So, your HR data is dirty. It includes issues such as missing values in people’s information, typos that corrupt categorical variables, wrong labeling, duplicate records, errors, or records that you neglect to update. And so, you live with that. You accept the inevitable reality that you’ll never have the perfect data in HR. Sadly, the messy data in HR is sometimes an excuse to avoid People Analytics projects, hence, missing the opportunity to impact. However, HR data cleaning is part of your People Analytics Journey.

Start with imperfect data assets

But what if you start with what you have, i.e., your imperfect data assets? Your data quality must not be a barrier to a project. On the contrary! An analytics project is a practical step towards better and cleaner data. Whether you take a DIY (do it yourself) approach or implement a people analytics solution, experience shows that practicing People Analytics will improve your data quality. After all, no one cleans data just for the sake of it. However, when you have a purpose derived from a business challenge, and your priority to gain visibility into your workforce insights is high, you’ll be motivated to put the time, effort, and resources into data cleanup.

Clean and Tidy data is a milestone in your analytics project

Data preparation is a part of every analytics process in each vertical of your organization. But no one taught you that when you took analytics classes. The datasets you’ve been practicing were perfect and ready for analysis. In real life, you don’t get readymade, tidy data. It is a milestone in your analytics project, but you must get there yourself. From my experience in many quantitative types of research in organizations, this is an effective way of engaging with your data, understanding it, and reaching the most in-depth acquaintance with it.

Systematic errors lead to new procedures for data maintenance

Right after the crucial stage of transforming a business question into the hypothesis that comprises your analytics project and spotting relevant data sources, you pull and merge datasets and start exploring them. The exploration phase starts with finding gaps in data quality and fixing them. You will indeed find systematic errors. However, it will eventually lead you to propose or come up with new procedures to maintain data integrity and new configuration of HR platforms that your HR department will embrace later to prevent or continuously reduce errors.

Therefore, you should not be intimidated by the entire data lake of the HR department. Instead, focus on cleaning the datasets of your analytics project. When I meet HR professionals that have analytics questions on their minds, I am confident that there is a higher chance that they will find a way to improve data quality. So, the win is double! You gain less burden in cleaning up only the data you need, but HR data quality is constantly changing for the better!

How far should you go in data cleaning?

If you reached this point, you are probably raising two additional questions in your heart: how far should you go? And how to do it the right way? The first question is hard to answer; therefore, my obvious answer as a consultant would be: “It depends.” In workforce analytics, data accuracy may be more critical in some cases, e.g., when predicting personal outcomes. However, there are many cases in which you study groups and deal with means as estimates. In such cases, you can handle the inaccuracy and make the appropriate notes and reservations about it. The expectations about data quality should be an ongoing discussion that depends on the context of the data usage. The data quality definition will include accuracy, completeness, consistency, integrity, reasonability, timeliness, deduplication, validity, and more. Again, it is based on the context of your analysis.

The second question, regarding the best practice of data cleaning, is easy to answer. I incorporated the data cleaning best practice into my workshops to teach you how to fix your data and ensure your data handling is well documented and your analytics project is reproducible.

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Background

LSH: Thanks for joining us, Yael. Tell us a little about yourself, your background, your role in PA.

YE: The 1^st phase of my career was in health organizations. I have a Masters in Public Health (MPH) from the Hebrew University. My role as the coordinator of the non-clinical quality improvement program in a large tertiary hospital included many aspects of HR including change management, training, and recruiting. This led me to further my education in HR and to explore opportunities in the HR profession. In the 2^nd phase, I worked for a few years in a placement agency, and then, in the past 11 years, I worked at Microsoft. I started as a staffing specialist in the R&D center in Israel. 5 years ago, I moved to an HR analyst role in a new global team within the HR function (HRBI).

LSH: The People Analytics function at Microsoft is considered to be one of the leaders in the field. What can you share regarding the vision, mission, and principles?

YE: The vision is, in short, #DataDrivenHR. The mission is to enable Microsoft to make evidence-based decisions about workforce and culture. The principles of driving this mission include delivering insightful research and analytics, providing robust and consistent reporting tools in partnership with engineering teams, delivering timely and accurate measurement of companywide business and HR priorities, ensuring data quality, and upholding employee data privacy and security.

The role of technology

LSH: From your experience, what was the role of technology in the evolution of the People Analytics?

YE: Technology supports all aspects of people analytics. There are many examples: It promotes data security and privacy by ensuring the data is used only by authorized people. It enables the use of data by all HR professionals by an accessible format that is easier to understand and communicate even if you are not an analyst. Self-service data solutions, i.e., Microsoft PowerBI saves time and enable us to focus on deeper analysis rather than providing customized data needs. It also enables us to integrate data from various sources and create powerful data models, using visualizations during the analysis and for communicating insights and recommendations. We also can leverage data that wasn’t accessible before, by Workplace Analytics, a company product that enables us to identify collaboration patterns that impact productivity, workforce effectiveness, and employee engagement, based on data from Office 365. We also use text analytics to leverage a huge amount of data from responses to open-ended questions in employee surveys, objectively, and in several languages. More useful products of Microsoft are Yammer, which is an enterprise social networking service that helps us to communicate learnings and ideas, and Teams, which helps to manage resources.

