People Analytics - University of Sydney

Using Data and Algorithms to shape the Employee Experience

PeopleAnalytics

2 People Analytics – Using Data and Algorithms to shape Employee Experience

This is a publication of the Australian Digital Transformation Lab, a joint venture between The University of Sydney Business School and Capgemini Australia. The information contained in this document is proprietary. All rights reserved. © 2019.

AuthorsProfessor Uri Gal,Professor Kai Riemer,Christopher Harth-Kitzerow(The University of Sydney Business School)

Catherine Aboud,Simone Briggs(Capgemini Australia)

ContactsProfessor Uri [email protected]

Professor Kai [email protected]

Further InformationDownload study:

http://hdl.handle.net/2123/20082

Table of Contents

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Key Findings

The Promises of People Analytics

Case study (1) Start slow, learn to build capability, then integrate

Case study (3) Enterprise Systems foundations

Executive Summary

People Analytics Market

Case study (2) Quick wins through digital employee engagement analytics

Challenges of People Analytics

What is PA?

Five PA software archetypes

Wearables and biometric technology

How to kick-off a PA project?

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Key Findings

1. People Analytics is achievable by better access to data on employee activity (big data), and advances in algorithms (data science).

2. People Analytics promises to create an objective view of the talent function to improve the entire talent life-cycle.

3. People Analytics is not an end in itself. It is a means to improving employee experience and talent management.

4. The market for People Analytics software ranges from all-in-one solution with HR system to innovative cloud-based apps that focus on a particular talent issue.

5. The biggest challenge to People Analytics implementation are lack of data quality and integrated systems infrastructures.

6. Wearable devices promise a 360-degree view on employee activity, but raise serious concerns regarding employee surveillance.


7. Promises of People Analytics are often over-stated. Data and algorithms are neither objective nor unbiased, as they are created by humans.

8. Predictive People Analytics is always based on past data and thus cannot cope well with future changes.

9. Employee Experience should always drive the implementation of People Analytics solutions.

10. People Analytics is here to stay. Organisations need to engage now, and understand its promises, limitations, state-of-the-art, and implementation challenges.

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PromisesProponents of People Analytics point to three main promises:

1. PA rationalises decision-making by substituting managers’ subjective, intuitive judgements with objective, ‘hard’, and unbiased data. Immune to emotional influences, algorithmic decisions are said to overcome favouritism and costly mistakes.

2. PA provides a comprehensive 360-degree view of the people function by collating data from diverse sources on employee behaviour - including sensors, messaging data and activity traces collected from various enterprise systems.

3. PA allows predicting the future with a view to detect issues in talent management early, to improve training regimes, performance incentives or retention rates.

Market Study of PA Software Solutions

We undertook a comprehensive market overview of PA software solutions. We found that most PA systems originate from established HR and talent management solutions, while a number of interesting and innovative new players are solving particular pertinent issues in a focused way. Our market analysis classified systems according to detailed criteria derived from the talent management wheel. We identified five main archetypes of PA systems:

1. Talent Management Solutions are comprehensive solutions with broad talent information capabilities.

2. Performance Analytics Solutions are allrounders with talent performance prediction and deployment capabilities.

3. Talent Deployment Solutions are specialist tools with a focus on talent deployment and resource performance prediction.

4. Talent Lifecycle Analytics Solutions are the People Analytics heavy weights with broad predictive capabilities.

People Analytics (PA) is the name for a new approach to talent management that has the potential to re-shape the employee experience. Making use of new computational techniques to leverage large amounts of digital data about employee behaviour, this approach promises to introduce evidence-based management to the talent function. Main drivers of the increase in the update of PA include advances in data collection and analytics (big data), as well as new approaches to algorithmic management based on machine learning techniques (AI).

Executive Summary

5. Specialist Talent Solutions are tools that experiment and pioneer new ways of doing people analytics.

Case StudiesWe analysed three separate Capgemini client case studies for this report. These cases provide insights into various aspects and challenges of PA implementation:

1. Building PA Capability: In the case of a large Australian utility we uncovered the benefits of starting slow to build PA capability through learning, and the necessity to integrate backend systems for access to employee data in the right quality.

2. Digital Employee Engagement: A multinational professional services firm scored a quick win through building a digital employee engagement analytics capability, which allows to survey employees for feedback and to integrate this data with other data collected via passive listening channels (e.g. social media).

3. Enterprise Systems Foundation: The case of a large beverage bottling company showed the importance of building a good enterprise systems foundation to enable systematic people analytics, integrating various talent management functions.

ChallengesWhile we demonstrate many benefits of People Analytics, it is important to highlight some of the challenges of PA, and reasons why some of the promises made by vendors and proponents of PA need to be taken with a grain of salt. Here are the most important ones:

1. Algorithms rely on subjective human input: The idea that PA and its data analytics and algorithmic capability will lead to unbiased and objective decisions is a fallacy, because both data and algorithms are always the result of human activity and design. A lot rides on who creates and how those algorithms are programmed.

2. Data can never fully or accurately reflect human behaviour: Any data set is always only partial and


selective and can never provide a comprehensive overview. A partial selection of performance data will shape worker behaviour downstream, running the risk that employees ‘work to the measure’ at the expense of activities that are not measured yet are still important.

3. Predicting the future is highly problematic: Any algorithmic predictions through techniques such as machine learning always rely on existing data, reflecting the past. Such predictions only work reliably as long as the future is a direct extension of the past. This means that such algorithms are quite susceptible to change.

ConclusionWhile there are a myriad of promises associated with PA, there are equally risks and critical voices who claim that

a narrow focus on data and those aspects of talent and work that are measurable come at the expense of a more holistic understanding of work performance and talent qualities. It is Employee Experience (EX) that needs to take centre stage, as PA is never an end in itself, but a means to achieving more effective talent management with a view to improve employee experience, satisfaction, productivity and retention.

In the following we will discuss, promises, software solutions, wearable devices, and a number of client case studies to provide a comprehensive overview of this emerging topic that promises to re-shape we do talent development, human resource management, and above all re-think the employee experience using data as evidence.

