Game Based Aptitude & Personality Traits Assessment - Arctic Shores Candidates are more successfully matched to roles through trait profiling. It aims to eliminate recruitment bias, overcomes diversity-linked timed test bias, creates a positive candidate recruitment experience • Measures aptitude, cognitive and personality traits C R A F T E D B Y C O N C E N T R A 23
Game Based Aptitude & Personality Traits Assessment - Arctic Shores How it works.. Outputs and Feedback.. Benefits.. Further Information.. Email: Tomas.Kuzmickas@arcticshores.com www.arcticshores.com C R A F T E D B Y C O N C E N T R A 24
C R A F T E D B Y C O N C E N T R A 25
Physical Data Collection – the Buzz Box from ‘We Love Surveys’ Prism Web Surveys Buzz Box Application • Real Time Feedback Quick and simple surveys • • Real Time Feedback Invite via email, social Ask up to 5 questions • media, website etc Works Offline Tops are interchangeable • • Simple or Complex surveys Simple or Complex surveys • • Completely bespoke design Responsive as standard Excellent response rates • Anonymous (unless • Bespoke design Less than 20 seconds to otherwise requested) • Multi-Lingual complete • Perfect for fixed and mobile • Anonymous (unless use Anonymous otherwise requested) • Multi-Lingual Report experience by the minute
Physical Data Collection – for Everyday Engagement In many organisations there is a reliance on annual employee surveys and customer mystery shopping Following these surveys reports can take a significant time to be received Mystery Shopping is a quick snapshot and can be inconsistent Scores very often do not reflect the associated comments. o 2 points in the year cannot give a reflective view of the customer o experience Quite often the results of staff surveys are out of date before being received Questions are often not about things that the local manager can o impact We Love Surveys believe in everyday feedback We give front line managers and organisations information about how their customer and colleagues feel every day, allowing them to react quickly to challenges and issues. We help you to increase customer satisfaction and turnover through better service delivered by happy people
Example: moving from annual surveys to monthly team-level pulse to address the local drivers of engagement Image credit: 2012 Orange, Concentra Analytics Ltd
Analytics for Insight - Running an Analytics Workshop - Introducing Key Concepts - Understanding Commercial Objectives - Art of the Possible - Understanding your Vision - Project Prioritisation HR CIPD Workshop 29
Running an Analytics Workshop C R A F T E D B Y C O N C E N T R A 30
Overview of the facilitated workshop approach A facilitated workshop will create alignment on business priorities, lock in sponsorship for analytics and develop a 30-60-90 plan to accelerate your capability Project Selection and Prioritisation 7 8 Setting your Rules of the Analytics Vision Workshop Agree short Introduce key term roadmap Analytics Concepts 2 (DDO) 6 9 Demonstrating 3 Understanding the the Art of the 1 Business Strategy and possible Outputs: Commercial objectives • 30-60-90 day roadmap Introductions & in scope 5 Scope setting • Initial Vision for Analytics 4 Brainstorming tough questions Notes • Workshop should be as interactive as possible; our role is to facilitate not consult • All outputs should be captured and wrapped up into a playback deck of the day • Avoid discussions on technology selection; the purpose is to agree priorities and next steps C R A F T E D B Y C O N C E N T R A 31
1 Start with the end in mind.. Introductions & Scope setting C R A F T E D B Y C O N C E N T R A 32
2 Get everyone in the room.. Rules of the Workshop C R A F T E D B Y C O N C E N T R A 33
3 Introducing Key Concepts Introducing Key Concepts C R A F T E D B Y C O N C E N T R A 34
3 What is a Data-Driven Organisation? Introducing Key Concepts “An organisation that leverages insight in its decision making and achievement of its commercial objectives, continually developing the right mix of people, process, data, strategy and technology to achieve this.” Benefits for being a Data-Driven Organisation Faster Cheaper Smarter Make faster decisions as a Business Improved use of technology, data, Increased commercial effectiveness when you reduce your ‘time to and process can reduce the through the improved use of data in insight’ production cost of current reporting operational and management and allow for further innovation decisioning C R A F T E D B Y C O N C E N T R A 35
