Deploying Business Intelligence and Analytics in HR

A Deloitte Human Capital Trends report from 2015 found that very few organizations were focusing on people analytics to tackle complex business and talent issues. This year's edition of the same report records an increasingly integrated approach to workforce analytics and warns of a potential backlash if companies go too far with people data.

Somewhere in between those two sub-optimal scenarios lie the sweet spot for people analytics. This is the point where sophisticated analytics meets data from a range of organizational and external sources to balance effective human capital management with achieving strategic business outcomes.

Previously, there was a limited focus on Business Intelligence (BI) and analytics mainly as a means to, as Forbes magazine describes it, cost-justify HR programs. There has, however, always been some degree of integration. Interoperability standards like SCORM, Tin Can and WaterShed, for instance, enabled data transfer between learning management systems and other BI and talent analytics solutions to align learning with workforce development objectives.

But integrated people analytics represents a generational shift in data-driven decision-making in HR. In this new model, learning & development initiatives would be aligned to business strategy.  A 2016 study from the SHRM Foundation and The Economist Intelligence Unit foresees HR becoming the provider of human capital analytics to create competitive advantage.

But the shift to an enterprise-centric people analytics model will require a systematic multi-year multidisciplinary transformation program. Here are a few key priorities that will be critical to the success of any such large-scale transformation effort.

Create a strategic roadmap — Start with a strategic assessment of data-driven opportunities at the intersection of talent management and business outcomes that will help build an actionable business case. Determine the data requirements, the technology options and the operating model that will best serve your business case. Demonstrate a proof of concept.

Enlist leadership support — Integrated people analytics is a cross-functional program that has to be mentored from the top. Involvement of senior leadership will make it easier to build collaboration between multiple line-of-business stakeholders and ensure support from all other organization functions. Establish a clear leadership team to manage the different aspects of the program.

Build a balanced team — Integrated analytics is a multidisciplinary practice. The team has to have cross-organizational and cross-functional representation with data scientists and analysts, technologists, business unit and department heads, HR specialists etc.

Define a data strategy — Organizations require a data strategy that streamlines the integration of clean, reliable and accurate data from across HR, organizational and external sources.  Establish a data governance framework that not only defines data quality, integrity and security but also clearly assigns data ownership, responsibility and accountability.

These are just a few key elements that will determine the effectiveness of people analytics-driven transformation. At the same time, it is also important to consider some of the key challenges organizations will face as they embark on such an ambitious journey.

One of the biggest barriers to effective people analytics, according to a PwC survey, was "multiple unintegrated sources of data".  Most organizations will face significant integration challenges dealing with siloed and fragmented data sources across multiple internal departments and business units. This problem will only get compounded further as they look at leveraging the power of external sources, with a multiplicity of data types and formats, to enhance the value of analytics.

A manual approach to validating and entering all this data into backend business analytics systems will have an adverse impact on both the efficiency and the accuracy of the analytics process. But solutions like cloud based integration hubs can empower business users to set up sophisticated integration for data preparation, data onboarding etc. without the need for any code.

The other big challenge in the path to integrated people analytics is the rather severe and global shortage of analytics and data science talents. But the rise of AI and SaaS solutions with sophisticated automation and self-service capabilities is taking advanced analytics to regular business users. In fact, Gartner predicts that the self-service trend means that soon business users will be producing more analytics output than data scientists.

The last of the top three challenges in this area is something that we briefly mentioned earlier — the risk that organizations will instigate a backlash by going over-the-top with people data. There is already a lot of discussion about the implications of GDPR legislation on workforce data and analytics. One study found that 81 percent of people analytics projects were jeopardized by ethics and privacy concerns. So self-regulation may play a key role in how the industry moves forward.

There are a number of established links between a data-driven approach to HR and its impact on recruitment efficiency, attrition management, business productivity and financial performance. So, what started as a narrow HR function with BI tools has now expanded into an analytical practice with a broader systemic and strategic potential. In fact, some predict that people analytics may eventually become a decentralized function operating across several organizational departments, including HR. But building an integrated model of people analytics still presents some challenges that are as significant as the opportunities it represents. The good news is that a majority of organizations seem fully invested in making it work.