Workforce data is a mess! What can you do about it?
Workforce data is a mess! What can you do about it?
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 for 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 of many business domains. There is no reason it skip over HR. Therefore, HR leaders should consider the right tools and training to keep their team’s progress in this journey.
2. Understand the complexity of workforce data
Workforce data may be scattered in many platforms, both in 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 issue 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 towards data integrity is worthwhile. It yields in high quality data that enable meaningful analytics. HR practitioners should configure their systems in a way that prevent 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 mind, 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, that 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 lacks 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 hypothesis about the subject in question. In other words, before they dive into an 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’ expectation, 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 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 relationships 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 suggestion? Please share in a comment.
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
About the author:
Littal Shemer Haim brings Data Science into HR activities, to guide organizations to base decision-making about people on data. Her vast experience in applied research, keen usage of statistical modeling, constant exposure to new technologies, and genuine interest in people lives, all led her to focus nowadays on HR Data Strategy, People Analytics, and Organizational Research.