3 How does Data drive action? Introducing Key Concepts Consider all elements of people, process, data, infrastructure and strategy you need throughout the process of insight generation to mature your internal capability Data Analysis Insight Decision Action Source : Gartner C R A F T E D B Y C O N C E N T R A 36
3 Analytics Maturity Model – where are we? Introducing Key Concepts Consider all elements of people, process, data, infrastructure and strategy 1 2 3 4 5 Initial Building Integrating Widespread Fully Awareness Traction Capability Value Generation Integrated • • • Emerging drivers for Forming link between Business Strategy and developing Analytics business outcomes, Analytics form an active drivers for change and ecosystem • No/Basic Analytics Analytics Capability • Strategy Insight delivery model and • Analytics Strategy Service catalogue in place • Low appreciation for Forming across across all functional areas potential value/use cases Functional Areas • Innovation and disruption • Low data quality and KPIs • Proof of Concepts/Value baked into roles and fragmented, poorly aligned used across the toolsets and no clear ownership organisation as part of • Analytics linked to business • Low appreciation for data Analytics methodology impact and commercial governance , or how data • S ynergies emerging and gains. Moving to self- can be managed as an being acted upon funding . Asset • • Progressing to single Clear data governance source of truth and basic practices, known supply governance chains, business owners C R A F T E D B Y C O N C E N T R A 37
… 3 Discuss: Where would you place yourself on the Introducing Key … Analytics Maturity Model? Concepts 1 2 3 4 5 Initial Building Integrating Widespread Fully Awareness Traction Capability Value Generation Integrated • • • Emerging drivers for Forming link between Business Strategy and developing Analytics business outcomes, Analytics form an active drivers for change and ecosystem • No/Basic Analytics Analytics Capability • Strategy Insight delivery model and • Analytics Strategy Service catalogue in place • Low appreciation for Forming across across all functional areas potential value/use cases Functional Areas • Innovation and disruption • Low data quality and KPIs • Proof of Concepts/Value baked into roles and fragmented, poorly aligned used across the toolsets and no clear ownership organisation as part of • Analytics linked to business • Low appreciation for data Analytics methodology impact and commercial governance , or how data • S ynergies emerging and gains. Moving to self- can be managed as an being acted upon funding . Asset • • Progressing to single Clear data governance source of truth and basic practices, known supply governance chains, business owners C R A F T E D B Y C O N C E N T R A 38
3 Analytics Capability framework Introducing Key Concepts Developing Analytics as a competence has a number of elements under people, process, data and infrastructure governed by a coherent strategy all of which focus on insight generation Data Infrastructure Strategy Process People • Data Footprint • Architecture • Alignment to • Requirements • Sponsorship (incl. measures commercial management • Vendors and • Ownership and KPIs) strategy • Demand pipeline Partners • Talent • Data flows & • Analytics • Innovation • Reporting Management Sources Roadmap Sandbox lifecycle Mgt • Best Practice • Data Governance • Benefits tracking • Cloud vs On- • Business Process Sharing • Security • Internal comms Premises • Embed/Adopt • Learning • Business • Insight delivery • Service Ownership model Catalogue C R A F T E D B Y C O N C E N T R A 39
3 Why do organisations struggle to become Data Driven? Introducing Key Concepts Typical Blockers Identified Overwhelmed by Lack of consistent Lack of executive Incoherent Data model internal data volumes KPIs and measures sponsorship and variety Challenging Culture change and Poor implementation Poor adoption or relationship between ways of working experiences embedding Business and IT C R A F T E D B Y C O N C E N T R A 40
3 What are the people and skillsets that DDOs develop? Introducing Key Concepts Combining Technical and Commercial skillsets to solve business problems Technical Competences Business Competences Creates effective visualisations Understanding the commercial context Awareness of Business Process and Structured analysis & Data Modelling Performance Measurement Ability to build compelling narratives; Possesses software competences story telling Possesses soft skills for stakeholder Ability to extract & manipulate data engagement Performs technical and hypothesis testing Applies technology to solve business issues C R A F T E D B Y C O N C E N T R A 41