LSH: Tell us more about your role: who were your clients, how did you support them?

YE: People analytics is an evolving field, and I was fortunate to partner with my colleagues in the HRBI team and with HR leaders and managers across the globe. Over time, I partnered with HR teams in both engineering groups and the sales organization. We leveraged an analytical approach to enable the business and HR to execute data-driven decisions in many aspects of the employment cycle, including hiring, headcount trends, diversity, retention, rewards, compensation, as well as candidate and employee sentiment. We partnered with HR to support ongoing HR processes as well as answering specific questions and hypothesis which were raised from their work with the business. One of the best practices was to set milestones during the analysis process in which we shared the work we did thus far, got the perspective and thoughts from our partners before we continued. This ensured that the deliverable answered the needs. Another major aspect of our work was promoting a data-driven approach in HR through one-on-one consultation, standard training on a data-driven approach, and tailored training on specific subjects.

LSH: What do you consider as challenges in your role with respect to technology?

YE: While it is a positive challenge, the ongoing development of knowledge in this field in general and of technology, in particular, requires ongoing learning. Ensuring that you take the time to learn with a very busy day to day work is challenging but also essential and very rewarding. Some other challenges are connected to the fact that technology helps with having more data available for use, thus increasing the need to make sure that we are using the data in an accurate way and to avoid bias throughout the analysis process. The availability of more data also raises the challenge of prioritizing work. One aspect of this is balancing between doing interesting analysis versus doing important analysis. Another aspect is balancing between the sense of urgency that is always driven by the business and the time it takes to do a thorough analysis.

LSH: What do you consider as a success story with respect to technology?

YE: These a great feeling of accomplishment when your HR partners share how they leveraged the tools and knowledge to promote a data-driven approach and seeing our work impacting business decisions. And of course, every time you succeed in a tough technical challenge with your data model or an effective visual you feel a great success. Personally, some of the meaningful cases of success were the use of technology, e.g., text analytics, to help in promoting general values of the unbiased approach, inclusiveness, and collaboration.

Data Ethics 

LSH: How do the people analytics team handle data ethics? Are there processes, partners in the organization, or outside?

YE: Microsoft has a volume of Privacy Standards dedicated to employee data, Data Use Framework for employee data, and Data Protection Notice for employees. Other internal tools and projects have more detailed communications. The company has created an Employee Data Governance Board to provide consistent company-wide direction and oversight on the legal and corporate policy issues reflected in the company’s privacy standards for processing employee personal data. This board is made up of a core team of privacy managers and attorneys for HR, Finance, and IT. Due to the need to combine employee data with business data, a data analytics governance framework was created and is used when embarking on a new people analytics project, to ensure that the right people are involved from the beginning, including legal, HR and any business stakeholders in addition to the people analytics team. We have a privacy manager on the team, who focuses on people analytics data privacy and security, and partners with other roles outside of HR on their use of employee data. We also have mandatory annual training for HR on privacy and data use, which is updated on a yearly basis. Training is helpful in framing the definitions and aspects of ethics, e.g., ensuring a purpose behind each data element and anonymization in reports.

LSH: What would you advise your colleagues whose employers are in an early stage in the field?

YE: Choose as your 1^st project subjects that are both important for the business and easy to succeed. Data is available regarding many aspects of HR. Use it to help with important business questions and with building the trust of the business in the data-driven approach. As long as you are aware of the limitations of the data you have, don’t be afraid of doing analysis with partial data. If we wait until we have the “perfect dataset” we will never start doing analysis. Also, showing the value from a partial analysis while being transparent regarding the limitations is a great argument for investing in more resources. As in any other aspect of HR, People Analytics requires ongoing continuous learning. Make sure you leverage resources and collaboration opportunities to continue learning.

LSH: Thank you, Yael! We are fortunate to have your perspective in our course and professional community!

The post The role of technology in the evolution of People Analytics appeared first on Littal Shemer Haim.

HR people must acquire better analytics skills. There is no question about that. When HR role vacancies are analyzed, this orientation is listed among the most important competencies. The data-driven part of HR practitioners’ work has emerged rapidly in the last years. All HR sectors must now leverage their data assets to make better decisions, and support all kinds of stakeholders, from employees to executives. They must also understand ML (machine learning) and AI (artificial intelligence) to have ownership of procurement and ethics in the implementation of HR-Tech. But how can HR practitioners close the gap? How can they up-skill and become more analytical?

 

A secret factor in learning programs

 

While most academic programs in the HR field still lag, and only a few exceptional programs focus on People Analytics, many other learning solutions can be found online. Wise and agile entrepreneurs, with a strong background in HR, analytics, and tech, already offer learning platforms and an excessive amount of content. But I believe that all of them still lack the secret factor that guarantees up-skilling HR.

Yes, a secret factor. But I’m going to tell you. After few years of training and mentoring HR people in the domain of People Analytics – with different levels of success, I must admit – I think I understand now how to prepare the HR team to embrace a data-driven mindset and People Analytics practices. By tracing the way HR groups in a variety of organizations have built their value chain in People Analytics, I discovered a new ingredient of success.

