In this era of increasingly intense and volatile competitive landscape, human capital is arguably the most important and the most sustainable source of competitive differentiation and value creation. With technology having levelled the playing field, the new advantage for organizations lies in its ability to acquire, develop and retain top notch talent to churn out innovative products and services, as the commitment, motivation and special talents of the workforce determines the success or failure of any organization. Consequentially, an increasing number of organizations are investing heavily in programs to use data and analytics for all aspects of workforce planning, talent management, and operational improvement to enhance their competitive advantage.
Many companies make use of analytics to improve the management of their human capital. Google has set up a scientific approach for all people related actions from hiring to employee engagement and rewards; Best Buy data-driven approach for enhancing employee engagement for its store level interactions with customers has yielded higher financial performance; Lockheed Martin has been able to closely identify high potential performers for special programs with data driven performance tracking; Dow Chemicals has benefited from an extensive workforce planning model that incorporates employee performance, and attempts to account for promotions and career advancements; Harrah’s entertainment has been able to translate the analytical focus in its marketing to people decisions, using data driven insights to address employee development and morale; and with a detailed study of the work environment and employee engagement at a unit level, Sysco has been able to determine the management actions that will best impact business
The importance of having a strong talent analytics program can’t be overstated any further. Yet, the reality is that many organizations are still grappling with the challenges of getting workforce analytics initiatives off the ground, and for a variety of misconceived notions thus leaving behind a lot of locked up potential. Here are some of the myths and truths related to analytics:
Myth 1: Human resources is not yet ready for predictive and prescriptive analytics
HR analytics is not necessarily sequential in nature, although there are different levels of analytical maturity. While ad hoc reporting and descriptive analytics is all about analyzing small amounts of data to recognize patterns, advanced (predictive and prescriptive) analytics is all about testing hypotheses. And both could be performed in parallel, independent of each other. Predictive and prescriptive analytics doesn’t require HR analytics maturity — what is needed is the combination of right-skilled people and right tools to mine both small and big data for business specific insights.
Myth 2: HR Analytics mandates making big investments in technology
Technology is a key enabler; however, any investment in technology needs to be made only in the context of the wider organizational landscape before investing too much too soon. HR organizations could learn from the experience of other functions like Marketing and Finance to understand what investments might be needed in the short and long-term. Moreover, given a plethora of readily available low-cost flexible, interactive and user-friendly software solutions that are easily deployable, HR organizations don’t necessarily need to make upfront high investments to address their short-term needs.
Myth 3: There is unavailability of enough data
HR has probably more than enough data that could be put to good analytical uses. Data on salary, employee performance, competencies, absenteeism, etc. are all good examples of possible HR data that could be combined with actual business data like profits, revenue etc. to help answer business-specific questions. Again, HR organizations could probably learn from their peers in Marketing and Finance to understand how it could refine its efforts to evolve from more tactical to strategic role and quantify its impact on the overall business performance. Moreover, what’s important is to start small with whatever good data is readily available and make some quick wins while also developing organization-wide employee data governance program to put right control in place to ensure availability of high-quality data at all times.
Myth 4: The more the data, the better the insights
Too much information is¬ no information. Tracking all data that’s out there might provide a cushion for the future, however, there’s also a risk to be easily lost in the data deluge. This could often over-burden people, while crippling the data collection and analytical efforts so much so that nothing gets delivered. Thus, rather than collecting all data out there, it is better to store only relevant high priority data that can be linked back to the business objectives, while also continuing to add more data on top of it with time.
As is evident, these myths have kept many organizations away from fully realizing the potential of talent analytics, however, now with these misnomers dispelled, the landscape is clear of any confusion which they once generated. This clarity and solid understanding of the power of workforce analytics has the potential to radically transform the HR organization and bring it to the forefront of strategic decision making.
The future of HR
The Human Resource function is witnessing one of the most disruptive periods ever, thanks to the paradigm shift in the technology space. Rapid technological advances in the space of Big data and Analytics, Artificial intelligence, automation are helping transform the role of HR from a more traditional support group to a strategic business function, by enabling them to take evidence based decisions by leveraging insights based on facts rather than guess work. For example, if a company is thinking about opening a new facility in a new geography, big data could help the HR compare employment costs and skills availability in different locations. Or it could help HR develop a deeper understanding of the employee experience in company operations through corporate social media listening and thus, take targeted actions to improve employee satisfaction and productivity resulting in improved business outcomes
Big Data and analytics coupled with the recent advancements in AI, Machine Learning and Natural Language Processing (NLP), are enabling HR to effortlessly identify trends and patterns in workforce behavior and performance.
For example, HR practice always has involved leveraging structured (resume, qualification) and unstructured data (body language, emotions, speech etc.) and what big data can offer is the ability to do so with massive amounts of scale and processing.
Listed below are some of the key areas where AI and Machine Learning are going to have a reverberating impact on HR:
Predictions: AI will have an impact in areas like recruiting the right talent and staff training and development needs. AI has also seen major advancements in NLP over the last few years and this has made it possible to leverage human conversations as data sets for Text/Sentiment and Behavioral analytics. With data from employee engagement surveys, performance management information, call log data, social media posts etc, AI can offer company-and team-specific predictions like insights on types of employees that will be the most successful, the populations that is at a high risk of attrition etc. This would allow HR managers to account for potential job openings and preemptively hire new workers to more smoothly transition workflow. Besides use in employee engagement and hiring decisions, AI can also help workers to find out optimal opportunities based on their background, skills, education etc. to predict their market success rates, and additionally recommend them job openings that might be potential good fits based on their profile.
Workflow Automation: Another area where AI is poised to be a game-changer would be in solving workflow problems. AI will enable automation of simple HR processes like interview scheduling, employee performance reviews, employee onboarding, and even the answering of frequently asked basic HR questions related to policies and processes. This will free up HR team’s valuable time and help them focus on more strategic and impactful areas. This area will see AI software take on many different forms including traditional Web applications, conversational interfaces (video, audio), digital assistants, chat bots etc.
Personalized Recommendations: In the near future, AI will also impact HR in personalization of information, particularly around training and employee development needs, and performance & compensation management. Artificial intelligence can analyze the data from employee interactions and use that information to better tailor training sessions to the individual. Machine learning will also be able to record how well an employee responded to a training based on their performance and, this information could then be leveraged to determine the most effective method for future trainings. In the domain of performance and compensation management, AI could help HR in estimating the value created by individual employees based on their performance outcomes and sphere of influence, to drive personalized recommendations for compensation and rewards. AI could take on the reins to automate these currently heavily manual and error-prone processes, thereby freeing up substantial time for HR department towards strategy planning and execution
As is evident, this next wave of HR revolution is already underway, and companies will not only be armed with the insights and information to outsmart the competition, but they will also be able to build a much smarter and collaborative workforce by:
• Enhancing the power of human judgement through analytics
• Empowering the collective intelligence of organizations
• Forming a true labor market based on accurately observed workforce needs and skill gaps
• Building a culture of trust and transparency through timely and accurate information sharing
With the rapid advancements in technology and increased availability of diverse datasets on employee behavior and communications for delivering highly personalized recommendations, one of the biggest issues that organizations will be confronted with will be mainly around data confidentiality. While an organization’s objectives for applying analytics to employee data might be well-intentioned, their efforts may get mired in controversies should there be a conflict with an employee’s right to privacy.
And therefore, going forward it will be imperative for organizations to augment their Talent Analytics efforts with strict governance around possible usage and disclosure of private information concerning any organizational and employee data.