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What is

PA? 8 People Analytics – Using Data and Algorithms to shape Employee Experience

People Analytics – an algorithmic and data-based approach to talent managementOver the last decade, a growing number of businesses have started using People Analytics (PA) solutions to manage their workforce and make strategic business decisions. PA includes computational techniques that leverage large amounts of digital data that are collected from multiple business sources and reflect different areas of employee behaviour.

Data can include employee performance reports, peer evaluations, levels of social engagement, online behaviour, and daily activities collected through smart tags, fitness trackers, or cameras. By analysing these data for patterns, PA applications present managers with data-rich and nuanced views of organisational resources, processes, and workers’ performance. Many PA applications include algorithmic mechanisms that use digital data as their input and analyse them to provide managers with recommended courses of action within defined problem areas.

Algorithmic talent managementProponents of PA claim that managers, by relying on data and algorithmic processing rather than intuition or gut feeling, can remove bias and subjectivity from their decision-making process and increase the speed and quality of their decisions. This is because PA allow managers to make data-based decisions instead of relying on traditional methods that are based on personal relationships, subjective experience, inherent biases, and an implicit tendency for risk avoidance. Therefore, it is argued that PA

can help managers run organisations more rationally, fairly, and effectively.

For example, many companies use PA to manage their hiring process. With rising volume of applications, HR departments are finding it increasingly difficult to give each application sufficient attention to avoid false negatives and false positives in the hiring process. PA algorithms can swiftly and methodically review thousands of applications to identify those applicants that possess the desired skills and traits for the position, thereby increasing the likelihood of finding the ideal candidate while saving time and money.

Due to these benefits, PA systems have been employed across a range of business domains and sectors, from healthcare, to education, to financial services, to retail. Indeed, the PA global market have experienced significant growth over the last few years and is predicted to reach 2.45M USD by 20262 . A number of vendors in the talent and people management space are now offering solutions with PA functionality.

Data science as a driver of People AnalyticsTo understand the emerging popularity of PA we need to examine it in the context of data science, which refers to the academic and professional field tasked with developing and applying principles and methods for analysing large datasets and translating them into actionable insights to inform decision-making.

In business organisations, data science is considered the cornerstone of a new wave of quantitative management.

2 https://globenewswire.com/news-

release/2018/08/10/1550382/0/en/

Workforce-Analytics-Market-to-Reach-US-2-453-9-Mn-by-2026-

Transparency-Market-Research.html

Proponents of PA claim that managers, by relying on data and algorithmic processing rather than intuition or

gut feeling, can remove bias and subjectivity from their decision-making

process and increase the speed and quality of their decisions.

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Key enablers of People Analytics: Availability of data and Advances in algorithms

Key enablers: Use of data Algorithmic management

Technological Advances

Easy access to data:n Increasingly cheap to collect, store,

analyse, and apply datan Access to vast amount of data sources

(data-brokers, social media, etc.)

Rise of consumer-based algorithms:Advances in machine-learning, neural networks and deep learning mathematics

Commercial Changes Affordable cloud applications drive data collection:n Specialised, lean resources,

scalable solutions

Growing use of algorithms:Algorithms are now applied across multiple areas, beyond obscure computational domains

Managerial Beliefs “Human decision-making cannot be trusted”n Humans are

inherently biasedn Prone to making errorsn Data and evidence-based

management as the solution

“Algorithms and the data they are based on are a reliable source of information”n Algorithms don’t make mistakesn They have no alliancesn They don’t get tired

It has been heralded by both practitioners and academics as a technological advancement that will render the management of organisations more rational and radically improve firm performance.

Thanks to data science, it is argued that managers can engaged in ‘evidence-based management’ by measuring, and therefore knowing and managing, their businesses more effectively. Access to large organisational datasets (big data), and advanced analytical techniques (algorithms) to extract knowledge from data, is seen as a prerequisite.

Under this paradigm, managerial competence is then a function of the size and quality of the available data, of managers’ ability to find meaningful insights in the data, and of their capacity to translate these insights into effective action. PA is a logical extension of these ideas. It reflects a data-driven approach to managing people at work. Focusing on important trends while ignoring

invalid or irrelevant data can increase the quality of managerial decisions and consequently lead to improved workforce performance along various dimensions such as engagement, job satisfaction, and productivity.

Overview of the reportWhile there are a myriad of promises associated with PA, there are equally risks and critical voice who claim that a narrow focus on data and those aspects of talent and work that are measurable come at the expense of a more holistic understanding of work performance and talent qualities.

In the following we will discuss, promises, software solutions, wearable devices, and a number of client case studies to provide a comprehensive overview of this emerging topic that promises to re-shape we do talent development, human resource management, and above all re-think the employee experience using data as evidence.


The Promises of People AnalyticsDiscussion of PA in professional publications and the popular press have been mostly positive. There seems to be a consensus about the benefits that PA can bring to organisations, managers, and employees . These benefits are varied and wide-ranging; from enhancing individual employees’ job satisfaction, to delivering operational efficiencies, to increasing organisational profitability.

Overall, these benefits derive from three distinct and tangible outcomes that PA is believed to deliver:

1. “PA rationalises decision-making” – One of the basic premises behind PA is that human decision-making is inherently subjective, biased, and therefore flawed. Managers are said to place an unwarranted emphasis on their intuition, untested assumptions, and gut feeling when they make decisions. Additionally, managers’ political associations and inter-personal relationships may cause them to make biased, and thus suboptimal decisions. Therefore, when decisions are made by algorithmic PA, it is believed that many of these issues can be resolved. The reason is that algorithms are bearers of encoded logic; they are immune to emotional influences and can rise above political intrigue to analyse data about employees’ performance in an unbiased fashion. PA algorithms never get fatigued or distracted, which makes them more consistent and efficient and they are not swayed by subjective opinion. Therefore, by using PA algorithms managers will be able to replicate the same structured approach to sort and classify data and optimise decision-making.

