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DATA-DRIVEN TALENT STRATEGY:

BRIDGING THE CAPABILITY GAP IN PEOPLE ANALYTICS

The ability to analyse and interpret talent pool data has initiated a new era in workforce planning. Increasingly, businesses are embracing innovative approaches to human capital in recognition of the competitive impact that a focused data-driven talent strategy can have. Equipped with a seemingly limitless flow of candidate and employee information, as well as a wealth of potential new platforms, the HR function has become a strategic powerhouse in the race to compete. The market interest in people analytics, consequently, is at fever pitch.

According to a recent study carried out by the Harvard Business Review, nearly three quarters of CEOs profiled indicated that Human Capital was a major contributing factor to sustainable economic value, whilst over half cited data access and data-driven insights as being of equal importance.1 Interestingly however, investment in big data was lower for HR than any other department. It’s more than a little ironic, then, that out of the ‘Top 10 Challenges in Getting Business Value from Big Data’ listed in HBR’s report, understanding where to focus big data investments came out joint last – along with determining what to do with the insights created from data. The discrepancy between knowing the value of people analytics and successfully utilising the data reflects one of the key difficulties that have emerged in this rapidly evolving field.

Due to widespread talent scarcity and a resultantly candidate-driven market, the need to bridge the capability gap in people analytics is evident.

In 2015, Deloitte produced a comprehensive study of Global Human Capital Trends, which showed that although three quarters of companies surveyed viewed people analytics as important to success, only eight percent described their organisation as ‘strong’ in this area.2 In-between the reality and the ideal stands a labyrinth of interrelated issues: the suitability of data in performance metrics; problems integrating new analytics models into existing platforms; overloaded or clumsy infrastructure; international regulatory conflicts; the limits of purely quantitative data; hiring biases – to name only a few. In such an environment the obvious watchword is simplicity. And businesses’ desire for a simpler direction calls for a reassessment of some of the basic considerations that underpin people analytics.

“ The recruiting organisation that figures out how to extract the value of data will define the future of talent acquisition” BRENDAN BROWNE, SENIOR DIRECTOR OF GLOBAL TALENT ACQUISITION, LINKEDIN

DATA-DRIVEN TALENT STRATEGY: BRIDGING THE CAPABILITY GAP IN PEOPLE ANALYTICS


In the words of David Ogilvy, ‘where people aren’t having any fun, they seldom produce good work.’ Understanding how to improve employee engagement levels represents one of the most critical uses of a data-driven talent strategy.

Sites like LinkedIn, Glassdoor and Twitter have compounded the pulling power candidates have by creating a more authentic narrative of an organisation. In Edelman’s 2016 Trust Barometer on Employee Engagement, employees were the single most trusted source to communicate a business’ financial and operational performance, the treatment of employees and customers, and the business’ ability to handle crises. In the same survey, the highest rated criterion for building trust in a CEO was communication with employees.3 A recent study at Stanford University also demonstrated how 10.24 million employee emails sent at a US technology firm could accurately predict levels of both individual attainment and cultural adaptation.4 Facilitating the conversation between leadership and employees therefore represents a vital aspect of effective people analytics. These correspondences should be the driving force around which a company shapes its core vision and values. Any processes which expedite this kind of internal engagement – such as company social networks or feedback platforms – are likely to have a huge influence on productivity, retention, and effective decision-making. (Research from another Harvard study has confirmed a strong positive correlation between these kinds of evidence-based decisions and improved financial and operational performance.)5

Google, for example, take an expectedly unique approach to engagement by differentiating between employee development and personal wellbeing in performance reviews - the argument being that by keeping the two distinct, emphasis remains on the person behind the role, allowing a more open and productive environment to flourish.6 Similarly, in a comprehensive global generational study, PwC showed that one of the defining characteristics of millennials is the value they place on a cohesive and collaborative work environment. To quote PwC, ‘emotional connection drives retention’.7 The takeaway is this: where employees feel more comfortable sharing their ideas and opinions, the information gathered will likely be more actionable. And where the culture of a business encourages engagement, cycles of continuous feedback and improvement are likely to follow.

employee engagement


As it becomes increasingly more difficult to differentiate a role to attract the best candidates, qualitative insight becomes vital to structuring any employee value proposition. Effective engagement, therefore, should be in place from the very outset of an employee’s journey; a community begins at the welcome sign.