If you follow my work for quite some time now, you already know that I promote psychological safety in a learning environment to bypass the resistance for change. Or, in the word of a mentee testimonial, in a case study of people Analytics is SMBs – “We could afford to experiment with data, and making mistakes, knowing that we had the support of a professional framework”. However, crucial as it is, that psychological safety is not enough. The keywords here are “experiment with data”, our own data.

 

Experiment with data – our own data

 

I honestly believe that HR people can overcome their analytics barriers when they exercise. Online courses do offer a lot of exercise. However, only when HR people practice with real data, their own organizational data, they can bypass the obstacles. Only when they define business questions that are related to people in their organization and practically use methodologies that help them to make key employee decisions, they can succeed in developing the desired skills, and start interpreting and present analytics for people-related decisions.

Practice your own data. As simple as that. It doesn’t mean that HR people should become data scientists. In their training programs, they only need to follow the value chain of People Analytics, i.e., make first steps toward a quick win, with respect to business questions, acquire in-house resources and abilities to executing the first analytic project within the organization, and use the first analytic project to impact the organization.

From my experience, exercising based on real questions, challenges, and data is an important motive. Moreover, such training does not only enable up-skilling but may also be the actual foundation of a data-driven HR.

When done in groups, in which each member contributes according to actual role or aspirations, and when collaboration is established with People Analysts or other relevant roles within the organization, HR up-skilling in the analytics domain is guaranteed.

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Educating and mentoring HR professionals to embrace the practices of People Analytics is a challenge. There are barriers, and it takes time and effort to overcome them. However, one issue remained unsolved for years: The lack of open HR data to practice on. Although there are many inspiring case studies of People Analytics, obviously, organizations don’t share their people data for the sake of learning. Simulation-based data may be an alternative, though usually it is oversimplified and lacks real or interesting patterns to explore.

 

A Practice with Open Data

In my recent teaching initiatives, e.g., the People Analytics session in Lahav Executive Education at the University of Tel Aviv, I wanted to demonstrate HR managers that their academic background, professional experience, and their common sense, is enough for exploring organizational occurrences and effects based on data. HR managers don’t have to become data scientists in order to conduct People Analytics projects. But they do need to communicate with Data Scientists, bring them business questions to study, and request research outputs. For that reason, I constantly search for open HR data and use it in learning sessions. Fortunately, I could present a case study of Gender Equality, that theoretically and methodological was based on a real project, but the analytics part was conducted on open data that was offered by other organizations.

For the Analysts and Data Science enthusiasts among my readers, it is worth mentioning that although it is not the first time I demonstrate People Analytics practices based on open data, this time my objective is a bit different. I did not use practical Machine Learning in this case study. The analysis process was based on research methodology and Statistics that a Bachelor of Social Science, i.e., someone with a B.A. degree, should understand and can comfortably communicate. Nevertheless, I used R for my analysis, because I believe that HR people who may not have learned or used R and manage to receive analytics from an inner supplier or an outsource service, should have a grasp on how a desktop of a Data Scientist looks like, and what in the functionality of R Studio makes it so popular.

My source and inspiration for the dataset was Montgomery County Maryland’s employee salaries in 2017. The open data included annual salary information such as gross pay and overtime pay for all active, permanent employees, and some demographics. The reason for opening this dataset to the public is the Digital Government Strategy of Montgomery County Maryland which aims to serve residents, employees, and other partners better. In this case, it serves the purpose of education, in an open-source community of People Analytics students, professionals, and enthusiasts. However, the dataset used is anonymized and randomized.

 

Gender Pay Gap

Pay transparency is among Global Talent Trends in 2019, according to LinkedIn. But “Transparency isn’t the goal. The goal is paying everyone fairly”, as Anil Dash, CEO at Glitch was wisely quoted in the report. Transparency forces Organizations to make sure they keep the compensation balanced across genders and other groups’ characteristics. Although people share salaries on sites like Glassdoor and LinkedIn, only 27% of companies are transparent about pay. The first step to establishing pay transparency, as recommended in LinkedIn’s report, is to conduct an internal audit, and explore how the company’s pay compares to competitors and whether it has a major pay gap across gender, race, and those in similar roles. If significant inequities are found, a detailed plan to fix them is recommended.

A pay gap audit or exploration may be a People Analyst’s task. However, in the People Analytics project, descriptive statistics is not enough. We need to go deeper into understanding the reasons for our findings and the directions for a solution. In the following analysis, I included some diagnostics and Inferential Statistics, to understand the reasons for the patterns in pay data. I assumed that as any American public organization, Montgomery County Maryland is subjected to some kind of strict regulation regarding equal pay. But only going beyond the basic descriptive statistics enabled me to find some interesting patterns. So, without further ado, let’s explore the findings.

