Challenges Of Big Data & Analytics In HR

Transcription

1 Challenges Of Big Data & Analytics In HR Data & Brain Power HRDF November 2017

2 Agenda HR & Big Data: Creating business impact Reality check: Where s our data? And what s in it? Reality check part 2: What are we supposed to do with all of this data? What might the future look like? 2017 Korn Ferry. All rights reserved 2

3 1 HR & Big Data: Creating business impact

4 If we really knew our employees, candidates, and alumni better what might be possible? Increased productivity Better retention Greater engagement More effective recruiting Empowered talent Transformed leadership Activated innovation 2017 Korn Ferry. All rights reserved 4

5 Today, we want data to answer a far larger array of questions than in previous eras Historically What experiences have potential hires had? How are people performing? Are we overpaying or underpaying? Are our employees engaged? Today What is the potential of existing and outside talent? Do we have the right people in the right jobs? Are we even defining performance properly? Are our people performing well against what s expected of them in the future? Are we overspending on rewards our talent doesn t care about? Do we have the right leaders in succession pipelines? Are our succession criteria even valid? What do we not know about our people that we should? How do people innovate? How do people collaborate? How do our employees really feel about us? How can we avoid costly ethics, fraud, and security issues? 2017 Korn Ferry. All rights reserved 5

6 Analytics capabilities makes a difference in outcomes Organizations more active with HR data analytics are 2x as likely to improve their recruiting and leadership pipeline 3x as likely to realize cost or efficiency gains 3.5x as likely to get the right people in the right jobs. Source: Bersin Korn Ferry. All rights reserved 6

7 Data is driving some surprising learning The best collaborators are exhausted and disengaged. More than anything else, psychological safety drives team effectiveness. Collaborative Overload, HBR, Adam Grant, Reb Rebele, Rob Cross Google, Understanding team effectiveness 2017 Korn Ferry. All rights reserved 7

8 On a stylized basis, what should the data journey look like? Collect and clean data Deploy appropriate analytics Enact decisions based on analytically derived results Define key data sources Identify critical business issues to be addressed 2017 Korn Ferry. All rights reserved 8

9 The reality is a bit more chaotic, though

10 2 Reality check: Where s our data? And what s in it?

11 No, seriously where s our data? HR Information Systems (HRIS) Learning management systems (LMS) Applicant Tracking Systems (ATS) LinkedIn or similar Performance systems Total Rewards Systems Ad hoc spreadsheets Social media Literally, pieces of paper (filed or unfiled) 2017 Korn Ferry. All rights reserved 11

12 A scary statistic Data scientists spend 85% of their time collecting and cleaning data Korn Ferry. All rights reserved 12

13 Data types and their associated tradeoffs Data type Advantages Challenges Structured data Easy to work with Garbage-in, garbage out if you don t ask the question, you won t get the answer Unstructured data When properly analyzed, can contain rich information More difficult to work with without sophisticated analytical instruments Quantitative data Simple to drive to business conclusions Can be deceptive correlation does not imply causation, etc. May mask underlying causes. Qualitative data Contains nuance and detail Can be difficult to benchmark Internal data External data Attributed data Anonymous data Proprietary and reliably sourced (to some degree) Fresh and often benchmarked perspective Can connect to a broader system of information around a given individual Without attribution, contributions may be more accurate Can be deceptively unreliable, and stored across a diverse array of unconnected systems; internal view may be fundamentally distorted Available to competitors; reliability can be spotty depending on source; can be expensive Due to discomfort around attribution, individuals may withhold critical information Unable to connect data to other data around individuals Self-reported data Reflects individuals beliefs May be skewed to influence outcomes Data reported by others Can contain less bias May be skewed to influence outcomes 2017 Korn Ferry. All rights reserved 13

14 Driving data quality: 9 key questions Who entered this data, and why? Would anyone want to manipulate either data points or the whole data set? Are there structural or technological barriers to accuracy or completeness? Has anyone touched this data since it was entered? What is the vintage of this data? What is the diversity of the population from whom this data is taken? Is there any element of self-selection? Is this data face valid? Could unconscious bias influence this data? How can this data be evaluated for accuracy and precision? 2017 Korn Ferry. All rights reserved 14

15 Organizations are changing how they look at some classic data types 2017 Korn Ferry. All rights reserved 15

16 and are examining new data sets for powerful insights INTERNAL EXTERNAL Employee to employee communications Interactions between traditional data sets Social media Supply and demand vs roles 2017 Korn Ferry. All rights reserved 16

17 3 Reality check part 2: What are we supposed to do with all of this data?

18 The core goal of analytics: separating signal from noise This is not always as easy as it looks: 2017 Korn Ferry. All rights reserved 18

19 New analytics techniques are adding tremendous value... Network analysis Sentiment analysis Predictive analytics Artificial intelligence 2017 Korn Ferry. All rights reserved 19

20 but organizations are getting quick wins by going back to basics The answers to many key questions Are people using the tools we give them? How do differences in employee population relate to outcomes? Are we rewarding people consistently and fairly? Where might employee dissatisfaction cause issues? often start here: 2017 Korn Ferry. All rights reserved 20

21 Visualization is emerging as a key capability It s not exactly new but it can be pretty cool Korn Ferry. All rights reserved 21

22 HR analytics talent is a critical piece of the puzzle HR departments around the world are adding dedicated analytics personnel Key qualifications: Excellent grasp of data analysis in business context Fluent in HR systems Strong capability in producing compelling outputs (with visualizations a strong plus) Learning agile and emotionally intelligent Organizations that add such personnel have reported deeper engagement with stakeholder populations as a result of a better data-driven dialogue 2017 Korn Ferry. All rights reserved 22

23 Leading practice in analytics and caveats Invest in well-functioning systems BUT Understand that systems are no substitute for complete and accurate data or a business-driven understanding of the answers you re after Get the right HR analytics talent in place BUT Don t forget to unearth and utilize the tacit knowledge of the organization that truly drives context Thoughtfully mine your organization s data BUT Understand its limitations Learn from data from the outside world BUT Have a strong view on your own environment and context first Benchmark, benchmark, benchmark BUT Know where your organization is unique Delve into complex issues BUT Keep outputs simple Use data to illuminate the future BUT There s no single predetermined future! 2017 Korn Ferry. All rights reserved 23

24 4 What might the future look like?

25 The future is both exciting and worrying Can data analysis begin to shape outcomes in real time? Could algorithms lock us into narrow patterns of behavior and choices? Will predictive analytics mean we hire only the right people? Will we hit a point of knowing too much about employees? Can we avert misbehavior before it happens? Will Big Data cause us to lose nuanced understanding of employees? 2017 Korn Ferry. All rights reserved 25

26 Your organization s future is shaped by a set of critical choices Do we have the right HR analytics talent? What kinds of analysis actually give us the answers we need? MOST IMPORTANT: Do we have the courage to act on what we learn? What information do we actually have? What systems best support our information needs? Collect and clean data Deploy appropriate analytics Enact decisions based on analytically derived results What critical levers do we need to hit to succeed sustainably? Identify critical business issues to be addressed Define key data sources 2017 Korn Ferry. All rights reserved 26

27 Thank you