Big Data in Human Resources
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The document discusses a big data webinar series hosted by Cronos Big Data Services that focuses on using big data in various industries. It provides an overview of key big data concepts and Cronos' offerings, which include workshops, proofs of concept, and implementations related to big data analytics. The document also outlines how Cronos helps organizations leverage big data to gain insights and make better decisions.
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Big Data in Human Resources
- 2. Big Data Webinar Series
- 3. Big Data Webinar Series Customer Intimacy & Develop New BusinessMarch 11, 2014 Operational ExcellenceMarch 18, 2014 March 25, 2014 April 1, 2014 April 3, 2014 Big Data in Public Services Big Data in Healthcare & Life Sciences Big Data in Human Resources
- 4. What is Big Data?
- 5. What is Big Data? Big Data is not about the Volume, Velocity or Variety of data It’s about collecting data that we never collected before and using it in creative ways to stay competitive and create value for your business
- 6. LinkedIn
- 7. LinkedIn
- 8. Moneyball
- 9. Moneyball The Moneyball approach is a real opportunity for HR Departments today
- 11. HR Themes • Employee retention – what creates high level of engagements and retention? • Accident claims – what factors and which people are likely to create accidents and submit claims? • Leadership pipeline – what are the most succesful leaders and why are some developed and others are not? • Customer retention – what talent factors drive high levels of customer satisfaction and retention? • Candidate pipeline – what is the quality of our candidate pipeline and how do we better attract and select people who we know will succeed in our organization
- 13. Source: Forbes
- 14. Big Data Concept Enterprise Data Hub HR Analytics Core HR Payroll CRMRecruitm.TMTraining Safety Job Boards MediaSurveys Billing Job Boards Interviews
- 15. HR Analytics
- 16. Data Flow Concepts AnalyzeAcquire Organize Decide OLTP Data RDBMS ETL DWH BI
- 17. AnalyzeAcquire Organize Decide New Data OLTP Data Data Flow Concepts RDBMS ETL DWH BI
- 18. Use Case Drives the Data Flows AnalyzeAcquire Organize Decide OLTP Data RDBMS ETL DWH BI New Data
- 19. AnalyzeAcquire Organize Decide New Data OLTP Data RDBMS ETL Use Case Drives the Data Flows
- 20. Big Data Journey Business Defines mandate and requirements IT Acquires and integrates data Data Scientists Build and refine analytic models IT Publishes new Insights Business Consumes insights and measures effectiveness
- 21. Cronos Big Data Services Offering Use Case Discovery Workshop Big Data Analytics Implementation Services Proof of Concepts
- 22. The role of the Data Scientist Business Strategy Analyst Hadoop System Administrator Hadoop Developer Data Architect Data Analyst/ Statistician Identify Business Pains & demonstrate through Analytical skills how the available data can be exploited on a Strategic Level Hadoop Cluster installation & administration. Data Loading Build the relevant dataset by cleansing, filtering, grouping and aggregating the data using parallel processing languages like MapReduce Hive, Pig, Impala, Spark,… ETL tooling, MDM, Data Cleaning & Matching Integration with Enterprise Architecture Data Governance & Security Through statistical Analysis, conceptual and predictive data modeling, machine learning,… Discover patterns, trends, insights. Translate these to Business Opportunities
- 23. The Role of the Data Scientist
- 24. Cronos Big Data References Use Case Discovery Workshop Proof Of Concept Implementation Services Big Data Analytics Call Center Healthcare Utility Editor Telco ISV Media Transport Manufacturing & Distribution Online Gaming
- 25. Clever usage of data to make better decisions Conclusions
- 26. Contact: matthias.vallaey@cronos.be` +32 496 57 66 27
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- Ziepubliek sector
- Ziepubliek sector
- Gestart in 2003 met paarduizendgebruikers, iedere 2 sec. komtereengebruikerbij, in 2014 hebbenze 275M gebruikersToepassingenwordengesofistikeerder. Data volumes wordenalsmaargroter.In de begindagenwerdgebruikgemaakt van conventionele technology.Bvb People you may know: 1x om de 6 wekenupgedate, somscrashte het systeem, het heefteens 6 maandennietgewerktIntroductie van Big Data (Hadoop) technolgieënmaakt het 1) mogelijkomexponentiëledatavolumesaanteverzamelen, maar ookomAlgorithmes op los telaten en semi-realtime updates tekunnenmaken en tepresenteren.
