you get from the tool
Most analytic projects fail not due to lack of technology but due to poor adoption; the more “bloat” in your tool and the more it doesn’t reflect your own unique needs the higher the risk your deployment will fail
Once you buy a tool it’s very difficult and expensive to move off of it, locking in the costs for years as the vendor will continue to add features that serve their market position, not necessarily your business needs
So how can you start your analytics journey and keep the most possible options open? Here’s a potential way forward:
Start by capturing the business requirements from the main people requesting data or the biggest current consumers of data. By requirements we mean understanding why they need the data and what they do with it more than how they use it
Don’t boil the ocean, but capture the requirements from the biggest users then spread out and validate/invalidate those requirements with others to enhance your requirements list
Armed with this set of business requirements you can effectively look for an off-the-shelf tool or work on developing your own solution (or via a partner firm) and not be distracted by features that may not solve a problem at your organization, helping you negotiate the best price for a product or build something that is a custom fit
Many organizations skip the requirements step when buying a vendor tool and just ask the major vendors to pitch. Instead, if you take the time to understand, document and confirm your business requirements you’ll be better positioned to drive the process whether you work with a tool vendor or a development project.
Listen to this week’s podcast for the full discussion on this topic as we discuss how to approach build vs buy with Dhruv Dang from Real Folk Inc.
What makes a good Analyst? Analyst is a very broad term used differently in every organization and job. But when you think of an Analyst what are the key attributes you think of? So often job descriptions and hiring managers focus only on technical skills. The ability to write kick-ass queries and wrangle data like nobody’s business.
But in many cases the “Analysis” in “Analyst” is missing. The critical thinking skills which enable a person to see contextual patterns in data and notice when something looks off. The ability to unconsciously notice the relatedness between several pieces of seemingly disparate pieces of information, and put them together into relevant, true-to-life pictures of information that tell the story of the business problem at hand. The communication and networking skills to dig into understanding significance and context in data.
I’ve worked with a lot of Analysts over the years who were technical wiz kids but couldn’t provide informational insights to save their lives. They lacked any ability to tell stories with their data. They could follow instruction steps, but when it came time to say what the output of those steps is saying, I’d get the deer-in-the-headlights look. It was like being able to efficiently gather all parts of an engine, without knowing how it’s put together or how to make it run.
On the other hand I have worked with Analysts who were a little slower and clunkier in gathering the data. But when they had it, they could gather all the nuanced context, draw the patterns, put it all together, and make it sing like Frank Sinatra. A true blending of both the art and the science of data.
No matter what the job description is, here are the attributes I feel make a good Analyst:
Artist – The ability to see pieces of disparate information and bring it together into a picture of relatedness.
Critical thinker – The ability to look at data and draw accurate inferences.
Communicator – The ability to reach out to others to gather contextual information.
Story-teller – The ability to put it all together and use compelling story-telling that engages the end user in understanding data output.
Technician – The ability to use available tools to query and compile the data.
This week our blog is brought to you by Pat Acheampong, the founder of Ahumka Digital. He is a self-proclaimed Tech Junkie, a Would be Philanthropist, an Entrepreneur, Bon Vivant, and Aspiring World Changer. With over 15 years of experience working in global businesses in the UK, North America, Australia, and Asia, he is currently head of Employee Experience and Service Delivery at Zurich Insurance in Hong Kong. Enjoy!
Employee Experience (EX), it’s one of the latest buzzwords in the employee culture, engagement, and wellbeing space. EX has been credited with everything from improving shareholder value to increasing business revenue, while reducing employee turnover, and customer churn. But how does this value get quantified for business executives using metrics they appreciate, rather than another warm and fuzzy HR initiative involving gyms, nap rooms, and free beer that ends up costing money? Here’s what the EX – Profit loop looks like:
Done right, the Chief Employee Experience officer replaces the Chief HR officer in a role that also encompasses elements of work facilities, and employee facing technology, similar to the role at Airbnb. Done not so well, it’s merely a re-badged employee engagement or even worse community officer role reporting up to the Chief HR officer through several levels. The difference is important, because so many firms look at EX as just another way of saying Employee Engagement. They are not the same, and here’s why.