Understanding commercial objectives and associated people challenges C R A F T E D B Y C O N C E N T R A 42
4 Link commercial objectives to people challenges.. Understanding Business Strategy & Objectives in Scope C R A F T E D B Y C O N C E N T R A 43
5 Link people challenges to tough questions.. Brainstorming Tough Questions C R A F T E D B Y C O N C E N T R A 44
Art of the possible Words, words, words – but what’s the underlying message you are - conveying in everything from job descriptions to annual reports? - The impact of chat-bots on the HR department, now and in the future - Industry leading tools for Analytics C R A F T E D B Y C O N C E N T R A 45
6 Art of the Possible with Min Art of the Possible Min Bhogaita Analytics Director, Concentra UK Words, words, words – but what’s the underlying message you are - conveying in everything from job descriptions to annual reports? - The impact of chat-bots on the HR department, now and in the future C R A F T E D B Y C O N C E N T R A 46
6 Analytical tools vary in their features and strengths Art of the Possible Overview of common analytics tools ? ? Org Design, HR analytics, Transition Visualising data / dashboarding Management Suitable for beginners Suitable for capable Excel users Fast, intuitive drag & drop Messy people data Free trial link Request demo Creating an analytics “portal” ? ? Statistics and graphics Suitable for capable Excel users Analysts with stats degrees Great UI - no coding required Multiple stats tools, allows coding Personal version link Download link ? Workforce analytics and planning ? Manipulating / integrating data HR Analysts & senior managers Suitable for capable Excel users Integrating multiple sources & visualising Visually manage complex workflows Free trial link Request demo ? ? Basic data handling & models Embedded OEM analytics Integrated Suitable for beginners OEM analytics All HR users Flexible and well supported No data integration required C R A F T E D B Y C O N C E N T R A 47
6 Combine different tools to find the solution that works best for you Art of the Possible Data manipulation Data storage Data visualisation and analysis Develop a reliable single data set Create an accessible Build reporting and modelling with latest information that is tools to answer questions about environment to publish analysis structured to provide fast analysis the past, present and future and share insights Examples: Manipulation of complex data for integration into user portal Automated processing of messy data for intuitive visualisation Standard HRIS OrgVue integration for HR analytics/ modelling C R A F T E D B Y C O N C E N T R A 48
Understanding your vision for Analytics C R A F T E D B Y C O N C E N T R A 49
7 A simple exercise for building a Vision statement.. Setting your Analytics Vision C R A F T E D B Y C O N C E N T R A 50
7 Sample Vision Statement Setting your Analytics Vision C R A F T E D B Y C O N C E N T R A 51
Project Identification and Prioritisation C R A F T E D B Y C O N C E N T R A 52
8 Translate your tough questions into potential projects.. Project selection and Prioritisation C R A F T E D B Y C O N C E N T R A 53
8 Prioritise your projects.. Project selection and Projects or Proof of Concepts are plotted based on Business Prioritisation Plot them in a 2 x 2 Value and Ease of Implementation Best suited for the 30-60-90 day plan Long List of Projects and Pilots Create our Short List (max. duration 90 days) Business Value / Impact A vs B Notes : Ease of Implementation • Projects or PoCs should be achievable within 90 days • Spread out the ‘wins’ across the period • Not everything should be a technology project; think about stakeholder engagement activities too! C R A F T E D B Y C O N C E N T R A 54
8 Priority setting by crowd-sourcing: A vs B, Crowdoscope Project selection examples and Prioritisation Diverse methods for collecting priorities For more info go to www.silvermanresearch.com For more info go to www.orgvue.com C R A F T E D B Y C O N C E N T R A 55
8 Sample Prioritised List of Projects Project selection and Prioritisation C R A F T E D B Y C O N C E N T R A 56
9 30-60-90 day plan Agree short term roadmap No one left unassigned.. Action Item Business Owner Target Date C R A F T E D B Y C O N C E N T R A 57
9 Follow-up Checklist Agree short term roadmap • Every action assigned • Every person has at least 1 action • Get the agreed actions out quickly • Set up diary items at the 30, 60 and 90 day markers for the workshop attendees • Write up the content, including images of the day then distribute! • Maintain communication and ‘face time’ to keep momentum across the group • Showcase the workshop event and the outcomes as wide as possible (lunch n learns) C R A F T E D B Y C O N C E N T R A 58