3 E.g.: https://www.bersin.com/deloittes-bersin-finds-effective-use-people-analytics-strongly-related-improved-talent-business-outcomes/;

https://www.entrepreneur.com/article/289042; https://www.hrzone.com/perform/business/this-is-the-year-of-people-analytics-again

2. “PA allows managers to gain a comprehensive and objective view of their organisation” – PA works by collecting digital data that captures all facets of an organisation’s activity - from employees’ performance, to their social behaviour and motivations, to organisational events, processes and resources. It stores the data in organisational databases and presents it in easily consumable executive dashboards. By accessing these dashboards, managers can then discover objective insights about organisational activities and their workers.

3. “PA allows managers to predict the future” With machine-learning algorithms embedded in PA, managers gain insights on the future. Algorithm-backed PA applications can be trained to identify subtle patterns and regularities in large datasets and, based on those, develop models that predict future trends. During the training process, different types of algorithms (e.g., association, clustering, and decision-tree algorithms as well as deep-learning) are applied to existing datasets to determine trends in the data and extrapolate rules that can be applied to unknown data. For example, managers can use predictive algorithms to identify employees who are likely to quit. This can be done by analysing data about employees who had quit in the past to find behavioural patterns or personality traits that they have in common, and checking current employees who exhibit the same patterns and traits. Another area is predicting the performance of new hires based on past data.

PA promises to remove the weaknesses of human decision-making from the equation.”

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Wearables and biometric technology12 People Analytics – Using Data and Algorithms to shape Employee Experience

Wearable and Biometric technology: emerging frontiers of data-collectionIn order to maximize business benefits, People Analytics require large swaths of digital data that capture all aspects of an organisation’s activities and employee behaviours. Traditionally, organisations collect data through operational and financial reports, performance evaluation meetings, employee feedback surveys, etc.

In recent years, organisations started deploying wearable and biometrics technologies to collect more comprehensive and granular data about their workers. Such devices range from conventional data capture via swipe cards used to access facilities, to GPS tracking devices, smart badges, fitness tracker and more outlandish sounding solutions, such as employee microchipping, whereby a chip is inserted under the skin in one’s arm.

Smart badgesSome businesses require their employees to wear “smart badges” that track their physical location within the office, and record their conversations and tone of voice (Lang 2017). When crossed-referenced against other business metrics, this information can be used to infer how productively different people work together, how their emotional states vary during the day, and how distracted they are when they engage in different work activities.

Fitness trackersOther organisations provide their workers with fitness tracking devices and encourage them to use them regularly (Cherry 2017). These devices can be used to monitor a range of health indicators such as heart rate, fatigue levels, and sleep patterns. While these data can be used to improve workers’ health, they also offer an expanded and detailed view of workers’ behaviour and physical well-being.

Chip implantsEven more penetrating is the use of electronic chips implanted under employees’ skin. Some companies have started using embedded RFID chips to allow their workers carry out everyday activities more easily, such as printing, logging into computers, or buying food (Kennedy 2017). Despite these potential benefits, these chips can be also used to monitor employees’ whereabouts at all times, both in and outside of the office, which leaves open many as yet unanswered ethical, moral and legal questions.

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The Marketfor PeopleAnalyticsSoftware


People Analytics Market

1. Areas of People Analytics functionalityIn order to evaluate software solutions for their scope of PA functionality we selected a well-known framework that provides a comprehensive outline of talent management activities and processes – the Talent Management Wheel.

The Talent Management Wheel (TMW) shown in figure 1 was adapted from Stahl et al. (2012) who state that the aim of talent management is to “recruit and select talented people, develop them, manage their performance, reward them and try to retain the strongest performers” (Stahl et al., 2012). Accordingly, the model captures these activities in dedicated segments that form the outer ring of the wheel. We have added to the model two additional, central segments with because we use model in the context of people analytics.

Recruitment and Selection: The practices concerned with the attraction of external talent to the organisation. This comprises the processes of position formulation, job advertising, application screening, talent screening, interviewing, contracting and onboarding. Recruitment is informed by higher-level workforce-planning process.

Development and training: Typical human resource activities concerned with both broad skills development, as well as more narrowly training for concrete job requirements as well as for risk & compliance. As such, it also concerns career planning, goal setting, and deriving

individualised professional development plans, as well as the development of leadership talent.

Performance management: The continuous process of negotiating, formulating, communicating, supporting, and controlling of performance goals at the individual, group or divisional level, always in alignment with the strategic goals of the organisation.

Talent retention: Activities that aim to keep valuable employees in the organisation. Talent acquisition is expensive – talent retention is thus an important activity to protect the investments made in talent during acquisition and development. The most important part of retention is the proactive identification of employees at risk of defection. Yet, retention must also be part of a broader initiative to create a ‘retention culture’ – which ties in with professional development, career planning and rewards.

Compensation and rewards: Compensation-related activities aim to develop merit-based, competitive remuneration schemes that allow to both attract and retain high-quality talent to the organisation. Rewards are used to provide recognition for individual or collective achievements above and beyond agreed-upon remuneration attached to a particular role or position. This can include reputation-based recognition (such as through certificates or awards), or monetary rewards such as bonuses.

The purpose of this research was to gain a structured overview of the market for People Analytics software solutions. Such a study provides further insight into the diversity and scope of PA as a phenomenon. We note that such software solutions can either be new, dedicated PA solutions, or existing human resource and talent management solutions which progressively implement PA functionality.

The market research was carried out in an agile and iterative way, alternating between the following three stages: 1) Identification of software solutions in the marketplace, 2) Evaluation of software solutions, 3) Clustering of solutions into meaningful groups.

Criteria for evaluationAs a prerequisite we had to derive a criteria catalogue by which to evaluate such software solutions. Our criteria cover both the breadth and depth of the PA phenomenon:

1. On the one hand we evaluated software solutions for their scope of PA functionality: What are the areas of HR and talent management in which a particular product delivers PA functionality?

2. On the other hand, we evaluated for depth of implementation: How sophisticated is the PA implementation?

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Talent review: Processes concerned with talent identification, profiling and mapping, used to take stock of the current workforce and plan for the workforce of the future. Talent review thus feeds into both recruitment and development. It comprises the identification of key individuals in the organisation, such as those of high value that would be hard to replace or those that are future ‘leadership material’.