The difference between employee experience and employee engagement lies in the difference between experience and engagement. Put simply, employee experience is holistic and encapsulates everything an employee thinks, feels and sees. In contrast, employee engagement refers to how positively an employee is occupied with or committed to the job. Employee engagement is one result of the overall experience and tends to be much more specifically associated with productivity.
Employee engagement tends to be associated with a narrow focus on technology tools, measurement or perks such as free food. These types of factors can be a part of an employee experience strategy, but they do not supplant a holistic and long-term approach to creating happy, loyal, and productive employees.
The Employee Experience is the sum of perceptions employees have about their interactions with the organization in which they work. Or to put it another way: EX = (Experiences + Expectations + Perceptions)/(Unmet Expectations + Unmet Experiences)
The business impact of employee experience
There is extensive research now available that shows the business impact of having a great EX in your company. Here are a couple of good reasons why a company should want to create a great EX for their employees:
Time and again, good employees are claimed to be the greatest investment by many firms, and with the ongoing war for talent, they’re hard to find. It stands to reason then that if you’ve fought so hard to attract and hire quality people, you don’t want to lose them.
Employee churn eats into the HR team’s time and also the business’s bottom line. Investing in a positive EX is crucial to creating an engaged workforce and is a very effective way of reducing staff turnover.
Research also shows that companies that invest in the EX are:
11.5x as often listed in Glassdoor’s Best Places to Work
4.4x as often in LinkedIn’s list of North America’s Most In-Demand Employers
2.1x as often on the Forbes list of the World’s Most Innovative Companies
2x as often in the American Customer Satisfaction Index
4x more profitable than those that are not measuring the employee experience
I would love to just throw down a series of metrics and tell you that these are the only ones you need to measure to evaluate a company’s EX. The reality is that with so many different ways of trying to improve the EX, the best way to do it is to implement, measure the impact on engagement, revenue, and costs, then learn from that for your next cycle. This build, measure, learn cycle is similar to the lean startup method. That said, there are a few metrics to look at that may indicate your EX and subsequent employee engagement are not as robust as they could be.
Employee Experience
Diversity index – Across a range of factors, not just gender. More diverse companies tend to perform better
Inclusivity score
Candidate NPS
Exit interview analysis
Alumni referrals
Employee referrals (people/products)
Recruiting channel effectiveness – which channels are providing the best recruits
Time to competence – How quickly are new hires becoming productive
Pulse surveys with text sentiment analysis
Employee satisfaction/engagement
ENPS
External review sites (e.g. Glassdoor)
Bradford Factor – This measures employee absence. The theory is that short, frequent, and unplanned absences are more disruptive, and indicative of employee issues than longer ones
Wellbeing measures
CSR
Employee retention
Regretted turnover
Rebound hires
Manager capability
Employee productivity
Employee productivity rate – As EX starts to go down, so does productivity
Learning outcomes – measure the usefulness of the training over time
Employee recognition – how effective are your programs
Meeting outcomes – to meet or not to meet. Too many meetings with little business value
Business outcomes
Profit per employee
Revenue per employee
Rate of innovation
Customer churn rate – Customers are more likely to stick with a company with a slightly inferior product than they are with poor customer service. Employees with low engagement don’t tend to deliver great customer service!
I hope these metrics have given you an idea of some of the ways you can measure whether your employee experience is producing business results for you. The full treatment of the subject could easily fill a book, and in fact already has filled several! This primer will hopefully give you a start in your EX journey.
Thank you again from the Turetsky Consulting Team (Now Salary.com) to Pat Acheampong founder of Ahumka Digital for being on the HR Data Labs podcast and for writing this guest blog post! We appreciate you sharing your knowledge and experience with us and our audience! To connect with Pat visit his LinkedIn and visit Ahumka Digital’s website to learn more.