Gender Pay in Montgomery County Maryland

“Telling a story with data” is almost a cliché in our field. Nevertheless, there is no substitute for the exploration of data visually, before moving on to test the hypothesis. There are plenty of visual tools out there. The great thing about R, however, apart from its price (free!), is the flexibility it enables in creating the story and reproduce it again and again as the data is updated. In the following description of my analysis, I did not explain every term in statistics, since I assume the readers learned them on their undergraduate studies. But “no one remembers”, right? So, the links in every statistical term may walk you through a “memory refreshment experience”, if you choose to follow them. 

I started my exploration, as shown in Figure 1, with the pay distributions. I intended to present, in a single slide, both common and separated gender pay distributions. I also wanted to explore both indications for center and dispersion, without losing information about outliers. So, I placed a boxplot near a histogram with a density plot and ordered the genders vertically, one on the top of the other, so the comparison would be easy for the bare eye.

If you look closely in Figure 1, you’ll notice a little difference between men and women, both in the deviation of histograms from the shared distribution, i.e., that normal approximation curve, and the center of the boxplot, which represent the median. Running t-test resulted in a p-value below 0.05, which means that on average, the pay differences between men and women are statistically significant. This significant result is impacted by a large number of cases in the dataset (about 9400 employees). The average yearly pay gap is about 4.5k US$. (I repeated the visualization and t-tests for all pay variables I had in my dataset, but for the purpose of simplicity, let’s remain with only one variable).

 

Figure 1: Gender Pay Distributions

Obviously, the average pay gap is not the whole story. Additional variables should be added, to deeply understand the source of the gap. Adding background variables, e.g., full vs. part-time job and tenure may change the story. For the analysis presented in Figure 2, I had to create new variables based on the raw data. I mention it because it is important to take into consideration that, usually, the data you download from your systems won’t be ready for analysis. A significant part of the Data Scientist time will be invested in cleaning, mounting, and preparing the data for the analysis.

Exploring gender pay averages across tenure ranges reveals that while both genders are promoted while gaining tenure, men are promoted with higher rates, as the different slope indicates. Running ANOVA reveals that the interaction between the gender and tenure variables is significant, meaning that the different slopes are not a random occurrence. Such interaction was not found between gender and full/part-time. However, we do witness full-time employees promoted at a higher rate, in comparison to part-time employees, as slops indicate. This interaction, between full/part-time and tenure, is also significant.

 

Figure 2: Gender effect, Tenure effect, Full/part-time effect

 

But who holds most of the part-time jobs? Apparently, the proportion of part-time employees in Montgomery County Maryland is significantly higher among women (18%), in comparison to men (3%). In other words, the accumulative gap between men and women throughout their careers, as they gain tenure, may stem from their assignment in full and part-time jobs. In a Linear regression model that explains the annual salary by gender, assignment, and tenure, the gender is not a significant predictor, as opposed to the other variables: tenure and assignment. Together these variables explain 37% of the variance of annual pay, which is a fair result, but still, other factors impact it too. Positions and occupations may be among those factors.

Indeed, a critical reader may raise a question about the male’s and female’s occupation. The dataset includes some occupations with both genders and other occupations with only men or women. I repeated the whole analysis after screening out those male and female occupations, and I got similar results. Yes, analysis within each occupation is also needed. However, there are 390 occupations in this dataset, so I prefer to leave this task to People Analysts in Montgomery County Maryland. (For dynamic charts of this case study, by departments for example, please visit my GitHub)

 

Additional thoughts

The gender pay gap analysis in this article is straightforward. Most HR managers with a B.A. education can handle it, with a little help from a data scientist on some occasions. I encourage HR practitioners who start their journey in People Analytics to practice this analysis. The data is available, and the insights may be vital. According to Gartner’s Digital Employee Experience Survey in 2018, #1 in the top ten memorable experiences that affect employee experience is “Being discriminated against at work”.  No doubt that transparency and closing the pay gap is crucial for employee engagement and indirectly to employer branding.

My last note may be the most important. Women still don’t get their fair share, according to an analysis by Visier. Data from this People Analytics platform reveals that the gender pay gap widened in 2017 rather than becoming smaller: In 2016, women made 81 cents to the dollar a man-made, but in 2017, women made 78 cents to the dollar, according to Visier data. Organizations still have a long way to go to close the gender pay gap, so why don’t you start by analyzing the situation in your organization?

(To explore the R code used in this article, check my GitHub).

Related Course

The People Analytics Journey

An overview of future role of HR leaders in improving business performance by informed decisions about people based on data. People Analytics transforming HR; The Role of People Analytics Leader; Case Studies and Simulations; Emerging trends of HR tech.

The Syllabus

The post Gender Pay Gap and People Analytics: A Practice with Open Data appeared first on Littal Shemer Haim.

Did you ever cut an onion while cooking? If you did, I bet it made your eyes tear. The well-known burning sensation in the eyes is simply a reaction to the sulfur that spread in the air when you destroy the onion’s cells. But what if you peel the layers of the onion one by one? There will be less damage to the onion’s cells, and therefore, fewer tears in your eyes.

