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Leveraging The Machine: The Future of People Data Is Now

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Leveraging The Machine: The Future of People Data Is Now

Big data is not only transforming the way we make decisions, but also the way that organizations recruit, manage and develop their people, driving engagement, innovation and productivity to new peaks.

Explore the future of cloud-based human capital management, and demonstrate how what you do today will influence and energize your company for decades to come.

Big data is not only transforming the way we make decisions, but also the way that organizations recruit, manage and develop their people, driving engagement, innovation and productivity to new peaks.

Explore the future of cloud-based human capital management, and demonstrate how what you do today will influence and energize your company for decades to come.

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Leveraging The Machine: The Future of People Data Is Now

  1. 1. Frank Ricciardi Senior Vice President & General Manager, Asia-Pacific 3 May 2017 Leveraging The Machine: Big, Big (People) Data
  2. 2. A little bit about technology… 2
  3. 3. Work has Changed 3 WHO WHAT WHERE WHEN HOW
  4. 4. Talent Management is Changing 4 1995 PROCESS-CENTRIC 2005 INTEGRATED 2017 DATA-DRIVEN
  5. 5. 86% of CEOs expect tech to transform business over the next 5 years 39% believe HR is well-prepared2 Technology Advances Source: 2 PwC Advisory 2015 Cloud Computing (Multi-tenancy) Mobile & Consumerisation (iOS, HTML5) Big Data (Hadoop) 6
  6. 6. The Era of Big Data
  7. 7. Types of Data Analytics 7 What Will Happen? PREDICTIVE What Should I Do? PRESCRIPTIVE Why Did it Happen? DIAGNOSTIC What Happened? DESCRIPTIVE
  8. 8. What is Big Data? Our apps are collecting data all the time. Define Your Question. Use the data to unlock the answer. BIG DATA: The Basics How does it Work? Analysing all this data with machine learning platform.
  9. 9. Dark Data
  10. 10. And NOW it’s finally being used in HR… Predictive Analytics is Everywhere Financial Predicting: Mortgage risk Outcome: Default warning. Boosted profits $600 million Energy Predicting: Prioritise Upkeep Activity Outcome: Saved millions in lost revenue & repair costs Retail Predicting: Product Purchase Outcome: 29% Sales Increase 13
  11. 11. People Analytics Matters... 12 46% of CEOs say they have already implemented dedicated “people analytics” New Image Needed Source: PwC’s Trends in People Analytics 2015 86% of CEOs say that creating or improving “people analytics” is a strategic priority for the next 1-3 years
  12. 12. No longer look at the past and guess about the future. Predict it.
  13. 13. A little bit about Cornerstone… 15
  14. 14. CLIENTS 3,000 USERS 31M+ COUNTRIES 191 LANGUAGES 43 OFFICES 24 Our Global Reach Note: User and client count figures exclude Growth Edition and Cornerstone for Salesforce. San Francisco Mexico City Santa Monica Santa Monica Sao Paulo Madrid Paris London Amsterdam Stockholm Dusseldorf Munich Tel Aviv Bangalore Mumbai Hong Kong Tokyo Sydney Auckland Shanghai As of 3 May, 2017
  15. 15. Our Unified Cloud Platform 17 Recruiting Recruiting Onboarding Campus Recruiting Learning Performance HR LMS Connect Insights Collaborative Learning CyberU Enterprise EPM Succession Compensation View Insights Engage CHR Planning View Benchmark PRODUCTSSUITES Human Capital Management (HCM) Unified Talent Management (UTM) Additional Solutions Edge & Marketplace Cornerstone Enablement (CFS/EXE) Selection