One of the biggest challenges with any project is figuring out what steps to take to get started. If you know you need or want a people analytics capability you may already be sold on the value, but what should you focus on first? How do you build out your plan and who do you get involved? Often good ideas never get off the ground due to the overwhelming nature of the first few steps. We are here to help you out!
The biggest lesson that we can share and is echoed by others is that you don’t have to start from scratch. There are probably already some starting points available for you to build upon! For example, reports and dashboards may already be used by other groups or functions to report financial or business performance data. That means there are people internally who have some experience building data visualizations and getting them into the hands of users. Start with them! Even if the data isn’t people data there are valuable lessons you can leverage for how to build and roll out an analytics capability given your organization’s unique culture and structure.
Another thing to keep in mind is that technology is a tool, but rarely a solution. There are indeed some turnkey people analytics technologies available. However, it’s challenging to get budget approval for a major spend when you haven’t yet demonstrated the value of what you can do! It’s often more pragmatic and less risky to build with the technology you already have in place, no matter how modest, to show the value of people analytics. Our friend Mark Berry has written about his own experience with this approach (and lots of other good lessons too) and we here at Turetsky Consulting have experienced this first hand as well. Once you get some adoption, have some internal champions and know the specific limitations of your existing technology you’ll be in a better position to advocate for investment in that shiny, exciting analytics product!
Finally the main thing is to just ask your users! Who would your internal customer be and what are the business problems they are trying to solve? Rarely will someone ask for a specific people analytic (though I’m sure somebody will want to see turnover) but it’s more important to ask why they need this information or what they will do with it. This will give you insights into the “hidden” requirements that will help you build something that will solve actual problems rather than just add to information overload.
Our latest podcast with Vikas Saini also touches on this broad topic. Let us know what you think, whether you’ve overcome any challenges with getting an analytics project off the ground and what battle scars you are most proud of!
Our friend and recent podcast guest John Tardy wrote this guest blog post expanding upon his discussion of focusing on Big Value not just Big Data. John is an expert in business analytics and we are delighted he is sharing his expertise with us. Read on to see why Big Data may not be necessary for your company to gain Big Value from your data!
Big data gets a lot of attention and is a game-changer for many use cases. Big data is not really a classification of data, but is about tools and techniques used to deal with data that is high volume, is generated at a high velocity, or has a high variety in its structure. Of course, there is a continuum along each of these dimensions and no definitive line between big data and normal (or “small” or “little”) data. Beyond the hype, the goal is always to solve some business problem and unlock value from the data asset, but as we move toward scenarios that could be described as big data we need to shift approaches in order to deliver practical solutions.
While the nature of big data sources provide new opportunities, the foundational analytical approaches and methods are not all that different. Instead, there are specialized technologies and techniques to deal with the nature of big data.
So, is big data better? Should we look for big data sources and big data opportunities to maximize value? While the hype points in that direction, the answer is no. Real value always starts with the business objective or challenge and responding to those needs may or may not involve big data.
There are use cases in HR Analytics that deal with big data. Recruiting candidate portals involve many applicants, the opportunity to monitor click patterns and candidate experience, and unstructured data such as resumes. Employee engagement efforts may involve a variety of data sources including email communications for analysis of the connections between different parts of the organization.
There are also many use cases that do not naturally deal with big data. For example, HR Analytics deals with a company’s workforce. There are few companies that exceed a million people in their workforce and none where the demographics for each person would be considered big data volume. Rather than big data solutions in search of a business problem, we should develop a data-driven culture by making the most value of the data we have.
Dependable results begin with a data strategy. We need to ensure that the business objective is clear, that we understand stakeholder responsibilities, there is a common understanding of what the data represents, and that the quality of the data will support the objective.
Once that foundation is established, what are some of the key ways that we can unlock value from data that would not be described as big data?
Reporting
It’s easy to skip over this, but business has been leveraging the value of reporting since the beginning. Reporting is the simplest form of transforming data into information that the business can use. That doesn’t mean that it is always easy. There can be challenges in collecting the data, the quality, or the way it is structured.
Visualization
Visualization takes our ability to gain information from data to another level. It allows us to explore more complex relationships and interactions between components of the data. It leverages our human capacity to understand and analyze information and relationships using our visual senses.