HR Analytics in practice - 3 traps - Power of visualisation - Analytics and context C R A F T E D B Y C O N C E N T R A 59
… Discuss the question on your table with your group … Some practical questions: • How complete and how ‘big’ do the data need to be? • Who should be the owner of People Analytics? HR? Finance? Someone else? • How to build a team? Internal vs. external capabilities • Who is the audience for HR Analytics? • What impact can HR Analytics have? • What tools should we use? Existing vs. new tools • What could we stop doing? C R A F T E D B Y C O N C E N T R A 60
There are many traps in HR analytics, that you need to be aware of. But there is too much value here to ignore Common issues include: Common opportunities include: • • Sample size Predictable issues that the organisation prefers to ignore – especially in workforce planning • False correlation • The cost of attrition and absence • Meaningfulness • The link between manager action and staff • Prediction performance • Treating people as mechanisms • Middle data • Differences in performance in role
When performing analysis, keep in mind that data can often be misrepresentative – think hard to avoid jumping to erroneous conclusions Common statistical traps Trap 1 The Ecological Fallacy An ecological fallacy is a logical fallacy in the interpretation of statistical data where inferences about the nature of individuals are deduced from inference for the group to which those individuals belong. Trap 2 Taking correlation for causation A correlation is just a number. It happens to calculate the strength of a linear relationship between variables, but it does not carry any information about causation. Trap 3 Ignoring statistical significance The greater the standard deviation, the less likely you can draw the conclusion. The smaller the sample size, the greater the danger. Be aware that the world is full of wrong and dangerous analysis and statistics C R A F T E D B Y C O N C E N T R A 62
Trap 1 In the 2004 US presidential election, George Bush won the 15 poorest states Bush vote by state in 2004 Conclusion: Poor people vote Republican and the wealthy vote Democrat. Gelman, A, (2008) Red State, Blue State, Rich State, Poor State, Princeton University Press C R A F T E D B Y C O N C E N T R A 63
Trap 1 At the individual level, wealth is positively correlated to tendency to vote Republican Bush vote by state in 2004 Bush vote in 2004 by individual income The Ecological Fallacy Gelman, A, (2008) Red State, Blue State, Rich State, Poor State, Princeton University Press C R A F T E D B Y C O N C E N T R A 64
Trap 1 Are genders paid exactly the same in this organisation? Source: The gender gap and statistical bear traps, Concentra blog http://concentra.co.uk/blog-gender-gap-and-statistical-bear-traps C R A F T E D B Y C O N C E N T R A 65
Trap 1 Within each skill level group women are being paid less than their male equivalents How is this possible? Source: The gender gap and statistical bear traps, Concentra blog http://concentra.co.uk/blog-gender-gap-and-statistical-bear-traps C R A F T E D B Y C O N C E N T R A 66
Trap 1 Aggregating data can hide distributions within the data: here, a high % females in the high skill group masks lower pay for females throughout Simpson's paradox Source: The gender gap and statistical bear traps, Concentra blog http://concentra.co.uk/blog-gender-gap-and-statistical-bear-traps C R A F T E D B Y C O N C E N T R A 67
Trap 2 Do sharks like ice-cream eaters? It is more likely there is a common cause... # of shark attack Correlation Causation See http://www.tylervigen.com/spurious-correlations for more ‘shark - Ice cream’ like examples C R A F T E D B Y C O N C E N T R A 68
Trap 3 When there is large variation within a dataset, it’s harder to tell a story about what is happening to the whole group The risk of using bar charts and the power of the box plot Average performance score Box plots for performance score Large standard deviation C R A F T E D B Y C O N C E N T R A 69
Tips: Simple lines help to find a story within scatter plots Pay Pay Performance Performance Source: "Correlation examples2" by DenisBoigelot, original uploader was Imagecreator - Licensed under CC0 via Commons C R A F T E D B Y C O N C E N T R A 70