Resource management: Concerned with talent deployment and talent controlling in a broad sense, at the process, product or project level. Comprises the analysis of the talent pool for selection and targeted deployment, as well as controlling practices such as the design and use of time sheets and other employee utilisation data. Feeds into performance management.

Company insights: This area links talent management to the overall company goals and metrics. It is concerned with showing how talent activities contribute to broader organisational KPIs, and to translate those KPIs into talent activities.

2. Sophistication of people analytics functionalityFor each area of functionality, as identified by the talent management wheel, we further ascertained the extent to which a feature implements different levels of data analytics:

1. Descriptive analytics uses data to paint a picture of the current state of affairs and is thus always backward looking.

2. Predictive analysis is forward looking and aims to use past data to extrapolate trends and certain behavioural patterns.

3. Prescriptive analysis is forward looking and aims to provide actionable advice in order to achieve a certain outcome.

We note that these levels of analytics depend on each other. In order to deliver predictive or prescriptive data analytics, descriptive analytics will already have to be implemented.

Evaluation and classificationThe identification and evaluation of People Analytics software solutions relied upon web-based research, and self-reported data via the websites, brochures, videos, and demo installations provided by the vendors. We do not make any judgements of the quality of a certain implementation of a functionality. We merely note when a software solution provides functionality in an area of the talent management wheel, as well as the extent to which it implements different levels of analytics. All evaluations were recorded in a table which was used for clustering. The following table presents the outcome of this analysis, ordered by the five clusters we identified.

Recruitment & Selection

Developm

ent

&Trainin g

Perf

orm

ance

Man

agem

ent

Retention

Compensation

&Rew

ards

Tale

ntRe

view

CompanyInsights

ResourceManagement

Figure 1: Talent Management Wheel (adapted from Stahl et al., 2012)


*the number in the table represents the count of vendors who satisfy the criteria to what extent

Retention Performance Management

Development & Training

Recruitment & Selection

Talent Review

Compensation & rewards

Insights on Company Performance

Resource Management

Talent Management Solution Sample Size (8)

2 8 4 7 5 1 8

Performance Analytics Solution Sample size (3)

2 3 2 3 3 3 2 1

Talent Deployment Sample size (4)

1 1 1 1 1 1 4

Talent Lifecycle Sample size (7)

1 5 4 2 1 3 1 2 1 5 1 5 2 6 2 1

Specialist solutions Sample size (7)

4 1 3 2 1

Talent Management Solution

Performance Analytics Solutio

Talent Deployment

Talent Lifecycle

Specialist Solutions

SAP Success factors Kronos HCM Epicor Human

Capital Management Netsuite HCM

Sutepeople Solarforce hcm ADP Workflow Now Peakon Zeroedin

Financial Human Capital Management

Employee Connect Oracle Human capital

Management Cloud

Qubix People Management Cloud

Small-improvements

Tenrox Replicon

SAP HCM PeopleStreme Cornerstone Talent

Management Software IBM HR analytics (/

Watson talent Insight Workday Human Capital

Management Sumtotal talent Capgemini People and

HR Analytics - HRSMART

Cultureamp Wooboard Microsoft

(Volometrix) Batterworks EFS SAS

performance Management

Quantium

Colour Key:

Provides descriptive analytics

Provides descriptive and predictive analytics

Provides descriptive and prescriptive analytics

Provides all levels of data analytics

People Analytics – Heat map and Classification Table

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Five PA software archetypes18 People Analytics – Using Data and Algorithms to shape Employee Experience

Archetype 1:

Talent Management Solutions “Comprehensive solutions with broad talent information capabilities”

Talent Management Solutions (TMS) provide basic functionalities across most sections of the Talent Management Wheel. Those functionalities are limited however to mostly descriptive analysis of employee data, with no predictive or prescriptive features. TMS represent

the bread and butter of HRIS and are more conservative in nature, with a strong focus on data sorting, aggregation and presentation. TMS generally do not come with resource management or deployment features.

Solaforce HCM

Finnish company Solaforce Oy provides a lean and cloud-based solution named Solaforce Human Capital Management. Targeted at companies of all sizes, Solaforce HCM offers descriptive analysis across different areas of a company on a subscription basis. With the promise of an intuitive user interface it aims to support the organisation with planning, organization, talent and compensation management. The tool provides comprehensive descriptive analysis for most areas of the Talent Management wheel and is thus a typical Talent Information Solution.

Performance ManagementAllows users to save personal goals and development targets and review their progress.

Talent Review Review reports on employee performance to identify talent in the organisation.

Recruitment and Selection

By listing skills and know-how of employees and combining it with company goals, the tool helps identifying missing skills in the company to support hiring decisions. It also promises to make the recruiting process easier by integrating multiple channels like social networks.

Compensation and RewardsManages salaries, incentives and other rewards and remuneration information. Employees can track personal payment and rewards history.

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Archetype 2:

Performance Analytics Solutions “Allrounders with talent performance prediction and deployment capabilities”

Similar at first glance to Talent Management Solutions, given their breadth of features across most sections of the Talent Management Wheel, Performance Analytics Solutions (PAS) have a strong focus on analytics for employee performance management. While offering

descriptive analytics in most feature areas, PAS provide predictive analytics for talent performance, as well as some resource management capabilities, linking talent performance to deployment.

descriptive analytics in most feature areas, PAS provide predictive analytics for talent performance, as well as some resource management capabilities, linking talent performance to deployment.

Financial Force Human Capital Management Cloud

Founded in 2009 in San Mateo, California, and originally built on Salesforce.com, FinancialForce started out as a cloud-based Enterprise Accounting solution. In 2013 the company acquired Vana Workforce, based in Burlington, Ontario, and subsequently launched its Human Capital Management cloud-based solution.

The FinancialForce Human Capital Management Cloud provides predictive analysis for Performance Management on top of descriptive analysis for all other areas of the talent management wheel. In addition, it provides resourcing information, tracking items such as time and money invested by different projects and departments in relation to their goals, with the promise to more accurately evaluate talent project performance.

Retention Provides analytics on leave and retirement of previous employees to improve retention

Performance ManagementAllows employees to record time against work, projects, locations, initiatives and predicts employee performance.