Statistical Analysis
Our intuitive analysis of visual information also has limits. We can only analyze what we see, or what we are presented, so the manner of that presentation can lead us toward incomplete or inaccurate conclusions.
Statistical analysis allows us to test a hypothesis with mathematical rigor. Is the trend we see statistically significant or possibly part of random variation? If we see a relationship between two variables, how strong is the correlation? With what level of confidence can we accept the hypothesis? These are techniques that are very impactful on even relatively small data sets as they help support a conclusion and give strength to a call to action (or not).
Machine Learning
Machine learning is really just more statistics and while it is often used with big data, we don’t need big data to use the approach effectively. Machine learning uses the data (and statistics) to determine the relationships between different variables. The algorithm learns these relationships, stores them in a model, and uses them for prediction in the future. This can be contrasted with explicitly coding business rules into a system. Using an algorithm to automate the learning of relationships that exist in historical data has huge advantages, and also some disadvantages. That is a topic for another blog post.
In the end, the value derived is not related to the size of the data source or the approach used to analyze it. It’s about using the right data and the right approach to answer questions that are relevant and actionable to drive business value.
John, thank you for sharing these insights with us! To read more from John Tardy visit his website, Unlock Data Value or connect with him on LinkedIn. Or listen to our recent podcast discussion with John here!
Our blog post this week comes from Jon Burton, the Senior People Analytics Consultant from Visier. Visier is one of Turetsky Consulting’s valued partners (see more about our partners here). We appreciate the opportunity to hear from Jon who has performed over 100 People Analytics Deployments! So without further ado, take it away Jon!
“I’ve had the good fortune of being able to partner with many large multinational organizations along their people analytics journey. A common mantra I’ve shared with my clients is there is no recipe book for guaranteed success that exists today. However, having been able to witness and partner with more than 100 companies along their people analytics journeys, with clients ranging from a single person dedicating part of their time to leading an analytics deployment, to a large and mature team with dedicated change management resources to assist the team, I’ve noticed a few key patterns for success:
People analytics needs to connect to a business problem (or at least a key HR problem). When asked what business problem your people analytics solution will solve, I’ve seen far too many companies respond with a variation of the following “it will make us more data driven, allow us to bring together disparate data sources, or provide us with predictive capabilities”. Pushing data out absent a connection to a measurable business problem (or at least a key and measurable HR problem) will make it harder to achieve success.
Engaging an active and engaged executive sponsor who can set the vision and hold leaders accountable. I’ve seen many organizations invest in a people analytics team, solution and have an executive that wants to be more analytical, but does little to nothing to support the deployment. In my experience, key user groups will have a desire to become more analytical, but this is a more strategic goal that is often overtaken by the more tactical needs that pop up day-to-day. Which of the following scenarios do you think will result in a higher impact to the organization:
Scenario 1: Having a strong and engaged sponsor who can set goals and expectations for usage, review usage, provide verbal or monetary recognition to those who are making changes, and hold their leadership team accountable for usage not in line with their goals OR
Scenario 2: Having the head of people analytics fully own the deployment of the people analytics solution – trying to manage up with key stakeholders to demonstrate value and HRBPs who do not report into them, but competing with other key HR initiatives.
I think we all know the answer to which scenario is more likely to be successful, but I suspect many of us also more commonly see the other scenario.
Phase your deployment – don’t do a big bang to all users. A successful people analytics deployment will require more energy and resources from a people analytics team. Questions will move from – how do I, why does this data say X when I have seen or think Y, to more advanced questions such as am I better off hiring externally or finding internal talent to develop for a key role, or how can I meet my diversity goals for key leadership positions. These higher value questions will require more effort and partnership (and thus resources) from a people analytics team to support.