Tips: Simple lines help to find a story within scatter plots • Terminate • Training Overpaid / Stars • Monitor action needed Pay Pay • Promote • Pay rise Bargain / Beginners risk of loss Performance Performance Source: "Correlation examples2" by DenisBoigelot, original uploader was Imagecreator - Licensed under CC0 via Commons C R A F T E D B Y C O N C E N T R A 71
Tips: Analyse spans and layers in context Average Spans of Control What if the business is focused on 'Spans and Layers'? Benchmark: Asia offices 1 • Overlay business results: the aims everyone agrees on 2 15 3 10 • Catch people doing things right 4 6 • Explain value of converting managerial overhead to preferred ways to 5 5 invest time 6 7 • Talk about the changes in the ways of working – what to stop – to allow a 7 7 reduced span of control and make the business more effective 8 • What enablers are coming this way that will make the business cheaper to 9 run? Mean: 6.3 Median: 5 C R A F T E D B Y C O N C E N T R A 72
… Discuss: What tips do you have? … C R A F T E D B Y C O N C E N T R A 73
Visualisation Exercise
6. Analyse & share data Find the best ways to look at the data based on your original hypotheses and the stories you are trying to tell Visualisation options Statistical representations Additional analytical elements Plotting a dimension over time Visualising the data early in the process will achieve buy-in and may also start uncovering some basic insights
6. Analyse & share data To generate interesting insights, slice and dice the data and mash-up data How many employees in each function Are more engaged employees better in Are male and female employees are are likely to be here in 5 years? performance? paid equally? Scatter chart showing performance vs. Average current salary by department Chart showing heat map of org by years engagement coloured by absence days coloured by gender to retirement
6. Analyse & share data Visualise to analyse Data table Sunburst coloured by engagement index VS.
6. Analyse & share data Exercise: Each group is given a set of visuals. Look at the questions below and choose the visual(s) that can best answer each question Questions: 1. Are there any opportunities to streamline span of control and layer mix? 1 2. How many high performers (scale 1-10 with 10 being the highest) in certain area (e.g. 2 Birmingham or Head Office) have less than 2 years of tenure? 3. Are there any correlations between large spans of control and performance? And what 3 about Absence days?
Analytics for Change - Modelling organisational systems in multiple-dimensions - Why does it matter? - Practical exercise HR CIPD Workshop 79
…………..But organisational data is not big data Big data methods are rarely required to answer organisational questions Image credit: Shutterstock
HR analytics answers questions about the organisational system Questions ? Customers Links Reports How are customers served? ? Required competencies Responsibilities Processes & Activities Percent time Employee competencies Employees fulfill roles Org structure Competencies Employees & Positions Which Responsibilities Gap analysis Gap analysis ? competencies are core? Goals Strategy ? ? & Objectives What are my key goals? Is right sized? Headcount reports C R A F T E D B Y C O N C E N T R A 81
Organisations need to know if their people are spending their time effectively and on the right activities, in the right quantities Business questions 1. “How do people in the 4. “How much time do we waste organisation spend their time?” on non- value adding activities?” 5. “Where does effort in one 2. “What are the business area influence a most expensive different business area?” activities?” 3. “How many people 6. “Is the current activity are involved in each mix aligned to business activity? effectiveness?” C R A F T E D B Y C O N C E N T R A 82
Process design involves building and analysing As-Is process map, then designing to-be process and structure Fixed Process design steps 1 2 3 4 Link To-Be Work Understand Analyze As-Is Design to To-Be Org As-Is Work Work To-Be Work Structure 1-1 2-1 3-1 4-1 Perform dimensional Link roles to activities Build as-is process maps Define to-be processes analysis (iterations) 1-2 2-2 3-2 4-2 Perform Individual Activity Optimize work and decision Detail to-be positions and Identify the areas of focus Analysis making processes org chart C R A F T E D B Y C O N C E N T R A 83
The most important thing when building these taxonomies is to get the level of detail right – it is easy to define too much detail Example taxonomy tree Depth 1 Depth 2 Depth 3 Depth 4 Depth 5 Depth 6 MANAGE BUY MEAT RADISH WASH FEED PREPARE VEG POTATO SLICE FRIES PEOPLE SERVE SAUCES CARROT PEEL CLEAN McDonald’s Single person Small 10 person canteen restaurant C R A F T E D B Y C O N C E N T R A 84