Development and TrainingProvides immediate access to all of the documentation, training and contacts, review requisitions.

Talent Review Review reports on employee performance to identify talent in the organisation.

Recruitment and SelectionHR professionals and managers can review and post requisitions, manage candidates, and provide feedback.

Talent Review Provides reports and analytics on who are leading employees in a company.

Compensation and RewardsHR professionals can review past compensations and set new compensation for each employee.

Resource ManagementProvides analytics on time and cost by employee and project to predict performance and improve talent utilisation


Archetype 3:

Talent Deployment Solutions (TDS)“Specialist tools with focus on talent deployment and resource performance prediction”

Talent Deployment Solutions are specialist tools that focus on only a narrow cross-section of the Talent Management Wheel, employing mostly descriptive measures. Their focus is on project and resource management. Here, they offer predictive analysis of resource utilisation, such as to allocate

talent skills and capabilities to projects, and carry out scheduling tasks. For doing so they collect and use limited employee-related descriptive data, always with the aim to optimize project performance and resource allocation.

Qubix People Management Cloud

Qubix is a globally operating consulting and applications company with a focus on Oracle’s ERP solutions. In the HR space Qubix offers its People Management Cloud which focuses on workforce optimisation, collaboration, employee engagement and project management. The tool offers predictive analytics on employee performance to predict outcomes and to improve talent-project fit. Qubix does not try to be a comprehensive talent management solution but rather a solution that utilizes several insights on the organisation’s workforce to further enhance business performance.

Retention Provides basic leave management capabilities

Performance Management Predicts employee performance to more accurately predict project outcomes

Talent Review Offers skills and experience profiling

Resource ManagementMatches employees to projects based on skills and experience, predicts project outcomes, provides insights with embedded analytics, time & labour tracking and ad hoc reporting

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Archetype 4:

Talent Lifecycle Analytics Solutions (TLAS)“The People Analytics heavy weights with broad predictive capabilities”

Talent Lifecycle Analytics Solutions offer the most comprehensive predictive, as well as some prescriptive capabilities, especially in the categories Retention and Recruitment and Selection. In addition, TLAS cover most areas of the Talent Management Wheel with at least descriptive features. A strong focus of these tools is talent retention management, with sophisticated predictive capabilities to forecast if employees are at risk of leaving

the company. In Recruitment and Selection, they further utilise prescriptive analysis to provide hiring managers with suggestions for the best fitting candidate for new and future positions. Additionally, they may give advice for specific competences to look for in an applicant. Given their strong focus on hiring, performance and retention, we termed this archetype Talent Life-cycle Analytics Solutions. TLAS typically do not cover resource management.

PeopleStreme

PeopleStreme Pty Ltd, founded in 2001, is a home-grown business headquartered in Melbourne with a world-wide client base.

PeopleStreme offers a comprehensive HR suite, with various modules for each challenge that HR departments face. Through surveys and filtering, its tools predict changes in the company and automatically preselect job applicants. It also provides a set of online training courses for compliance and HR. Predictive analytics are used for early detection of employee attrition and risk reporting of potential leaves to the responsible manager. Prescriptive analytics are used to filter job applicants based on company profile and job requirements to make hiring suggestions to the HR department.

PeopleStreme covers most parts of the Talent Management Wheel with predictions and suggestions on the Recruiting and Retention part. It also provides additional metrics, analytics and services like training and automatic feedback.

Retention Surveys to evaluate employee satisfaction, to automatically detect changes in satisfaction and predict future attrition. Calculates a Flight Risk score as percentage for each employee.

Performance ManagementHR professionals and managers can set measurable objectives, PeopleStreme identifies competency gaps

Development and TrainingOffers online courses: HR professionals and managers can schedule trainings based on identified skill gaps

Recruitment and SelectionAbility to automatically filter job applicants based on company profile and job requirements. Also provides suggestions for future employees with reasoning

Talent Review Creates talent profiles with skills and development for each employee

Compensation and RewardsHR professionals can review past compensations for each employee and issue new compensation with automatic budget control


Archetype 5:

Specialist Talent Solutions (STS)“Tools that experiment and pioneer new ways of doing people analytics”

Specialist Talent Solutions (STS) comprise a group of tools whose only commonality is that they feature only very selective talent management capabilities. Typically, they interpret one area of the Talent Management Wheel in a particular or unique way, and thus will complement tools from the other four archetypes. For example, there are survey-based solutions (Culture Amp, Betterworks) that assist HR professionals in conducting employee surveys

and their analyses, in some cases offering benchmarking by comparing answers with a database of companies in a similar business field. Another example, Wooboard focuses on gamification and employee engagement, implementing a public recognition and rewards scheme for employees within the firm. Overall, it remains to be seen which of the novel concepts offered by tools in this archetype will take hold in the market and whether more solutions will follow.

Culture Amp

Culture Amp, founded in 2009 and based in Melbourne, focuses on surveys to get direct employee feedback on satisfaction, employee career progression and their views of the company.

Survey questions can be extracted from Culture Amp’s library or created manually. Resulting statistics of these surveys can be benchmarked against other companies to identify competence gaps and room for improvement, development or training. Managers, individuals and teams can also request feedback on their performance. They receive a report that shows strengths and weaknesses as well as development opportunities. Culture Amp’s aim is not to offer comprehensive talent management functionalities but to focus on development opportunities for the company and individual employees.

Performance Management Provides analytics of strengths and weaknesses for employees and departments based on survey results and benchmarks against other companies

Development and Training Identified strength and weaknesses can be used to identify knowledge gaps and development potentials for employees to then schedule trainings

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Case study 24 People Analytics – Using Data and Algorithms to shape Employee Experience

Case study 25

Client case study 1:

Start slow, learn to build capability, then integrate.ContextCapgemini met with the Acting Head of Human Resources at a Large Australian Utility to discuss his views on People Analytics (PA) as a discipline and how his organisation envisions incorporating People Analytics to drive more informed decision making and address broader business issues. The Client is inching towards implementing People Analytics capabilities, with a focus on driving insights based on employee data available. During the early phases of implementation, the Client encountered challenges due to the lack of unified and advanced systems to support a fast and full-scaled adoption – an issue they recognised will be ongoing for at least three more years preceding a 2019 effort to begin to integrate and retire numerous internal systems.