  16. 16. 31M USERS | 191 COUNTRIES | PETABYTES OF DATA Cornerstone has “Massive Data” User  Position  Previous Position  Education  Skills  Certifications  Accomplishments  Languages  Organisation  Preferences Collaboration  Connections  Live Feed (Status)  Feedback  Actions  Snapshot  Teams  Discussions  Badges  Likes  Comments Talent  Transcript  Performance  Skills  Goals  Assessments  Development Plans  Succession  Compensation  Applicants  Applicant Status Organization  Industry  Business Unit  Department  Division  Region  Groups  Hierarchies  Cost Center  Grade  Location
  17. 17. 19
  18. 18. Cornerstone Analytics REPORT Cornerstone Reporting Standard & custom reports embedded with Cornerstone DISCOVER Cornerstone View Highly visual dashboards that can easily slice/ dice talent information PLAN Cornerstone Planning Big data solution for workforce planning PREDICT Cornerstone Insights Predictive analytics for managing talent decisions 7
  19. 19. How Cornerstone Big Data Works 21 A Single Unified System Data Across All Dimensions Talent Management Algorithms Based On 7+ Years of R&D Machine Learning Improves Over Time Predictive Model Take Action
  20. 20. Predicting the Future RECRUITING Making the best match of applicants to jobs and teams What factors lead to more engaged, high performing employees? With whom should people connect? ONBOARDING How can we ramp up new hires more effectively? CONNECT LEARNING How can I recommend the learning most fruitful for the employee? Which pay structures improve performance? PERFORMANCE How do I b the performance review process? How do I detect ineffective managers? COMPENSATION Which factors predict employees with high potential to succeed?SUCCESSION 22
  21. 21. Cornerstone Insights 23 Backed by data science, answer critical workforce questions about how to better develop and foster talent. Use Actionable Dashboards Optimise Talent Processes Simulate Scenarios Get Predictions & Recommendations
  22. 22. Some More Questions We Can Answer… 24 Compliance • Where do the highest risk levels reside & why? • What changes can I make to my content to ensure that employees complete their courses on time? Succession Planning • Which employees are ready for a new role? Career Mobility • What are possible career paths based on current role and career interests? Employee Growth • What is my employee’s potential for new roles & how can I best develop them? Learning & Development • What courses drive the most impactful development? • How can I effectively assign courses & optimise our catalog?
  23. 23. Predictive & Prescriptive Analytics Source(s): 1. http://elearningindustry.com/top-10-e-learning-statistics-for-2014-you-need-to-know Business Benefits DEEPER INSIGHTS DATA-DRIVEN SUGGESTIONS SMARTER DECISIONS COST SAVINGS 12
  24. 24. 26 Impact of Predictive Analytics Source: Bain & Company. 2.5x Healthier leadership pipelines 2x More likely to deliver high-impact recruiting 5xMore likely to have faster decision-making 30% Higher stock market returns than the S&P500
  25. 25. Cornerstone is Leading the Pack 27 Data Science Machine Learning World-Class Expertise Volume & Variety of Data Comprehensive TMS View Unparalleled Assets New Investments Visualization Unified System Data Center Computing Technology Dedicated Analytics Team Data Anonymization
  26. 26. • Continued advancement of “the machine” • Big data services deeply embedded within the suite at the point of need • Benchmarking solutions • Even more prescriptive and intelligent recommendations What’s Next?
  27. 27. Technology Architecture Trends 29 PLATFORM Unification PLATFORM Cloud PLATFORM Data
  28. 28. 31 Strategic H R Analytics =+ Unified Talent Management
  29. 29. Questions?