IAA, Activity mapping & analytics Survey time spent, calculate costs and likely impact 702K Marketing Value Stream 66.4K 183K 220K 232K Review & Develop Report Develop Marketing Define Marketing Plan Marketing Strategy Process Objectives Process Process Process 15K 3K 14.3K 30.2K 20K 27.7K 38K 19.8K Competitive Sales Analyze Gather analysis targets Activity Activity Activity Activity Media Schedule Prioritize mix Target Activity markets audiences Activity regions and – espciallly segments CC + CONS 14K Activity 9.2K 9.2K Activity 44.3K 40.5K 19.5K Post Documented Verify mortem Report ‘compellingness’ objectives 38K Deliverable Activity to content 11.8K Deliverable CC & CONS Metrics Budget Activity Deliverable asset list Activity Develop Messaging end user 0 Activity 15K personas Activity 30K 14K 11K Assets Metrics Deliverable Deliverable 23.4K Approve Partner plan objectives Digital 30.6K development Activity Decision Activity Use case development 29.3K 36.3K Competitive Action Activity positioning Activity 15.5K Change 14K Change marketing plan product? or process? Decision Decision 15.9K PR Stop the work Activity Media 24.6K Communications Plan Customer Deliverable Outsource benefits Consumer Deliverable insight 33.5K development Improve productivity Activity 24.2K Cost Nothing breakdown Deliverable Approve plan 30.2K Decision Approve strategy Decision C R A F T E D B Y C O N C E N T R A 85
Activity mapping & action planning Map activity changes and their effect on the business Marketing Define Marketing Develop Marketing Strategy Develop Marketing Plan Review & Report Objectives Action Target audiences – espciallly CC + Sales targets Schedule Gather CONS Competitive analysis Media mix Analyze Stop the work (2) Outsource (4) Improve (7) Nothing (24) Prioritize markets regions and productivity Verify ‘compellingness’ to content Metrics Report segments Develop end Digital Target audiences Schedule Approve strategy CC & CONS – espciallly CC + Budget asset list Post mortem user personas development Develop end user personas Gather Approve plan CONS Documented objectives Partner plan PR Sales targets Post mortem Partner plan Metrics Prioritize markets Messaging Metrics Media mix Documented Approve objectives regions and Metrics objectives Digital development Assets segments Competitive Competitive positioning analysis Assets Report PR Change product? Use case development Analyze Customer Competitive benefits positioning Verify Media Communications Plan Change marketing plan or Consumer insight development ‘compellingness’ Media Use case process? to content CC & Communications development Cost breakdown Customer benefits CONS Plan Consumer Budget asset list Define Marketing insight Approve plan Approve strategy Objectives development Messaging Develop Cost breakdown Approve Marketing objectives Strategy Change product? Develop Change Marketing Plan marketing plan or Review & Report process? Marketing C R A F T E D B Y C O N C E N T R A 86
The RAD framework Do away with the complexity from other systems such as RACI, RASIC, RAPID Keep it simple Throw out complexity Responsible You are ultimately responsible for ensuring the A* end result or output of the work is achieved. R You understand and manage the delivery and I C approvals required to be able to deliver the end result. Approve A Veto power on a decision and sign off actions Deliver Deliver the work, e.g. provide information, D analysis and other support to person A* In the RACI definition, A stands for Accountable . I = Informs . Needs to be informed of the outcome of the decision or process responsible C = Consulted . Needs to be consulted prior to an outcome/decision being reached Note: In the Data-Driven Design Book, it was the RAS rather than RAD model. S stood for Supporting the person C R A F T E D B Y C O N C E N T R A Responsible in actually doing and delivering the work. This has now been made more explicit with D for Deliver. 87
Experiment and iterate the design by letting people interact with the data Create gamified process cards • Creating process cards helps to physically assign activities to different roles in the organisation • Specific activities can be identified for improvement, outsourcing or can be handed over to different areas of the organisation • Map activities to roles and iterate an organisation seeing how the organisation physically takes shape C R A F T E D B Y C O N C E N T R A 88
Now start to design the roles – assign activities to roles based on who is or will be responsible for doing the relevant work Roles C R A F T E D B Y C O N C E N T R A 89