The hierarchical definition of People Analytics

People Analytics is just like an onion. This domain of expertise has many practical layers. Its hierarchical definition includes at least five perspectives: C-level and business perspective, HR processes, data in HCM and other HR-tech platforms, data science methods, and the daily activities of the People analyst. If you try to cut through the entire hierarchical structure of this multidisciplinary profession, it will be so hard to grasp, that it will make you cry. Well, at least metaphorically. However, if you explore the layers of this definition one by one, you’ll get a thorough understanding that eventually will enable you to impact your organization – Tearless guaranteed!

The variety of roles that are involved in any People Analytics project within the organization contributes to the complexity of this practice. Notice that in effect, each layer in the structure of People Analytics definition is influencing and being influenced by the nature of the layer on its top and bottom. For example, the data stored in HR-tech platform influences but is also influenced by the HR processes that generate it. This complexity implies challenges in People Analytics activities.

The language of business

A crucial part of your challenge in People Analytics is the effort to establish communication between different professionals. In the hierarchy illustrated in the People Analytics definition, you have a C-level perspective above HR processes and data science beneath. Therefore, you must ensure communication between two kinds of professionals: executives and data experts. I consider this communication as an important layer to peel, in the onion metaphor.

The People Analytics journey enables HR managers to become more strategic because they speak the language of the business. Obviously, they must do so, since People Analytics is all about impacting the business by the right questions and insights derived from people’s data. However, they can support decision making, only if the people who are in charge of data science projects can communicate effectively with business leaders.

The role of the People Analytics leader is considered sometimes as a translator, the enabler of this communication. The People Analytics leader must make sure that the data scientists understand the business needs in workforce-related analysis, come up with the right business questions to analyze and return with the best storytelling with data. The People Analytics leader must also make sure that the owners of HR operations, who may be in charge of BI in the domain of workforce, understand the needs of data consumers among the executives.

Demystify People Analytics

On the other hand, you have executives. They need support too, on their journey to the data-driven organization. Perhaps they need you, the People Analytics Leader, to demystify this domain for them. Business leaders may be familiar with quantitative methods in other domains, e.g., Marketing, Finance, and Operations. But how deep is their understanding of statistical models and algorithms in the field of the workforce? Do they really know how to interpret the insights derived from the shiny tools and methods of People Analytics to the right decisions about people, careers, and employee experience? They surely can benefit from learning some new terms, to avoid the inconvenience experience involved in misunderstanding concepts, methods, and technologies.

Democratizing data is a process, not an outcome

Enabling the communication between the data professionals and the data customers is actually a part of the process of democratizing data in your organization – a significant part of preparing a vital portion of your workforce for the future. Though democratizing data is relevant to all parts of the business, the domain of workforce apparently lags. Implementing tools for telling stories with data within the HR department is important, but it is certainly not enough. The gap in communication must be close too. Closing the gap will enable the process of getting the business question right, ensuring data integrity and transparency, iterating to find the best algorithms, and getting people to use the insights in their decision making.

Therefore, the HR leaders, who are also responsible for learning, must lead simultaneously, toward data literacy among the management and toward understanding the business among the data pros. People Analytics is not about software or cloud services. It is a mindset that should become common throughout the entire organization. Closing the communication gap between executives and data pros is an important part of educating the workforce, and it may save a lot of burdens, just like peeling the onion’s layers, one by one.

Related Course

The People Analytics Journey

An overview of future role of HR leaders in improving business performance by informed decisions about people based on data. People Analytics transforming HR; The Role of People Analytics Leader; Case Studies and Simulations; Emerging trends of HR tech.

The Syllabus

The post Your journey to People Analytics makes you cry? appeared first on Littal Shemer Haim.

The post Key takeaways from People Analytics World, London 2018 – Part 1 appeared first on Littal Shemer Haim.

Workforce data are the molecules of People Analytics. No predictive model, diagnostic analysis, or visualization can possibly be created without proper and relevant data. Anyone who appreciates the advantages of data-driven HR should stress quality in HR data. However, when I start a conversation about data with HR leaders, many of them spontaneously respond with a sigh. They know the naked truth: HR data is a mess! Nevertheless, there is so much that HR leaders can do to cope with this challenge, starting today. Let’s start with the following six suggestions, which hopefully will inspire us to face this painful issue.

 

1. Understand the advantage of workforce data access

Workforce data is everywhere in the organization: HRIS, ATS, CRM, LMS, etc. Business leaders need insights, which derived from that data, to improve business performance. A huge variety of technological solutions are available today, which enable HR people and other non-technical professionals to create insights from the data. The missing link is a desire to access the data, and to use it in actionable ways that reveal new opportunities for the company. The ability to access the data and use it properly will empower HR people to have ownership and responsibility of workforce data, and encourage them to maintain data quality in order to support informed decisions in the organization. Data democratization is a demand for many business domains. There is no reason it skips over HR. Therefore, HR leaders should consider the right tools and training to keep their team’s progress on this journey.