- Gestart in 2003 met paarduizendgebruikers, iedere 2 sec. komtereengebruikerbij, in 2014 hebbenze 275M gebruikersToepassingenwordengesofistikeerder. Data volumes wordenalsmaargroter.In de begindagenwerdgebruikgemaakt van conventionele technology.Bvb People you may know: 1x om de 6 wekenupgedate, somscrashte het systeem, het heefteens 6 maandennietgewerktIntroductie van Big Data (Hadoop) technolgieënmaakt het 1) mogelijkomexponentiëledatavolumesaanteverzamelen, maar ookomAlgorithmes op los telaten en semi-realtime updates tekunnenmaken en tepresenteren.
- “Moneyball: The Art of Winning an Unfair Game” verteld het verhaal van Billy Bean als Manager van de Oackland Athletics Baseball team.Toen Billy in het begin van zijntrainerscarriere de sportieveleiding van het team overnamverkeerde de club in grotefinancieëleproblemen.De Club had geengrotebudgettenterbeschikking o Top spelersaantetrekken en zemoestenookhunduurstespelerslatengaan.Billy gooide het over eenandereboeg en gebruikteanalytische en statistischemethodesomzijn team teheropbouwen.Hijontleede het spel tot in het kleinste detail. Op basis van zijninzichtenselecteerdehijspelers die, door objectieftemeten, voldedenaande vereisten van het spel. Ditleverdesomsverassendenamen op.Hijmoestvechtentegen de conventioneleideeën van directieleden, trainers, scouts en van de spelerszelf. Hij was niet op zoeknaarspelersmet de mooiste slag, of met de grootstepopulariteit. Hijbaseerdezichpuur op cijfers.Billy werdkampioen in 2002. Hijliet teams achterzich die nochtanseendriemaalgroter budget tespenderenhadden.
- Veel vanwatalgemeenwordtgeacepteerdals “Best Practices” in sourcing, recrutering, interviewtechnieken en algemeen Talent Management is gebaseerd op conventioneleWijsheid. Ideeën die algemeenbeschouwdwordenalsWaar.Het probleem met conventionelewijsheden is datdezewijdverspreidzijn, dikwijlsnietwetenschappelijkonderzocht of getest en dusuiteindelijknietnoodzakelijkwaarzijn.Het MoneyBallvoorbeeldbiedtgroteopportuniteitenvoor HR afdelingen.
- Deze slide zalzekerherkenbaarzijnvoor HR. De strategischevisievertaaldzich in hoe eenondernemingzichorganiseert en zichdifferentieert op de markt.Het is de rol van HR om de Workforce teplannen en tealligneren. En ditgaande van Talent acquisitie overPerformance management & Talent calibratie tot Leadership ontwikkeling.Uiteraard met de bedoeling Top Talent aantetrekken en tebehouden en het algemeen engagement en productiviteit van allemedewerkersteverhogen.
- Catalyst IT Services is eengroteAmerikaansetechnologiedienstenverlener. Over de laatstejarenheenhebbenzemeerdan 10.000 kandidatengescreend.Ditzoueenzeertijdsrovende en dureoefeningzijnindienzijgeen Big Data technologie en algorithmeszoudenhebbeningezetomditteverwezenlijken.In het verledenkwam het voordat door de subjectieveinschatting van recruteerders de juiste man of vrouw op de verkeerdeplaatsterechtkwam of omgekeerd.Het Datacrunchingproces is ontwikkeldrondeen online assesment tool.Dezeapplicatieberekenddan hoe eenkandidaatzichzougedragen in eenbepaaldesituatie. In het geval van eenalgemenepositieve score op de assessmentwordt de kandidaatuitgenodigdvooreeneerstegesprek.Uiteraardgaat de computer het werk van een recruiter nietvervangen. De recruiter heeftwelveelbetere en objectieveinformatieterbeschikkingomzijnoordeel optebaseren.Nettoresultaat: Catalyst IT Services heeft de helft minder personeelsverloop in vergelijking met hun sector genoten.