Current StateThe Client has taken a slow and progressive approach towards implementing their People Analytics project. One of their first steps was to re-design reports to provide leadership with simple and actionable insights into the employee experience. The reports are now collated by a single data analyst in a monthly dashboard. These reports incorporate systematic analysis of data, such as:

• Unscheduled sick leave to identify and interpret any patterns

• Women with STEM (Science, Technology, Engineering, and Mathematics) backgrounds to track diversity of education and experience

The Client is now gradually moving from simply utilising PA as a reporting competence towards developing a more robust predictive analytics capability. For example, the

Client ran a study which revealed a correlation between home addresses and employee turnover. They found that people who resided far distances from the head office departed the organisation after one to two years. Based on this insight, the organisation began considering ways to provide alternative and more flexible work opportunities for employees.

ChallengesIn the process of building their reporting strategy around data analytics, the Client encountered three main challenges which impacted adoption. Firstly, the Client had a limited People Analytics capability (e.g., no specialised skills) – therefore HR management started by recruiting a Data Analyst solely focusing on interpreting insights from employee data. Secondly, disjointed and isolated business systems lacked integration necessary for ease of data flow. Thirdly, ongoing large-scale IT projects competed for investment and resources. These challenges highlight the importance of aligning leadership across functions to fully understand the potential return on investment of people management data and analytics.

Future of PAThe future of PA is uncertain, specifically due to growing cyber-security and data privacy concerns, as well as potential for full adoption by employees. Data securitisation and transparency may present as barriers to scale and adoption of PA. However, companies already have access to employee data and are already making various uses of it. Then, why not use this for good to benefit both the employee and the business?

Let information be free!”


Client case study 2:

Quick wins through digital employee engagement analyticsContextOver the course of two years, a Multinational Professional Services Client embarked on a journey to enhance their Employee Experience. As part of this broad initiative, the Client sought to modernise their methods to engage with employees with real- or near-time and embed data-driven decision-making capabilities into the organisation. Previously, the Client (more than 250,000 employees globally), distributed a Global Employee Survey every 18-months to collect feedback, measure engagement, morale and performance. Although it was distributed online, the process proved manually laborious, and required up to three-months for teams to analyse the data and provide results to the Board of Directors (by which time the data was stale).

ChallengesThe Client wanted to explore enhanced solutions to collect employee feedback and make real-time business decisions. The Human Resources team led the initiative to explore options to achieve this objective, with interest in identifying an agile, strongly employee-centric and data-driven solution that would be capable of data scraping both internal and external sources. After assessing and benchmarking the solutions of leading vendors the team discovered and enlisted a small organisation – designers of a bespoke People Analytics & Employee Engagement Software – to support their pilot. The platform is the world’s leading platform for measuring and improving Employee Engagement and was fit-for-purpose to the Client.

SolutionThe HR team encountered challenges throughout the design and implementation journey, but identified significant opportunity to evolve the Client’s current employee engagement methods:

• The solution provides access to concise and transformed data (minimising information overload, presenting visual dashboards, etc.)

• Data collected reflects honest feedback from employees (all answers are fully anonymous). Employees can submit free text comments, providing rich insights.

• The vendor customised the product in collaboration with the HR team

• The pilot launched in eight countries and ‘Champions’ were recruited in each to build awareness, understanding, and engagement to ensure a successful adoption and future scaling of the platform

• The collection of data strictly adheres to data privacy standards

Looking ahead, the Client aspires to collect data via passive listening channels (e.g., Yammer, LinkedIn, other social networks) and incorporate the data into the platform, ensuring the Voice of the Employee is truly captured as heard across channels. While the HR department drove the initial deployment and advocated for the change, they envision the platform to be adopted more broadly as a valuable business tool by the organisation.

Main OutcomesSeveral benefits of this implementation were identified, namely:

• Feedback collection: Constant and anonymous employee feedback in groups, allowing more robust and real-time analytics of employee sentiment and instant decision-making. Employees are questioned to reveal what drives their engagement: reward, work challenges, facilities and environment at various points in their employee journey.

• Manager autonomy: Managers are empowered by the data and gained autonomy to closely review results without reliance on others to get access to and transform raw data. They can drill-down into results to better understand the context of a success or issue. Managers will continue to receive training to interpret data dashboard metrics, as the Client has discovered those who attended learning events were more engaged with the platform.

• Improved reporting: Reports are produced in an easy-to-digest format and dashboards contain native analytics to predict future behaviours (e.g., it was revealed during the first pilot which conditions develop high performing managers during the first pilot: presence at work, freedom of expression, stretch challenges and senior leadership support)

Capture data on employee experience from various channels to truly hear the employee voice.”

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Challenges of PAWhile the potential of PA have been highlighted in the professional and academic discourse around it, we maintain that there are good reasons to be sceptical about some of its assumed benefits. The reasoning for this scepticism is anchored both in our own research and observations of how technologies are used by managers, as well as in the wealth of scientific knowledge about human cognition, algorithms, and artificial intelligence. We will lay out our arguments in three steps, each corresponding directly with one of the promises of PA outlined earlier.

1. Algorithms rely on subjective human input

Much of the literature on PA assumes that algorithms help managers make rational and unbiased decisions because they objectively analyse large amounts of data that reliably represent different areas of employee performance. While PA applications indeed utilise large amounts of data that are processed by algorithms to recommend courses of action for managers, this process is far from being rational or objective.

Algorithms are designed by people from a particular social and organisational background. Therefore, the algorithms that they design may reflect the institutional structures within which they live and hence reproduce the biases inherent in the data they are using. For instance, hiring algorithms have been shown to preferentially treat job applicants with “white-sounding” names. This is because they were designed to replicate existing hiring patterns within the organisations that used them, which in some

cases were biased in favour of people whose names “sounded white”.

