/ Smart DataData AnalysisPart I



In the field of human resources (HR), both big and small enterprises are facing a couple of strategic challenges:

  • get the best fit candidates on board [1],
  • constantly educate their employees according to company needs and personal skills, and
  • retain acquired and educated talents.

By best fit we mean not only highly skilled, motivated and flexible candidates. We are also referring to professionals with the “right” mind-set, principles and behaviour suitable for a particular business culture.

Known HR methods based on feelings, impressions and relationships are being disrupted by today’s rapidly developing technology: its capability to analyse a wide variety of large data sets is constantly growing and opens new opportunities for data-driven decisions. It can indeed help tackle HR challenges in recruiting, retention or productivity more efficiently and do it faster as with traditional methods.
State-of-the-art systems can bring together structured and unstructured data, connect and process it in shortest time using in-memory computing and analyse it with machine learning algorithms to reveal information crucial to decision making [2]. HR challenges in recruiting, retention or productivity can be tackled more efficiently and faster as with traditional methods.
Data can tell how well a candidate is suited for a position, if employees are satisfied with their job, and in which areas a person or team could profit from targeted measures for improvement. New progress in advanced analytics and artificial intelligence (AI) [3] can also help foster and make sense of all employee touch points whether in structured HR processes or less structured social and mobile interactions [4]. This unlocking of data by means of statistical and AI methods for the field of HR is called data-driven HR [5].


In order to benefit from the full potential of data-driven HR, appropriate data needs to be collected, managed and analysed according to your business goals. In the following we exemplarily list a number of data sets which can be used in machine-based analysis supporting different HR processes.


Since skills become outdated very quickly, a candidate’s standard application documents such as CV and diplomas can be regarded in combination with data available on the web and in social media networks. Analysing data from LinkedIn profiles (CV as well as activity feeds), professional tweets, websites of attended conferences and events, or activities in specific communities (like GitHub or StackOverflow in case of technicians) can lead to insights into the candidate’s professional aptitude. For a personal suitability check, tweets with personal interests as well as social networks like Instagram, Facebook, Google+ and further personal appearances on the web like association memberships can be considered. This information might be used when looking for leadership positions, where fast decision making, creativity and high analytical skills are frequently seen as more important than that a degree in economics. Besides the „right fit” analysis, this data can also be applied to identify the newest recruiting trends, e.g. knowing where high or low performers come from [6].


The data sets described above can also be used to monitor an employee’s job satisfaction index. This information is particularly crucial in the age of digitization where job changes happen frequently, and the employee attrition rate is difficult to control. Among the top-tech companies, last year Facebook had one of the best retention rates in the United States with less than two years of average employee tenure [7].

In combination with internal employee data like internal surveys, performance, learning activities as well as management assessments, this enables to provide a 360-degree view of the employees which can be translated into specific HR interventions: evaluating an employee’s development index, offering them appropriate trainings, mentoring or even job changes inside the company and helping them meet their career aspirations [6:1]. All these measures lead to higher retention. And there is one more benefit of using data analysis for HR purposes: Doing so considerably increases the fairness in distributing rewards and recognitions as well as in identifying high performers and high potentials [6:2].


Finally, through the analysis of employees’ behavioural data like sociometry patterns, location, movement and posture in the workplace, the work environment of the teams can be optimised, as shown in the case of a call centre of the Bank of America [8]. By improving the interactions between employees and employers and even the office floor plans, the company has been able to save $15 million a year.

The next part of this series will shed some light on the various ways to collect, process and analyse data relevant in the field of human resources. Stay tuned!

  1. John Sullivan. 10 Reasons To Shift To A Data-Driven High Business Impact HR Model. Source: https://www.visier.com/clarity/10-reasons-shift-data-driven-hr-model/, visited on 21.03.2018 ↩︎

  2. Martin Kleppmann. Designing Data-Intensive Applications In Designing Data-Intensive Applications. O’Relly Media, September 2014. ↩︎

  3. Human + Machine: Reimagining Work in the Age of AI Cover Paul R. Daugherty, H. James Wilson Harvard Business Press, 20.03.2018 - 264 Seiten ↩︎

  4. The Future Workplace Experience: 10 Rules For Mastering Disruption in Recruiting and Engaging Employees Cover Kevin J. Mulcahy, Jeanne Meister McGraw-Hill Education, 02.11.2016 - 304 Seiten ↩︎

  5. Creative HRM. About Human Resources. Source: https://www.quantifiedcommunications.com/blog/people-analytics-data-driven-human-resources/, https://talentculture.com/the-time-for-data-driven-hr-is-now/, http://www.creativehrm.com/data-driven-hr-blog.html, visited on 18.03.2018 ↩︎

  6. The HTR News Desk. How to use data for employee retention. Source: https://www.hrtechnologist.com/articles/productivity-analysis-hr-analytics-tools/how-to-use-data-for-employee-retention/, visited on 10.03.2018 ↩︎ ↩︎ ↩︎

  7. Jason Wong. How Long Do People Stay at top tech companies? Source: http://risehigh.co/blog/length-of-employment-in-top-tech-companies/, visited 20.03.2018 ↩︎

  8. Althea Chang. Big Brother & Big Data: Good for Productivity? Source: https://finance.yahoo.com/blogs/big-data-download/big-brother-big-data-good-productivity-180650903.html, visited on 10.03.2018 ↩︎

    Olga Mordvinova

    Olga Mordvinova

    Passioned to connect data with use cases and AI to save time and generate more business value.

    Read More
    Dr. Andrea Rösinger

    Dr. Andrea Rösinger

    Chief Digital Officer, Managing Partner, Executive Advisor for Business IT Transformations, Certified Coach (EBS)

    Read More