Traditionally, limits to hard data usage in hiring models centre around the fact that it’s easier to detect so-called ‘threshold skills’ (things like a college degree, IQ, specific certifications) than it is to determine ‘distinguishing skills’ (such as critical thinking, culture-fit, and creativity).9 And while it may seem clichéd to return to Google as the arbiter for big data usage, their success lies chiefly in the insights they draw out of their vast reservoirs of information. To get around the ‘threshold skill’ issue, for example, Google removed unfair biases in their hiring strategy by considering top-achievers from any lower-ranked college more favourably than average achievers from the very best schools – what Laslo Bock, Google’s Senior VP of People Operations, calls ‘casting a wider net’. The consensus from Google HQ? The former group usually outperform the latter. Another key lesson to derive from this is that the onus is on the company to try out new approaches when testing the validity of data-derived hypotheses. Internally, this could mean selecting a specific business unit to trial new systems and methodologies in. Externally, this might involve setting up a controlled experiment between different market subsectors (‘trystorming’, as it has been dubbed elsewhere).10 At any rate, however, having the right analytics talent behind these kinds of implementation strategies is crucial.


recruitment strategyCompanies that develop successful people analytics frameworks outperform their competitors in quality of hires, retention levels, leadership pipelines and several other key performance metrics.8 Engagement ultimately allows a business to go beyond basing talent strategies on quantitative information like salary or mobility figures alone.

DATA-DRIVEN TALENT STRATEGY: BRIDGING THE CAPABILITY GAP IN PEOPLE ANALYTICSDATA-DRIVEN TALENT STRATEGY: BRIDGING THE CAPABILITY GAP IN PEOPLE ANALYTICS


Despite cross-sector interest in the area, high levels of investment in people analytics is not always viable. But infrastructure doesn’t have to mean a large-scale Hadoop cluster and your own private wing of MIT.11

Education, for many HR leaders, can be the first point of call when it comes to using data effectively. A recent paper by the University of North Carolina business school asserted that a significant part of bridging the big data talent gap will come from HR professionals educating themselves and their leadership teams in the appropriate big data skill sets, including:

• Advanced analytics and predictive analysis

• Complex event processing

• Rule management

• Business Intelligence tools

• Data Integration.

UNC also stress the importance of a computer science background that can work in congruence with keen business knowledge.12 Once again, however, the keystone in the big data bridge is the analytics team’s ability to create meaningful insight from their data sets. As people analytics progressively becomes a board-level topic, educating internally brings the twofold benefit of disseminating knowledge and improving executive awareness of the value of data, thus prompting decision makers to confront the current capability gap. ‘Start with the tools you have’ – say Deloitte. Even basic pivot tables that are pertinent to the business’ wider aims can be of value to executive management. HR teams therefore have a responsibility to understand the interests of their leadership and ‘data-cater’ accordingly.

What’s more, a company’s people analytics capacity doesn’t necessarily stand or fall on the basis of hiring the top talent in the sector

(who are, naturally, in scarce supply – McKinsey puts the talent gap in deep analytics at up to 190,000 positions in the US alone).13 Whilst the value of having a solid contingent of analytics specialists is obvious, other avenues show potential. Alongside full-scale predictive solutions from the likes of Oracle, HP and IBM, so called ‘self-service’ analytics systems including Tableau and Alteryx have proliferated. Other websites such as Beapplied.com and Textio are also beginning to emerge, giving HR more control over the screening biases inherent to the recruiting process to ensure that the best talent is not unduly overlooked. Whilst such platforms might offer part of the solution, however, there is no real alternative to deep analytics talent – and companies who understand this will either hire aggressively from the relevant sectors (the ‘build’ approach) or contract in analytics talent where scalability is needed (the ‘buy’ approach).