Moreover, algorithmic recommendations are based on an analysis of large amounts of digital data. However, which data to collect and how to analyse the data are still human decisions that cannot escape subjective preferences. For example, when designing an algorithm to assess workers’ productivity, different data can be used, such as how much revenue workers have generated, how many clients they have brought in, or how much time they spend on email. However, the algorithm could also be designed to account for the employee’s level of social engagement, training record, and absenteeism. It is the designer’s or manager’s decision which data are reflective of ‘performance’.

And any such reductive or partial selection of performance measure will always shape worker behaviour downstream, which runs the risk that they ‘work to the measure’ at the expense of those activities that might be important yet are not measured.

2. Data can never fully or accurately reflect human behaviour

It is commonly claimed that by collecting and analysing data about different organisational areas, PA provides managers with an objective and comprehensive understanding of their


organisation. However, in practice, when comprehensive and valid data are out of reach, or when faced by time or financial constraints, organisations will leverage data that are either inexpensive to access or readily available, and use them as proxies for human behaviour or skills.

For example, organisations may evaluate the dependability and trustworthiness of job applicants by analysing these applicants’ credit scores. Similarly, organisations may assess the quality of their sales people based on client feedback surveys. In both examples, simple proxies are used to gauge complex traits and behaviour instead of more nuanced and complete measures. Consequently, PA can promote a false sense of security in the data because they present over-simplified and fragmented models of highly complex phenomena.

3. Predicting the future is highly problematic

Algorithmic calculations embedded in PA are based on the assumption that by analysing sufficient amounts of data, human behaviour becomes predictable. However, predictions from algorithmic analysis often leads to organisational decisions and processes that comply with such predictions thereby creating the conditions that make those predictions more likely. By facilitating such self-fulfilling prophecies the organisation might blind itself to alternatives.

For example, a company may employ an algorithm to assess the revenue potential of its recently-hired sales people. Such an algorithm can be based on their performance on standardised tests that they had completed during their on-boarding process, as well as on reviews from their previous employers. This analysis can be used to rank all new sales people. Using this list, the company can then differentially allocate training resources and invest more effort, time, and money in new recruits with higher potential, at the expense of those who were ranked lower on the list. However, this preferential practice itself is likely to produce the very results that the initial analysis predicted, namely, that the higher-ranked recruits will perform better than those ranked lower on the list.

Such occurrences make it difficult to accurately assess the predictive power of PA applications and the algorithms that they use. In most practical situations, it is materially impossible to separate the effect of different antecedents and isolate their respective impacts on organisational outcomes. Therefore, in most instances, the ability of PA applications to predict future trends remains under question.

An additional problem with algorithmic predictions is that they always extrapolate from past data. The future is thus implicitly assumed to be an extension of the past. Such rationale hold true under conditions of stability, yet will fail the more market conditions are prone to change. In times of disruption and change, algorithmic predictions on past data can thus present a considerable risk to the organisation. PA provides more insight and great confidence in the decision making. It is important to be aware of these limitations of PA and address any concerns through other means.

We must not forget that data and algorithms are the result of human activity – they are neither objective nor unbiased”

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How to kick-offa People Analytics project?


The first step in any PA project is to identify the challenge at hand or pertinent pain points: Which critical business talent is at risk of resigning? Which human resources should we allocate to support each business function? Which candidate profiles produced the most successful hires in the organisation? How might turnover in a specific department impact the organisation’s ability to achieve business goals? These questions illustrate today’s challenge for organisations as they seek to attract, nurture and retain the best fit-for-purpose talent.

Laying the foundation to a successful PA implementation requires a concise project vision. Based on Capgemini’s client transformation experience, we have observed patterns and best practices that, when considered, will underpin a successful PA implementation.

1. The Employee Experience Imperative Employees are the most important customers for any PA implementation project. Employees can be the most influential brand advocates of an organisation. If nurtured with the appropriate support, employees will be committed to fulfilling the company’s vision and objectives. Hence, human-centric design principles should be applied at all stages of the project. To collect the ‘Voice of the Employee’ consider:

• What do your employees value most (professional development opportunities, work-life balance, flexible work environment, mentoring, autonomy)?

• What are their habits (at work and in their personal life)? • How satisfied are employees with the organisation’s

ability to meet their needs?• How are employees benchmarking their experiences to

their peers at competing organisations?

Consolidate the feedback into a set of key insights to share with top management. Look for patterns that emerge from the insights to begin to shape employee

personas. At the end of this phase, you should have a better understanding of the challenges that surface to guide the implementation process.

Focus on the Employee Lifecycle Each stage of the Employee Lifecycle corresponds to and can be addressed by a different PA capability. In order to support employees throughout their employment lifecycle companies must collect the right data at the right time to generate relevant insights for employees.

2. Align HR Strategy with Global Objectives Often PA projects are handicapped due to poor alignment with strategic business targets. It is important to frame the business case for a PA capability in the context of general business outcomes and emphasize its potential to deliver value to other critical business functions.

Human resources are the heartbeat of any organisation – they drive the creation of intellectual property, products and services – and the HR function is the floodgate in and out of the organisation. With such tremendous responsibility, there is a strong case for investment in PA at every stage of the employee lifecycle.

3. Cross the Starting Line Before the Finish Line: Get the data right Conducting an end-to-end data quality assessment is necessary to determine the current suitability of data to support PA objectives. The assessment should include a review of data completeness, validity, accuracy, and consistency. Specifically, it should address the following questions:

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• How long does it take your team to derive results from a given data set?

• Which employee attributes is the organisation currently capturing? For what expressed purpose?

• Where is the data stored and how is it shared? Is it in siloed systems or in one centralised repository?

• What are your data storage costs? Are they constant or rising relative to the amount of data you are collecting and your data operations?

• What is the data sharing culture?• What is the main purpose of HR Analytics as-of now

(E.g., Workforce productivity, Learning & development focused, Recruitment of new profiles)

• Does the current data strategy address the key challenges of the function?

• What are the gaps? Where are the opportunities you can foresee?

4. Bridge the Gap, Plot the RoadmapBased on the data quality assessment process, the next step is to begin identifying your PA journey. For instance, if your data costs are increasing despite a constant data pool size, you may be capturing poor quality data up-front that cannot be utilised and is sitting idle without returning value.

