People Analytics Basics - Day 3 PDF

CCCCCCCCC DDDDDDDDD People Analytics Basics  CCCCCCCCC DDDDDDDDD Quick Recap  TA Questions FRANCISCO...

CCCCCCCCC DDDDDDDDD People Analytics Basics  CCCCCCCCC DDDDDDDDD Quick Recap  TA Questions FRANCISCO DE VITORIA z How were the majority of tasks in Traditional HR (Before 1980)? What were these tasks? SOOOO What was an important role that appeared after 90's? Enumerate three new concepts that appeared after 2020's? What is the EACH model that Lucy Adams talks about? What's more complex? Diagnostic or Prescriptive Analytics. What provides more value? IndNaex as Udrid, U Data & Data sources Ul Where and How to get the Data? U Technology in People Analytics U Building Employee Trust The importance of Data U FRANCISCO Data is absolutely key to everything we are doing. It is needed in all areas of HR and fuels the insights that help us make better decisions in all aspects of the business. Kathleen Hogan, Chief People Officer at Microsoft  TA The importance of Connections FRANCISCO z Connections unlock the Value of Data. The value of data comes from an organization's ability to understand it in relation to other data. By itself, data offers finite value. When connected, data's value is infinite Dave Packer, Neo4J Classification of Data for People Analytics FRANCISCO DE VITORIA uD Organizations use a combination of these data types to gain insights, make data driven decisions and provide experiences for the employees. Type of Data Data Collection Type “ Structured Win Data or First — Party Data Employee Data Unstructured Declared HR Data Inferred / Derived Business Data Social Data The majority of workforce data is hidden. Digital print, social interaction information pertain to the employee and companies can also infer additional information from the employees' behaviors, activities, abilities, etc. - Showcase Microsoft Graph - By Type of Data nrl' DE VITORIA U STRUCTURED UNSTRUCTURED Employee Demographics Assessments (Behavior, feedback) Employment History Survey comments Education/Skills/Compete Social Media i ncies Training attendance Email Content g Prior Roles & Projects Preference data (Qualitative). Performance Data Collaboration (Ratings, Promotions, etc) Pay progression Employee Experience Bonuses Benefits By Source FRANCISCO DE VITORIA Engagement Experience Performance Productivity Collaboration Business Social Outcomes Network Data Data Efficiency HR Data Recruitment Compensation Onboarding Careers  HR of Employee Data by HR Department FRANCISCO DE VITORIA /gad)udl Learning & Recruiting Performance — Talent Rewards / Onboarding — Transition Employee Inclusion & Employee Profile Development Intelligence Compensation Experience Diversity Courses Applications Feedback Skills (In Demand,. Transition Employee Emerging, Base Salary Joiners Records Records Listening Gender Demography Obsolete) Audience Requisitions Priorities specialization Variable Pay and Joiners Feedback Transition Passive Listening Race & Ethnicity Role Bonuses Feedback Data Spend Pipeline Output Proficiency Salary Structure Age Position Attend, Off El t. Si |.. endance ers ngagemen Competitors Market Data e*ua. Historical Info Orientation Fvaluation Hires Strengths H\stor\ca.\ Disability Status Preferences Information Vendors Spend Historical Info Employee. Social Preferences Planning Behaviours Certifications Development Goals By Data Collection Type TA FRANCISCO DE VITORIA z Win data or First-Party Data - ??? Declared Data - ??? Inferred Data / Derived - ??? 11  TE FRANCISCO IIIIIIIII Contest By Data Collection Type TA FRANCISCO Win data or First-Party Data DE VITORIA Information collected directly from your own sources or interactions with the z employees. Originated from your own apps or interactions. Is considered the highly accurate and reliable. Ex: Employee Records, Payroll records, Learning Records Declared Data - Information that employees share with the company through forms, surveys or other collection methods. Originated from employees providing the data. It is quite accurate since provided directly from employees. Ex: Employee Surveys, Employee self-service websites, performance goals, etc.??? Inferred Data / Derived — Generated based on existing data and patterns making predictions or assumptions. Derived from analyzing other data sources, applying algorithms, etc. Accuracy can vary and depends on the source data and the algorithms and models used for inference. EX: Engagement Score, Turnover Prediction, Skill gaps, Interests, etc. 13  TA FRANCISCO DE VITORIA U Technology in People Analytics  Technology as One of the Nine Dimensions for Excellence in People Analytics Nine Dimensions for Excellence in People Analytics As defined by David Green in the Reference Book Technology consists of all types of analytics technology needed for successful people Stakeholder analytics. In particular, it outlines the topic of Management ‘Build vs Buy”, using technology to scale analytics solutions and emerging technologies to accelerate data gathering, analysis, insights and the democratization of data. RESOURCE> my future @ Source: Reference Book- Excellence in People Analytics People Analytics Technologies As the People Analytics field has grown, HR Technology companies have started developing People Analytics solutions. Three waves of People Analytics Technology Adoption: = MN * ENE Loee o búrningglass _ SAP SuccessFactors () A cru nch r Q INSTITUTE 99 Worklytics workday. visier 4\ PeakonTalentNeuron First Wave: core HR Second Wave: Aggregator for Three Wave: People analytics Analytics dashboards Technology * CHRO preference * — Specialized technologies for * Providing dashboards * Could do anything? specific Use Case. * Aggregating data from different * Isagoodstartasa * Workforce Planning sources “Source of truth” * Talent Market Intelligence * Powerful visualization * — Relationship Analytics and Lack of Quality Data Models. Increased cost as an add-in. ONA Incomplete Story. * Employee Engagement and Could just be seen as providing “soft Lack of Analytics listening. benefits” Capabilities. Need to have a big use case behind. Lack of HR Capability and Culture to Complex to Adapt. Be mindful of privacy and ethics. absorbed. Could also be CUSTOM BUILT (Genome) Technology Landscape for People Analytics Figure 7: People Analytics Tech Market Solution Matrix Data Integrator Analytics