Globally speaking, research undertaken at McKinsey has also found that despite the geographic discrepancy between North America and Europe’s data infrastructure versus that of developing economies, the latter are able to partake in the big data boom by outsourcing storage and analysis to centres located in already developed markets. In addition, the McKinsey report contends, organisations in developing countries may actually benefit by ‘leapfrogging’ to the most recent technologies and bypassing cumbersome legacy systems. Whilst a significant amount of work remains in making big data internationally transparent, there is certainly untapped potential for mutually beneficial engagement between developed and developing markets.

infrastructure


“ Best practice big data companies have built sufficient scale in a core group of deep analytical talent, upon which the rest of their organization can draw” MCKINSEY GLOBAL INSTITUTE

DATA-DRIVEN TALENT STRATEGY: BRIDGING THE CAPABILITY GAP IN PEOPLE ANALYTICSDATA-DRIVEN TALENT STRATEGY: BRIDGING THE CAPABILITY GAP IN PEOPLE ANALYTICS

Finally, analytics influencers have a shared consensus as to where the people analytics function must sit within the wider HR infrastructure. ‘If analytics is to become a core part of HR’s delivery’, says David Green (Global Director of People Analytics at IBM), ‘then it has to report into and be driven by the CHRO.’14 Mark Berry (CHRO at CGB Enterprises) claims that by ceding analytics to a ‘less analytically inclined’ leader, companies risk mismanaging a function that depends on ‘a specialised set of knowledge, skills, and abilities’ – which is only likely to further damage recruitment efforts for any analytics function in the future.15 Traditionally, HR teams have not been aggressive enough in demanding high-quality visualisation, instead settling for simply mirroring business operations in their use of data. A good CHRO will be clued up to the value of analytics and will know what they want from the available data, which can only strengthen the case of the function in front of executive management. Ultimately, without proven capability from HR, the budget for analytics is unlikely to increase.

Understanding the value of people analytics is essential to retaining a competitive edge in a marketplace that will only become more data-oriented. Where companies are serious about getting the best people – and getting the best out of their people – a data-driven talent strategy must firmly underline the road ahead. Part of the challenge at present is convincing decision-makers to take the next step. But the capability gap in big data is not so much a leap of faith as it is a vital crossing point into the future. In congruence with developing core analytics talent and mobilising data to improve employee value propositions, a well aligned HR analytics function may be the first step to convincing C-suite to consolidate their efforts towards further investment in this field. And if the data is anything to go by, those who do so will gain a considerable advantage.

From pipelining the best talent to helping to re-elect a president, data is approaching the era where its limits are defined only by the imaginations of those handling it.16


conclusion


1 “The Big Data Opportunity for HR and Finance.” The Harvard Business Review / Workday. Web. pp 1-16. 10 May 2016. <https://hbr.org/resources/pdfs/comm/workday/workday_report_oct.pdf>.

2 “Global Human Capital Trends 2015: Leading in The New World of Work.” Deloitte UP. Web. pp 1-112. 10 May 2016. <http://d2mtr37y39tpbu.cloudfront.net/wp-content/uploads/2015/08/DUP_GlobalHumanCapitalTrends2015.pdf>.

3 “2016 Edelman Trust Barometer - Employee Engagement.” 2016 Edelman Trust Barometer - Employee Engagement. Edelman Insights / LinkedIn, 4 Apr. 2016. Web. 10 May 2016. <http://www.slideshare.net/EdelmanInsights/2016-edelman-trust-barometer-employee-engagement>.

4 Goldberg, Amir, Sameer B. Srivastava, V. Govind Manian, and Christopher Potts. “Enculturation Trajectories and Individual Attainment: An Interactional Language Use Model of Cultural Dynamics in Organizations.” SSRN Electronic Journal. Stanford / Berkeley. Web. pp 1-33. <http://faculty.haas.berkeley.edu/srivastava/papers/Enculturation%20Trajectories.pdf>.