In addition, your review may surface that a variety of PA tools are being utilised by lines of business, but with little

coordination. This can indicate that you may be able to leverage features of an existing solution.

5. Co-create, Iterate and Sell To ensure successful project outcomes, Capgemini strongly promotes employee buy-in as well as partnerships with the HR team, technical data scientists, organisational leaderships and vendors. A collaborative approach can result in a unified, motivated and aligned team.

In parallel, it is recommended to support the implementation roadmap with a business case. This may prove challenging as HR is generally perceived as a cost centre to any business. Therefore, it is important to emphasise the potential benefits of PA rather than focus on the costs associated with its implementation. For instance, a well-functioning HR function can increase employee satisfaction and therefore positively affect profitability. Additionally, HR departments can delegate transactional work to PA tools and focus on added-value activities such as talent management, personalised career paths, training, etc.

Finally, identify metrics to measure project success across phases of the implementation. Consider how these metrics may vary across stages to be able to reliably track progress over time.

Key Steps • Attract profiles from various backgrounds

• Motivate employees to attract new profiles

• Ensure employees are the first advocates of the company

• Ensure a smooth Day 1• Help new employees to

transition into their new job, provide tools

• Ensure appropriate support• Provide clear development

paths• Create excitement

• Provide and follow-up on learning opportunities

• Create the right environment for innovation and self-emancipation

• Ensure recognition, continuous listening

• Collect regular feedback and adjust

• Understand reasons for separation

• Collect detailed feedback and ideas for improvement

• Address gaps in engagement

• Stay in contact, appraise for role change

Value for People Analytics

• Review large No. of applications through a methodical approach

• Match the right skill to the right job

• Automate the onboarding ritual• Reduce logistics effort,

potential mistakes, manual processes

• Provide development trends, personalised paths

• Provide detailed, tailor-made and near-time dashboards to management

• Identify behavior patterns, predict performance

• Predict employees likely to quit or look for other positions

• Create an end-to-end view on the employee career

Data to be collected

• All basic employees’ data (ID etc.)

• Past exper iences, projects, skillset, etc.

• Adjustments of all data collected to date

• Employee first feedback

• Employee performance reviews & ratings

• Peer evaluations• Online activity (L&D, Linkedin,

etc.)

• Employee feedback and gaps identified

• Employee next position

Key Challenges

• Get the right profiles, at the right time, in the right position

• Standardise the process, be efficient and avoid ‘first image’ mistakes

• Provide a differentiated, personalized path for each employee that meet their needs and expectations

• Make sure leaving employees are still ‘ambassadors’ of the company

Recruitment Onboarding Development & Retention

Seperation/Role change


About the Authors

Kai Riemer is Professor of Information Technology and Organisation and leader of the Digital Disruption Research Group (DDRG) in the University of Sydney Business School. He studies the disruptive nature of technology and how people make sense of innovations.

[email protected]: @karisyd

Christopher Harth-Kitzerow is a student of Information Systems at the Technical University of Munich and research assistant at the University of Sydney Business School. Christopher’s current studies focus on efficient algorithms and data structures.

[email protected]

Uri Gal is Professor in Business Information Systems at the University of Sydney Business School. His expertise is in IT-enabled change processes and his research focuses on the effect of new digital technologies on people and organisations. He is particularly interested in the use and impact of Workforce Analytics on managerial decision-making, organizations, and employees.

[email protected]

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Australian DigitalTransformation Lab“Co-Creating Digital Advantage”The Australian Digital Transformation Lab (ADTL) is a joint venture between The University of Sydney Business School and Capgemini Australia. It combines the established academic research skills and knowledge of the Business School’s Digital Disruption Research Group (DDRG) with Capgemini’s expertise, leadership, and applied innovation acumen in business transformation and organisational change.

The Lab engages in a range of applied research activities and produces insights in the following two key areas, with a distinct Australian focus:

1. Digital transformation of business and customer interaction: Digital technologies are transforming established business models and modes of interactions between customers and business partners. Often narrowly viewed as disruptive threats, the lab highlights and explores the innovation opportunities of digital technologies for rethinking value creation and customer interaction.

2. Digital transformation of work and organisation: Digital technologies enable new forms of work, organisation and culture. The Lab targets the organisational impact of new digital technologies with a particular focus on

the paradox and challenges of managing the bottom-up adoption of transformative digital technologies.

As an applied research lab the ADTL combines Capgemini’s proven Applied Innovation Exchange, a digital lab and methodology for engaging in customer-centric design, rapid prototyping and digital experimentation, with the academically-minded network and thought-leadership of the Business School’s Digital Disruption Research Group.

It combines the established academic research skills and knowledge of the Business School’s Digital Disruption

Research Group (DDRG) with Capgemini’s expertise, leadership,

and applied innovation acumen in business transformation and

organisational change.


References

Cherry, J. 2017.

“How to Start a Workplace Wellness Challenge Using Fitness Trackers”, in: Glassdoor:https://www.glassdoor.com/employers/blog/start-workplace-wellness-challenge-using-fitness-trackers/

Kennedy, M. 2017.

“Wisconsin Company Offers To Implant Chips In Its Employees” in: NPR:https://www.npr.org/sections/thetwo-way/2017/07/25/539265157/wisconsin-company-plans-to-start-implanting-chips-in-its-employees

Lang, M. 2017.

“Electronic Tracking Spurs Workplace Privacy Debate”, in: Government Technology:http://www.govtech.com/policy/Electronic-Tracking-Spurs-Workplace-Privacy-Debate.html

Stahl, G. K., Björkman, I., Farndale, E., Morris, S. S., Paauwe, J., Stiles, P., Trevor, J. & Wright P. 2012. “Six Principles of Effective Global Talent Management”. Massachusetts Institute of Technology.

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The University of Sydney Business School is a world-class business school at the forefront of global business and management education. Relevant and focused on today’s business world, we are the only Australian business school to achieve membership to CEMS – the Global Alliance in Management Education in addition to international accreditation from AACSB and EQUIS.

Dedicated to the highest-quality teaching and to ground breaking research, our staff are industry leaders with a passion for creating tomorrow’s world business leaders.

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