https://redthreadresearch.com/wp- content/uploads/2020/12/RedThre ad PAT2020 Final-1.pdf :‘) Humanyze G sonere TA 1: 0- n E questback ; sishjeuy snonupuo) SAP SuccessFactors() Worklytics » Syntell 5 H B Qearsite engage &\ PeakonC Culture Amp £ Reflektive e ennova* w ol DA.,;Q S ; Data Creator Analytics  Buy vs Build [ FRANCISCO DE VITORIA Recommended to Buy People Analytics Technology when: V) * Lackof existing solution: The technology available will not help analyze a clearly defined and highly prioritized Business Problem. * Technology experts are not available: * Time is limited * Recommended to do an initial Pilot before committing Building People Analytics Technology: * Use Cases are Complex. An assessment of “desired use cases” vs “vendor solution use case covered” could be done to see the fit for purpose. * |fthere is a long-term corporate strategy maybe it is better to build the technical infra “in-house”. * An investment case could be carried out to confirm if there is a greater ROI if the technology is built in house and it will deliver a better, faster, more cost- effective solution with greater employee benefits. Buy/Build Combination is maybe the perfect fit specially in Big Companies. Source: Reference Book- Excellence in People Analytics  TA FRANCISCO DE VITORIA U Building Employee Trust  TA FRANCISCO DE VITORIA U Would you be open to provide your personal data to your company???? What do you think the company should do for you to provide your personal data?  David Green E* - siguiendo e Co-Author of Excellence in People Analytics | People Analytics leader | Director, Insight222 & myHRfuture.com... FRANCISCO 1 día - O DE VITORIA Building Employee = What are the key steps in unlocking employee trust with the | use of workforce aata ana eople ana yElcs ? Trust to get According to a recent study by Deloitte (see link in comments), trustworthy companies outperform the S&P500 by 30-50%. In this week's episode of the Digital HR Leaders podcast, Yves Van Durme, Global Human Capital Leader of Employee Data Organisational Transformation at Deloitte, and | discuss how to build workforce trust with the use of people data, and how this can generate value for individual workers, teams, organisations and society. In our conversation, Yves also provides guidance on another closely related topic: how to prepare your workforce data for the EU Corporate Sustainability Reporting Directive (CSRD). “(CSRD requires companies to take) action on things like gender pay gap, diversity, work-life balance, so quite a mixture of different topics. | think it make's (CSRD) very interesting in terms of (the) impact it can have on organisations and on the people side as well." You can listen to the episode here: https://Inkd.in/ 21 eNbX9chb7  Building Employee Trust FRANCISCO DE VITORIA The four principles of responsibility A key to unlocking shared value? Building workforce trust by activating principles of responsibility in quantified organization initiatives Measure the right things Share responsibility, share value Practice transparency and privacy Give workers agency and control Reguiarly evaluate inks between metrics Give to get: Creating value for workers Guard worker data- ensuring Make E opt-ir granting workers and outtomes. measuring with their data data s secure and that it is aggregated agency and consent in participating what is intended and ensuring accurate and anonymized in data collection {;_5?. 8‘ cause and effect reasoning Es = 06 yY - Focus on development and growth Empower workers with data providing supportive feedbackto Transparency first: being open Continualiy auda for bias: using facilitate learning and ciear with workers about management toois: enabling workers new advances to ensure fairness 9 @ data collection and use @@ Share governance horzontally and vertically distributing governance Co-creste practices wih workers: and responsibilty across the csuite involving workers from the start in and down to individual workers creating the data initiatives themselves 06 00 Snare cunenstip of data: enabling some data to be portable, going with the worker 22  Steps to build Employees Trust and get Employee Data @ Communicating the Benefits and the Value for the Employee and for the Organization. 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Locate— Where do we find the desired talent (externally / internally) for the need? Deploy Select- How do we assess, select and hire the right Onboard Enable talent? JOIN Onboard- How do we provide talent a positive onboarding experience that also increases ONBOARD Measure effectiveness? Select STABLISH Deploy- How can we place the right people to the right CONNECT roles at the right time? Locate PERFORM'. Network Enable— How can we enable talent to perform at their ATTRACT best in their current role? RECRUIT Define HIRE Employee Life Measure— How do we measure workforce performance and increase talent performance? Cycle Network— What channels should be leveraged to ensure our workforce are properly connected? Engage Engage— How can we enable talent to perform at their Transition LEAVE DEVELOP best in their current role? Develop— What opportunities can we provide for talent Develop to develop, coach and grow? Succession Succession— How can we prepare a talent succession pipeline to meet business objectives? Transition- How can we create a seamless and pleasant experience for people leaving the organization? 29  TA FRANCISCO Practice of the Day Continue with figures & data ' TA DE VITORIA ) N y Y As People Analytics Lead in your effort to provide data to HR Director you = continue to analyze data provided to answer additional questions... Y Let's continue to get insights!  Continue with figures & data ’ TA DE VITORIA A, v Quest|on N , Are we a young company? Avg Age by Job and Store as well as total. * Whatis the “Tenure” of the organization (Avg length of = service) by Job and Store * Could you provide any insight on terminated data? Remember this will be presented to the CEO!!! v Steps Have a look to the provided data for the company * Different fields and meaning that this could have * Think on how you can provide the desired data * Continue to add info to the dashboard * REMEMBER TO SAVE since we will continue to work on the dashboard and UPLOAD a screenshot to the Task. ***LENGTH of SERVICE & AGE could be calculated with the info in the file U FRANCISCO DE VITORIA