A key pattern I’ve seen in the most successful people analytics deployments is to go beyond HR with your deployment. This builds on the three patterns noted above and will require data that supports a need of the business, a key executive sponsor from the business, and is a key phase needed after HR. More clients that I want are hesitant to deploy outside of HR for a variety of reasons, but this has been a consistent pattern of success with many of my clients. A best practice I’ve seen with many of my clients (and consistent with my “phase your deployment” pattern) is deploying to HR and the business, then deploying to finance. This group is a key owner of numbers within an organization and is likely to have differences in numbers and perhaps assumptions as to what makes up headcount, so be sure to get them aligned on your efforts.”
Thank you again from the Turetsky Consulting Team to Jon Burton from Visier for being on the HR Data Labs podcast and for writing this guest blog post! We appreciate you sharing your knowledge and experience with us and our audience! To connect with Jon visit his LinkedIn and visit Visier’s website to learn more about how to get people answers on demand with Visier.
While creating, revising and deploying employee benefits programs, it’s important to give some thought to the data behind the program. There’s a LOT of valuable insight that can be learned just by making some simple choices as you decide on the technology used as the backbone of your benefits program. Leveraging your benefits data can help you see the big picture of what your people need from their benefits package. As you optimize this process and improve the technology, you can shorten the time between gathering data and implementing the insights gleaned from that data to better serve your employees. One approach is to invest in collecting and organizing your people data in a data warehouse, data lake, or other data repository designed for analysis.
By aggregating anonymous benefits data then visualizing and reporting on this data patterns and outliers emerge. Which benefits are most used? How does this compare over time or between different parts of your organization? If you have access to benchmarks is your experience an outlier compared to other similar organizations in your industry? All these insights will help you tailor the right benefits programs for your employees, ensuring that your spending is in alignment with what your workers value the most.
Insights and analytics will also help your organization proactively adjust your benefits mix as workers change what they use. For example, if you see that your company is spending more on diabetes related benefits there’s an opportunity to provide seminars or information specific to this condition or offer other non-medical benefits like dietary consultations. Employees can opt-in to what they want and over time as people improve their health, you may even see a reduction in healthcare costs!
Your people are your most valuable resource, so of course you want to take great care of them! Leveraging your data can help you better serve your employees, attract top talent, and better focus your expenditures. Want to get started, but aren’t sure how?
Data governance. Some think of it as the Ambien of the Analytics world. Guaranteed to put most people to sleep. Let’s face it— data governance is seldom fun. It’s not sexy, it’s not a bright shiny thing, and there’s seldom an adrenaline rush. But it is OH SO necessary in Analytics. It is the backbone of Analytics. Without it, the body would crumble into a heap on the floor. Simply put: it’s the difference between good data and bad data. And we all know having no data is better than having bad data.
So what is it? The Data Governance Institute defines it as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” More simply put, “Data Governance is the exercise of decision-making and authority for data-related matters.”
Data governance has many facets. Among the most important are:
Data definitions
Data formatting
Defining “source of truth”
Defining how certain data can be used
Defining who can use it
Data rules such as policies, standards, business rules, etc.
Key to data governance are strong Champions within the business. These are the people out in front of the organization gaining buy-in for the elements listed above. Also key are the Analysts behind the scenes slogging through data tables, definitions, use cases, etc. They find and serve up the information the Champions need. Both are thankless jobs and take special passionate people to execute. Never has a parent ever heard their child say, “When I grow up I want to be a Data Governance Analyst!” But make no mistake….. these folks are the unsung heroes of the Analytics world. Without them our data would be bad and meaningless. We’d be out of jobs and our companies would implode as the dearth of good data sucks all the Oxygen out of our competitive advantage.
So raise a glass and give a heartfelt toast to those who keep our data safe and clean!!
Does your company need help getting started with data governance, ask me.
It has been about 3 months since the last update to the Regulation S-K. Meanwhile, many organizations have publicly disclosed many new, material Human Resource metrics. We still haven’t seen much in the way of public discussion of the disclosure or lack thereof.
A few publications have done some good work with a summary of what has been disclosed. Harvard Law School Forum on Corporate Governance has a great study on disclosures. As part of the survey, they found that organizations are disclosing these metrics more than others. It’s no surprise that Headcount is the top HR metric to be disclosed. More interesting is that D&I is number 2.