- An Example: Hiring the Best Sales PersonLet me give you an example:One of our clients, a large financial services company, operates under a belief system that employees with good grades who come from highly ranked colleges will make good performers. So their recruitment, selection, and promotion process is based on these academic drivers.Several years ago one of their analysts performed a statistical analysis of sales productivity and turnover. They looked at sales performance over the first two years of a new employee and correlated total performance and retention rates against a variety of demographic factors.What they found was astounding. The results are shown below.What did drive sales performance:An accurate, grammatically correct resumeHaving completed some education from beginning to endHaving successful sales experience in high priced itemsDemonstrated success in some prior jobAbility to work under unstructured conditions.What did NOT matter:Where the candidate went to schoolWhat GPA they hadThe quality of their references.
- Most companies have lots and lots of HR data (employee demographics, performance ratings, talent mobility data, training completed, age, academic history, etc.) but they are in no position to use it.Our newest research on HR systems shows, in fact, that the average large company has more than 10 different HR applications and their core HR system is over 6 years old. So it takes effort and energy to bring this data together and make sense of it.Most importantly of all, there is a real discipline to data analytics. It demands skills in data analysis, cleaning, statistics, visualization, and problem-solving. Most HR professionals do not yet have these skills, so companies have to find these people and bring them together to work on HR data.Step 1: The Use of Anecdotal Evidence—Before HR information management systems and similar tools were widely available, HR departments were forced to make human capital and hiring decisions based on past experiences, opinions and hunches.•Step 2: The Use of Internal Data—As cost-effective computing and data gathering tools became available, HR then began conducting simple “data dumps” and counts to bolster their decisions.•Step 3: The Use of Internal Metrics—Next, HR improved the quality of its decisions by examining the company’s own operational data—e.g., internal sourcing and hiring metrics. This represents the beginning of HR’s shift toward true talent management.•Step 4: The Application of Descriptive Analytics—The next shift HR makes is toward actual data analysis—looking at information (such as attrition rates) and analyzing past events for useful insights on how to make future sourcing and hiring decisions.•Step 5: The Application of Predictive Analytics—Finally, HR uses Big Data, to determine probable future outcomes of human capital decisions, which represents a significant leap in terms of extracting value from dat
- What Will You Walk Away Able to Do?Define Big Data for your organization and pinpoint the business problems big data strategies can solve in your organization to get higher return on employee investment, improved talent analytics, and predictive analytics for human resource functionsBe a strategic member of the executive management team by connecting what you are doing with the organizational business plan Create the required governance structure and get buy-in from key stakeholdersImplement the organizational changes needed to support new processes around data collection, cleansing, and analysis Identify the best tools, process and techniques to solve you’re the business problems you are trying to solve for measurable benefitLearn how to and when to partner with IT to build the right infrastructure required to capture the data and perform the analysisSee first hand examples of predictive analytics and other big data methods that are being used in businesses today
- Big Data wordteenknelpuntberoep
- N-AllooGrabbleAquafinBlondéTelenetNGDataDe PersgroepBlondé/Thermo KingPOC voor Sales in samenwerking met Blondé. Samenbrengen van allesoortendatabronnnen (CRM Sales & Service, ERP, Persberichten, Social Media,…) en dan via Content Mgmt platform van Blondécreëren van gepersonaliseerde “Sales Dashboard”voor de sales.EriksNapoleon Games
- What is Big Data for Human Resources?Big Data in HR refers to the use of the many data sources available to your organization, including those not traditionally thought of in HR; advanced analytic platforms; cloud based services; and visualization tools to evaluate and improve practices including talent acquisition, development, retention, and overall organizational performance. This involves integrating and analyzing internal metrics, external benchmarks, social media data, and government data to deliver a more informed solution to the business problem facing your organization. Using these tools, HR organizations are able to perform analytics and forecasting to make smarter and more accurate decisions, better measure efficiencies and identify management “blind spots”.