Pick one role and answer whether or not the role makes sense Sample questions to test whether each role makes sense • Is it doable, is the role over-loaded? • Would it be motivating? • Could you find someone to do it? • Is it one FTE or multiple? • If multiple, then how would you determine the number of FTEs required? • Is the level of the work consistent? • Would the role have sufficient purpose? • What decision-rights and responsibilities does the role entail? • What are the key process outcomes? • What sort of competencies would be required? C R A F T E D B Y C O N C E N T R A 90
The Future of HR Analytics - Five views - Fundamental principles HR CIPD Workshop C R A F T E D B Y C O N C E N T R A 91
View #1. HR Analytics journey will be like playing the piano People often speak about gradually building In practice, the HR Analytics more resembles HR analytical capability... playing the piano <Linear Maturity Model> Very difficult to establish Causation in organizational Prescription data, because of the number of uncontrolled variables Prediction Causation Prediction may be possible, but often you would want intervene – Correlation so you will jump to Prescription, and by intervening, change the “ Description behaviour of the system For our team to be able to do HR Analytics at each end of the keyboard, from descriptive to predictive, not ” seeing it as a series of necessarily sequential steps. Placid Jover, Unilever Hindsight Insight Foresight CIPD HR Analytics conference C R A F T E D B Y C O N C E N T R A 92
View #2. The future will be Predictive Analytics IBM Watson attrition analysis / Visier attrition prediction C R A F T E D B Y C O N C E N T R A Sources: Watson Analytics for HR: Retain your team, IBM Watson Analytics, https://www.youtube.com/watch?v=MUbmmuve1h8 93 Visier Blog, http://www.visier.com/workforce-intelligence-101/why-hr-needs-data-driven-workforce-planning-to-avoid-talent-shortfalls/
Examples of Predictive Analytics • Recruitment : which profiles of employees will be most effective in their jobs? • Demographics • Channels • Education (UtilityCo) • Psychological profiles (Elkjop) • Retention • % probability of departure in next year, forecast date of departure (Watson, Visier) • Attrition as a function of interventions e.g. New Starter Day (Maersk), training & coaching (Starbucks – after 2 years) • Performance • Engagement scores as fn (last year’s engagement) • Performance scores as fn (engagement, last year’s performance, training completion) • Accident rate as fn (engagement) • Engagement scores as fn (action taken) C R A F T E D B Y C O N C E N T R A 94
View #3. The future of HR analytics will be big / external data C R A F T E D B Y C O N C E N T R A 95
View #4. The future of HR analytics will be Real-time analytics Old engagement surveys New engagement tools Example: eBay Europe Fast Feedback • • Fast and agile 50-100 questions (wide range of topics) • 1 year cycle • Focused and targeted 4 – 6 weeks designing survey, 2 – 4 weeks getting responses, • Responsive 3 – 4 weeks analysing results by HR, line managers, execs, Endless action ‘planning’ workshops C R A F T E D B Y C O N C E N T R A Source: The Dinosaurs of Employee Engagement Surveys, Jane Piper, https://www.linkedin.com/pulse/dinosaurs-employee-engagement-surveys-jane-piper?trk=hp-feed-article-title-comment 96
View #5. The future will be driving organisational change in complex systems Strategy Required Roles Competencies Products & Processes & Customer Teams Organisation Competencies Employees Services Activities Actual Employees Competencies Delivery The customer’s demand pulls through all other elements of the system C R A F T E D B Y C O N C E N T R A 97
#5. HR Analytics needed at each stage so all elements work well together Strategy Objectives & Goals RACI Workforce Required Roles planning Competencies Products & Processes & Gap Customer Selection Teams Organisation Services Activities Analysis Actual Employees Right-sizing Competencies Activity- Transition Based Cost Management System Bench- marking Analytics Delivery C R A F T E D B Y C O N C E N T R A 98
If you remember only one slide from today… Start with the business outcomes 1 Align on pain points 2 Build consensus on the priority questions to address 3 Think about ‘credibility and capability’ in your roadmap 4 5 Showcase learnings and benefits to build momentum and buzz C R A F T E D B Y C O N C E N T R A 99
What one thing? C R A F T E D B Y C O N C E N T R A 100
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