 

2. Understand the complexity of workforce data

Workforce data may be scattered on many platforms, both in the HR department and in different lines of business. It comes in many formats. Parts of it are structured, while other parts are unstructured, e.g., text fields from employee reviews. Sometimes, the data is not recorded digitally, due to certain difficulties or priorities. In other times, when the data did get recorded, old records are deleted or replaced, due to database structure constraints. Different users may have different needs, which a shared platform does not support, therefore some of them may keep supplement records, e.g., in Excel sheets. Furthermore, when new needs emerge, relevant data may be recorded elsewhere, in different systems. Hence, one of the most challenging issues is the different unique identifiers in different data sources, which sometimes makes it impossible to automatically combine data by matching field. Understanding the complexity of workforce data is the first step to deal with it. HR Leaders must start to get to know workforce data as much as they understand HR processes.

 

3. Prepare to improve workforce data

The struggle toward data integrity is worthwhile. It yields high-quality data that enable meaningful analytics. HR practitioners should configure their systems in a way that prevents or reduce errors. For example, they may want to eliminate mandatory requirements for fields that are not always available at the time of data entry, consolidate fields with duplicate information, and remove fields with no immediate purpose. When analytic questions are on HR people’s minds, higher the chances that they configure their system in a way that contributes to improved data quality. However, some of them still need guidance in system configuration and data entry processes.

 

4. Prepare to integrate data from different sources

Throwing all the data into a data lake and hoping for an amazing insight to emerge is a nice fantasy that is about to fade away. Instead, you must pick an important business problem to solve, identify and gather relevant data into the data lake, which will include HR structured data, HR unstructured data, and a variety of data from different lines of business. This involves huge challenges: First, you don’t want to disrupt anything in your business processes. Secondly, assuming you found the data, you must deal with duplications, versions, incomplete data, and issues of unique identifiers. And finally, you must do it fast enough, to face managers’ demands, in accordance with organizational and business challenges. You may find out that IT is not available to help with your initiatives, and worse, IT may lack the HR context to understand the data. Therefore, HR leaders should start reassessing their platforms and exploring the ability to integrate them with other solutions, e.g., their ATS and LMS. They must also be aware of other tools that may be needed: blending data tools (e.g., Alteryx), advanced statistics tools (e.g., R programming), and visualization tools (e.g., Tableau).

 

5. Prepare to build stakeholders’ trust

Data scientists and People Analysts usually have a hypothesis about the subject in question. In other words, before they dive into analysis, they acknowledge their expectations about the results. They must start their exploration with a question in mind, otherwise, they would not know where to start in the infinity of analytic directions. However, this is not always the case with other stakeholders – employees and managers. They may be surprised, shocked, confused, or embarrassed when exposed to the findings. Therefore, it is important to know in advance something about their expectations, attitudes, and beliefs. Whether the analysis supports or disproves stakeholders’ expectations, the analyst should dig deeper into the data, to provide supporting details. An analyst who anticipates potential questions and concerns can be better prepared with answers and contributes to stakeholders’ trust.

6. Remember the cause: Serving the organization’s goals

For HR to take a strategic role in management, it needs to broaden the scope of its analytics agenda to business questions. By blending people’s data with business data, HR can provide insights beyond HR metrics and may answer questions such as: How good is the workforce in executing the business strategy? It can start to analyze the connections between employee behavior and productivity, predict business outcomes by competencies, and measure the impact of various training programs.

I believe that any HR leader experiences these six angles in the ride to data-driven HR, but the mixture and volume depend on the phase in the journey. Any other suggestions? Please share it in a comment.

References:

Bernard Marr, “What Is Data Democratization? A Super Simple Explanation And The Key Pros And Cons“, forbes.com
Vangie Beal, “Structured data“, webopedia.com
Stacy Chapman, “Silos in Talent Data – Sigh“, linkedin.com
Alyssa Ruff, “How Smart HR System Design Leads to High-Quality Data“, analyticsinhr.com
“Data lake“, en.wikipedia.org
Roger Nolan, “Digital Transformation: Finding Your Data is Half the Battle“, business2community.com
Eric Knudsen, “3 Rules for Building Stakeholder Trust in Your HR Data“, visier.com
Rupert Morrison, “Five mindsets HR needs to get right to deliver business impact“, http://blog.orgvue.com

Related Course

The People Analytics Journey

An overview of future role of HR leaders in improving business performance by informed decisions about people based on data. People Analytics transforming HR; The Role of People Analytics Leader; Case Studies and Simulations; Emerging trends of HR tech.

The Syllabus

The post Workforce data is a mess! What can you do about it? appeared first on Littal Shemer Haim.

I was honored to be first recognized as a leader in People Analytics back in 2017 alongside many respected and inspiring colleagues. But unfortunately, in those early days, most of the recognition was aimed more at men than women. So, I decided to change this. 

I started a personal challenge to salute 365 women, one for each day of the year. There is only one IWD (International Women’s Day), but for me, every day is a woman’s day. Though the list of my data heroes became longer, I realized it wasn’t enough. We needed professional and inspirational role models of women in the field. 

As years went by, the challenge turned from a list to interviews with women who generously offered a glance into their journey to become exceptional practitioners in People Analytics. I hope this version of my challenge is a snowball of inspiration that will encourage more women, particularly HR professionals, to enter the data world of HR. 

So, let’s celebrate and share this!

