Fractal Analytics Blog

Navigating the Frontiers of Human Capital Management

Navigating the Frontiers of Human Capital Management

By Manik Pasricha
April 26, 2017

Human capital management (HCM) is currently valued at $13bn+ in United States and is projected to grow to $20bn by 2021. This sector is moderately consolidated with top 10 players accounting for 50%+ revenue. Major players in this sector include: SAP, ADP, Oracle, Workday, Paychex, Insperity, TriNet, Kronos, IBM, and Ultimate Software. Organizations, with strength as small as handful of employees to as large as hundreds of thousands of employees, engage with HCM companies on ongoing basis. Such engagements include employee solutions ranging from selective offerings such as payroll, benefits and performance management, 401k, tax, and compliance to comprehensive outsourcing through co-employment.

Traditionally, growth in this sector has come from operationalizing employment processes and mitigating compliance costs, with technology acting as an instrument of convenience. During this time spanned over decades, most HCM businesses have witnessed fairly unrestricted surge driven largely by deep-rooted client relationships and proactive assessment of legislative actions. Consequentially, the size and sustainability of the HCM business became dependent on its representatives’ ability to negotiate pricing and remain a valued knowledge partner for customers.

However, following the pattern from many other sectors in the past decade, the HCM sector has been (and will continue to be) disrupted by emerging technology entrants with cloud-powered, analytics-centered offerings. For relationship-driven HCM behemoths seeking growth, this presents an opportunity to target new frontiers of Human Capital Management with a nuanced technology-driven approach. From using technology as a CRM tool, these companies need to tap into the full potential of analytics, digital, and cloud to continue to lead in the emerging landscape. This indicates a major re-orientation of HCM worldview and execution may well decide the winner and losers for this sector. Such re-orientation will need strategic investments in innovation, technology leadership, and infrastructure. Beyond the Chief Technology officer, C-suite leadership would need the expertise of data-informed decision enablers. These experts would need to be provided a mandate for long-term change, space to fail & grow, and P&L ownership. Beyond organic growth, HCM companies would also need to look for opportunities to acquire smaller players with niche offerings and intellectual capital.

Unfortunately in many cases, a large part of the current implementation is guided by the immediate chatter around an emerging technology wave, be it Artificial Intelligence (AI) today or mobile-apps from a few years ago. While it’s advisable to remain in-sync with the emerging paradigms, it’s also important to account for the investment needed for the desired maturity. For instance, the stakeholders wanting to install GPU clusters to catch the deep-learning wave should first reflect and articulate the intended outcomes. For some companies, higher-order computing power stands in stark contrast with the existing database paucity. Add to that, lack of the data quality stewardship and limited collaboration from business pushes the analytics team of such companies to remain a marginal force. This situation is a self-reinforcing deadlock – the analytics team not being able to deliver its intended impact because of limited enterprise assistance; and hence, business leadership not leveraging analytics with complete confidence.

We recommend a 4-pronged approach to escape such deadlocks, towards unlocking the promised long-term analytics value:

1. Building an integrated data warehouse

Loss of consistency and reproducibility as a result of sporadic information management can’t be overstated. Silo-ed information not only erodes the trust needed to collaborate but also creates friction to build upon prior knowledge. An integrated approach to data warehouse development is, thus, indispensable for HCM companies to develop real-time, 360-degree view of customers and their employees. This information can be enriched with external data sources – both free and paid sources which, when blended with the internal information, can uncover useful patterns.

2. Incremental visual storytelling and predictive solutions:

Empowered by an integrated repository, HCM companies can leverage advanced analytics to navigate and predict previously undiscovered aspects of customer engagement, marketing effectiveness, and sales capabilities. For one HCM company, we discovered that there is no statistical difference between the HCM’s communication (unstructured data) with an active vs disengaged customer. In such a situation, even proactive communication without any tangible deliverable (such as a change in engagement manager or even minor discounts) is unlikely to upturn the customer’s decision to churn. Such solutions have helped analytics leaders turn-around a deadlock trap into a business sponsor-driven green-field.

3. Scaling up horizontally & vertically

Analytics teams can create appreciable impact by implementing solutions for one part of the business. However, given the wide diversity of HCM customers, there’re opportunities to drive significant P&L impact by scaling up horizontally (cross-pollinating across business units) & vertically (using operational excellence to assess strategic decisions). For instance, most HCM companies have portfolio of tens of products with thousands of features across different market segments. Often, features overlap or one product has expired in one segment, only to be re-launched for another segment with a different name. Building product recommendation engine can considerably reduce this internal complexity while providing insights for targeted launch of new solutions. In addition, Fractal has provided scaling guidance to HCM analytics teams to explore the revenue potential of B2C solutions, beyond the traditional B2B and B2B2C offerings.

4. Tapping into the potential of AI

Wide-ranging, demonstrable P&L impact will give analytics teams the deserving liberty and exposure to move up the analytics maturity curve. Analytics leaders can then invest into the talent and technology to leapfrog into AI development. Three specific areas which are expected to gain from AI advancements in HCM are recruiting, training services and customer assistance through chatbots. A great AI application in recruitment would be to assess the candidacy of prospects who weren’t hired previously and kept ‘on file’ while they continue to demonstrate employment interest and career progression. AI solutions can deliver personalized training modules to chart the best way for employees to train & contribute. Similarly, customer and their employee assistance AI-powered chatbots can provide round-the-clock expertise. A word of caution: One view is that somehow such AI solutions would be able to circumvent human bias. Thus, an AI-powered recruitment would overcome recruiter’s inherent bias to enhance workforce diversity. Such expectations are unlikely to be fulfilled for no algorithm can completely eliminate bias. Algorithmic bias have had large devastating effects, not very different from human bias, in sectors ranging from stock-markets to weather prediction.

The devil of this 4-pronged analytics transformation approach lies in the implementation details. For the AI success projected 12-24 months in future, the analytics leadership and institutional mechanisms need the management mandate today. With the right mix of articulated roadmap and enterprise collaboration, HCM analytics will go beyond chasing the next wave to proactively defining it.


About the author:

Manik Pasricha has 5 years cross-industry/function global experience in helping key stakeholders make data-informed choices. He graduated from Indian Institute of Technology Delhi in 2012 with a degree in Civil Engineering and minor area specialization in Business Management.

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