5 McAfee, Andrew, and Erik Brynjolfsson. “Big Data: The Management Revolution.” (2012): 1-10. www.rosebt.com. Harvard Business Review. Web. 10 May 2016. <http://www.rosebt.com/uploads/8/1/8/1/8181762/big_data_the_management_revolution.pdf>.

6 “Open Sourcing Google’s HR Secrets - Knowledge@Wharton.” Wharton, 26 Feb. 2016. Web. 10 May 2016. <http://knowledge.wharton.upenn.edu/article/open-sourcing-googles-hr-secrets/>.

7 “PwC’s NextGen: A Global Generational Study” PwC / USC / London Business School. Web. pp 1-16 10 May 2016. <http://www.pwc.com/gx/en/hr-management-services/pdf/pwc-nextgen-study-2013.pdf>.

8 Kaushik, Niraj. “12 Steps to Building an Effective Talent Pipeline.” Oracle Blogs. 13 Aug. 2014. Web. 10 May 2016. <https://blogs.oracle.com/oraclehcm/12-steps-to-building-an-effective-talent-pipeline>.

9 Goleman, Daniel. “What People Analytics Can’t Capture.” Harvard Business Review. 07 July 2015. Web. 10 May 2016. <https://hbr.org/2015/07/what-people-analytics-cant-capture>.

10 “What Is Trystorming?” Lean Manufacturing and Six Sigma Definitions. Web. 10 May 2016. <http://leansixsigmadefinition.com/glossary/trystorming/>.

11 McAfee, Andrew, and Erik Brynjolfsson. “Big Data: The Management Revolution.” (2012): 1-10. www.rosebt.com. Harvard Business Review. Web. 10 May 2016. <http://www.rosebt.com/uploads/8/1/8/1/8181762/big_data_the_management_revolution.pdf>.

12 Ahalt, Stan, and Kip Kelly. “The Big Data Talent Gap.” (2013): 1-15. UNC Kenan-Flagler. Web. 10 May 2016. <https://kenan-flagler.unc.edu/~/media/Files/documents/executive-development/execdev-big-data-talent-gap.pdf>.

13 “Big Data: The Next Frontier for Innovation, Competition, and Productivity.” McKinsey Global Institute, June 2011. Web. pp 1-156. 10 May 2016. <http://www.mckinsey.com/business-functions/business-technology/our-insights/big-data-the-next-frontier-for-innovation>.

14 Green, David. “Key Takeaways from People Analytics 2016.” LinkedIn Pulse, 3 May 2016. Web. 10 May 2016. <https://www.linkedin.com/pulse/key-takeaways-from-people-analytics-2016-david-green?trk=mp-reader-card>.

15 Berry, Mark. “Seven Deadly Sins To Avoid with HR Analytics Initiatives.” LinkedIn Pulse, 5 Sept. 2015. Web. 10 May 2016. <https://www.linkedin.com/pulse/seven-deadly-sins-avoid-hr-analytics-initiatives-mark-berry?trk=prof-post>.

16 “HP Vertica Analytics Platform Overview.” HP, 2014. Web. pp 1-2. 10 May 2016. <http://www8.hp.com/h20195/V2/GetPDF.aspx/4AA5-8917ENW.pdf>.

references

Bersin, Josh. “The Geeks Arrive In HR: People Analytics Is Here.” Forbes, 1 Feb. 2015. Web. 10 May 2016. <http://www.forbes.com/sites/joshbersin/2015/02/01/geeks-arrive-in-hr-people-analytics-is-here/#181ce5847db3>.

Green, David. “The Oscars for People Analytics.” LinkedIn Pulse, 11 April 2016. Web. 10 May 2016. <https://www.linkedin.com/pulse/oscars-people-analytics-david-green?trk=prof-post>.

Jobvite Recruiter Nation Survey 2015. Jobvite. Web. 10 May 2016. <http://www.jobvite.com/wp-content/uploads/2015/09/jobvite_recruiter_nation_2015.pdf>.

Schwab, Klaus. “The Fourth Industrial Revolution: What It Means, How to Respond.” World Economic Forum, 14 Jan. 2016. Web. 10 May 2016. <https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/>.

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