Being A Data Scientist in The HR Department

A successful data science function in the HR department requires balancing the analytics maturity of the business and HR leaders with the data scientist's skills. It is essential and fascinating to explore how data science and HR needs are knitted.

ABCs of People Analyst’s success

The culture of the People Analytics community is remarkably open. While datasets, analytics, and insights are restricted, experiences, resources, and advice are generously shared. It inspired me to list the ABCs of success: autodidact habits, business understanding, and coding skills.

Leveraging workforce data as it was a state security project

An interview about People Analytics with a Lieutenant Colonel in the Israeli Military intelligence - A rare chance to explore practices in the most secure organizations, and to discuss experience with AI, business insights and ethics.

The role of technology in the evolution of People Analytics

An interview with a former HR analyst at Microsoft, discussing the role of technology in People Analytics and data Ethics: challenges, success stories, and advice - one of many perspectives we had in "The People Analytics Journey" course.

HR Challenges in A Data-Driven World

A valuable part of my tailwind comes from my global community of experts who dedicate their careers to helping executives and managers, especially in the domain of HR, to become more data-driven. Here's an interview with one of my data...

People Analytics Leader – Survive Your Onboarding!

Most case studies that we encounter represent mature stages. However, most new players in this rising profession struggle with different challenges. The onboarding of People Analytics Leaders is fascinating and worth following. Here's one example.

People Analytics in SMBs: Small Data, Huge Impact

This interview with an HR manager in a fireside chat during a People Analytics class offers an introspective approach to a joint journey, as a mentee and mentor: the motives, the obstacles, the quick win, the team participation, and more.

Can you reinvent career development by using analytics?

An interview with a professional in the field of People Analytics, Learning, and Organization Development, about career-growth challenges and internal mobility.

The post Challenge: 365 Women worth watching – in Data, People Analytics and HR Tech appeared first on Littal Shemer Haim.

Mark is the CEO of a fast-growing company. He had fund-raised millions of dollars and received a lot of recognition in the press. From the outside looking in, Mark is a success story waiting to happen. Yet, he is having trouble sleeping. His company is about to release a major product, but many of his best engineers are leaving the company. He lost 24% of his R&D department in just two quarters. How did this happen? And how was he so unaware of the way his employees were feeling?

Getting to the root of employee turnover

The costs of employee attrition are high, especially when losing a high performing employee. They take with them extensive knowledge of the company, relationships within and outside the organization, specific business practices, and much more. The important questions Mark must ask himself now are: What is happening in the company that is making people want to leave,? Who can he go to for help? Is this something he should discuss with human resources?

HR and People Analytics

Unfortunately, many HR leaders have not acquired yet the tools needed to analyze their people data in the right way. They can hold conversations with their employees and try to make sense of certain trends, but in the end, they would have trouble providing Mark with evidence-based answers. This is where People Analytics is becoming increasingly important to growing companies, and particularly to HR. People Analytics expert explores, infers and communicates significant data patterns to initiate and support strategic business decisions related to people in the organization.

Common questions that people analytics can answer include: How can we find more top performers? How can we better engage our highest performing workers? Which qualities do amazing teams have? Which people are most likely to stay in the company or leave it?

Two things are happening in the domain of HR and People Analytics: First, it is being used to predict future behavior based on present data. Secondly, it is being used for the sake of both HR and other departments in the company. Gaining answers to these types of questions can save a company from significant loss.

Why many CEOs need the People Analyst?

In an increasingly competitive space for top talent, a new profession has emerged: The People Analyst. This analyst is in charge of combining all the data a company has on its people to tackle business challenges. Much of the work of the People Analyst can be supported by machines. Many HR-tech companies understand the importance of data in knowing how to manage and engage people effectively. For example, bob, an all-in-one, cloud-based HR and benefits platform, is making headway in this field. This platform consolidates all the rich real-time data of a company into one system and gives decision-makers valuable insights into their employees from this data.

What about privacy and data protection?

As more people are becoming aware of the importance of protecting their data, privacy and data protection plays an important role in People Analytics. Regulations are being put in place to offer rights over employees’ data: Employees must be informed about data usage, data transferring and period of storage. Anyone who analyses employee data will need to follow these regulations.

People Analytics can help

Had Mark hired a People Analyst who can access the right platforms and tools, he would have been able to track how his top performers feel and behave and could have taken preventative measures to stop them from leaving. If you are a company in growth mode, you know how crucial is to retain your employees. People Analytics serve as an important way for you to stay on top of everything that is happening in your company.

The post People Analytics in fast growing companies: Lead start-ups to data-driven HR appeared first on Littal Shemer Haim.

(A version of this article was published in TLNT magazine)

Think about data that you share at work, in the most personal sense. You share with your employer, and sometimes with potential employers, so many aspects of your life: details about your professional path, your personal status, health care, social-economics, legal and geographical background. You also agree to share information about what you do in different times and places, who you meet, what information you consume, and so on. Moreover, you leave your digital footprints on the web, social networks, and different apps, where data reveals to employers a lot about you. Did you ever consider how data might affect you at work? How does your employer actually use the data about you, and how technology enables it? What is allowed to do with your data, and what is considered crossing a red line in terms of ethics and regulations?

AI (Artificial Intelligence) and ML (Machine learning) are two buzzwords that dominate the HR tech world today. We don’t know yet if there is a bubble in this field or rather a huge influence on management practices. Nevertheless, the common opinion among professionals is that managers will make better decisions, more informed decisions, related to the workforce, by using predictive algorithms that, for example, fit candidates in jobs or let employers know who is at “flight risk.” There are tons of discussions about this subject, but mainly from the organization’s point of view. What I’d like to do now, for a change, is to take the employee perspective.

 

Should employees worry?

If you tried to land a job lately, perhaps you had a video interview (e.g., by HireVue), or you were asked to play some mobile games (e.g., by Knack). These technologies, which probably offer you a nice experience as a candidate, actually enable organizations to predict your performance in certain roles, basically by pre-exploring reactions of high and low performers in those exact roles. As a candidate, you’ll probably consent to participate in those practices, even though you don’t know exactly what data these machines collect about you and what is the secret predictive model they use backstage.

I’m not saying that predictive models are bad. On the contrary, I believe that in general, a machine that fits the right person to the right job and does so better than a human whose perceptions may be biased is actually positive, not only for organizations but also for employees, since they may have a better chance to thrive in the right roles. However, anyone who has some general knowledge about ML can point to the confusion matrix and demonstrate that algorithms are not perfect, or more precisely, how much imperfect they are.

Why are predictive algorithms not perfect? There are many technical and statistical reasons, but the one that concerns me, in this context, is the possibility that human biases affect seemingly unbiased machines. The promise of ML and AI was that the more information we feed these sophisticated computer algorithms, the better they perform. Unfortunately, when the input data reflects the history of an unequal workplace, we are, in effect, asking a robot to learn our own biases. Garbage in, garbage out, right?

Such unfortunate effects can easily occur in the workplace. For instance, if an analyst explores people who were promoted in the organization for the last decade and decides to use their data to predict high performance, it might result in a model that exclude minorities from predictions about high performance, since maybe minorities were rarely promoted in the past due to social biases or discrimination. This example may be extreme, but it can underline many other subtle possible occurrences.

 

Who will defend employees?

Defense (and self-defense) starts with awareness. Indeed, the awareness of data protection and privacy is increasing, and influencing society in general, particularly in regulation. Employee rights are broadened these days in the context of workforce data, although not evenly in each corner of the world. In the EU, a new privacy regulation, the General Data Protection Regulation (GDPR), was published lately (and will be enforceable starting May 25th, 2018). It has serious implications for any employer who processes its employees’ and potential employees’ data, whether it is data regarding work environment or internet behavior. Among many issues, the GDPR offers employees additional rights to reinforce control over their personal data, e.g., extended access and rights to be informed about data usage, data transferring, and period of storage. The new regulation is currently covered by legal experts, and anyone who analyzes employee data will soon start to consult legal departments regarding activities that did not require consultation in the past. In Europe, a new organizational stakeholder emerges – a Data Protection Officer (DPO) – and will be involved in analytics projects.

However, in my opinion, compliance with the GDPR is only a starting point. It will surely force awareness of the HR analytics team to privacy issues. But although it aims to protect privacy, I believe it will also influence employees’ behavior, and HR analytics practitioners will have to respond: When people start exercising their rights and request access to their data, People Analysts will be ready in advance to give them comprehensive information about their data usage. When employees start asking to correct or erase their data, employers will request more transparency and security from HR software providers. Organizations will ensure that they process only the personal data that is necessary for the specific purpose they wish to accomplish, and therefore, they’ll need long-term planning and more serious considerations. This will move the field of People Analytics forward. The implications for employees and candidates: Transparency! But not only…

 

Beyond transparency

I believe that eventually, even if it will take a few years, the People Analyst role will include more components of procurement. Analysts will make less programming on their own and be experts in HR tech and analytics solutions. They will learn, for the sake of regulations and ethics, to ask vendors hard questions and be more critical about model accuracy and data privacy, and therefore, they’ll contribute not only to a culture of a data-driven organization but also to a safe work environment regarding employee data. Employees and candidates, for their part, will judge employers, in addition to Employee Experience perceptions, by employer ethics in data management, and when feeling secure, they’ll be more receptive and enthusiastic to participate and cooperate with AI and ML to influence their career path.

 

References:
Kevin Markham, “Simple guide to confusion matrix terminology,” dataschool.io
Stephen Buranyi, “Rise of the racist robots – how AI is learning all our worst impulses,” theguardian.com
Arnold Birkhoff, “9 Ways the GDPR Will Impact HR Data & Analytics“, analyticsinhr.com

Related Course

The People Analytics Journey

An overview of future role of HR leaders in improving business performance by informed decisions about people based on data. People Analytics transforming HR; The Role of People Analytics Leader; Case Studies and Simulations; Emerging trends of HR tech.

The Syllabus

The post Employees in the big data era: Will you let robots determine your future at work? appeared first on Littal Shemer Haim.