Notas del editor

  • Talking Points:
    Who – Today we have 4 (or some say 5) generation in the workforce forcing us to rethink our organization structures and models
    What – Roles are changing within organizations; Jobs lost in the recession have either been eliminate or redefined to meet the new needs of the organization; Job expectation are also be redefined
    Where – It’s a global, virtual world; we are no long constrained to our desk to get work done. Work/life balance is blurred and Work is adapting to employees not vice-versa
    When – When we work is also changing. Flex-hours, shared jobs, work-life balance means greater emphasis is being placed on maximizing performance instead of logging hours.
    How – Mobility, and more important the rise of mobile devices, combined with social networking has changed our work models. Employees are now leveraging multiple devices to get work done no matter where they are.

  • Notes:
    1995: Back in 1995, Talent Management was very process centric.
    2005: Integrated – The need for shared information between HR processes drove the need for integrated talent management.
    2016: Data Driven – Today, organizations need to leverage the immense amount of data they have to gain deeper insights to solve for increasing skills gaps and talent shortages, developing future leaders, and improved employee engagement.
  • The technology that makes this possible is a slew of new big data frameworks – processing and storage frameworks. In the last 5-10 years, it has become much cheaper, easier, and faster to look at data in disparate places, and turn it into insight.

    As technology advances, leaders are acknowledging its power. A study by PwC this year surveyed CEOs about their views on technology. 86% of CEOs expect tech to transform business over the next 5 years. 39% of that same group of CEOs believe that HR is well-prepared. Why?

    A lot of HR departments are not technologically sophisticated or even resistant to technology and don’t understand the rapid pace of change in technology. So, what’s the big change now? Big data is transforming the way organizations are going to work and will help you get ahead of everyone else. It’s what is next in leveraging technology to help HR make more-informed talent decisions to drive business value.
  • The different categories of analytics that we utilize here in Cornerstone, how the rest of the worlds has already started using the most advanced kind of these analytics, and how we’re now incorporating them in our product portfolio.

    at Cornerstone we define analytics into 4 specific categories: Descriptive, Diagnostic, Predictive, and Prescriptive.

    These 4 different types of analytics also generally define the maturity model or the maturity spectrum for the use of analytics within the enterprise.

    Definition of the 4 different types of analytics
    Descriptive Analytics: Description of what has already happened (What happened?) Ex: Attrition.
    Diagnostic Analytics: Exploration of meaningful information (Why did it happen?) Ex: Attrition due to seasonal weather.
    Predictive Analytics: Prediction of future outcomes based on historical data (What will happen?) Ex:
    Prescriptive Analytics : Recommendations based on predictive model output (What should I do?)

    Prescriptive data analytics are the most valuable type of analytics because it uses available data to recommend specific actions that could increase the likelihood of a particular outcome occurring.
    , organizations need to leverage the immense amount of data they have to gain deeper insights to solve for increasing skills gaps and talent shortages, developing future leaders, and improved employee engagement.
  • Talking Points:

    1. What is Big Data?
    The basic idea behind the term “Big Data” is that everything we do is increasingly leaving a digital trace (or data), which we can use and analyze. It refers to our ability to make use of the ever-increasing volumes of data. – Bernard Marr, Advanced Performance Institute (UK).

    • Transforms the way we do business and live our lives.
    Benefits are very real, truly remarkable, and only going to grow:

    Better understand and target customers
    Retailers: predict what products will sell
    Telecom: predict if and when a customer might switch carriers
    Optimize business processes
    Retailers: optimize their stock levels
    Supply chains: optimize delivery
    Impact our lives
    Enable us to find new cures
    Better understand and predict disease

    2. How does it work?
    The datafication of our world gives us unprecedented amounts of data. The latest technology (cloud computing, distributed systems together with the latest software and analysis approaches) allow us to leverage data to gain insights and add value. (Barnard Marr)

    First, we are collecting data on different actions in your Cornerstone products, as well as data that other clients have shared with us…
    Data that is generated in your system
    Data that you collect from your employees
    And data that is shared by your fellow customers

    We then analyze this data instantly to determine which of these data are related to the question you want to answer…

    4 keys layers that data has passes through for actionable insight
    Data sources layer – sources of data for your organization
    Data storage layer – where your Big Data lives
    Data processing/analysis layer – Analyzing the data to draw useful insights
    Data output layer – Take action from insights to benefit from them
     
    Define The Question.
    Machine learning allows us to collect massive amounts of data, mine the data, and use evolving algorithms and statistics to uncover unique patterns and insights and make predictions based on these.

    The key is asking the right business question. Then use the data to unlock business value.


  • Dark Data
  • Industry examples of companies that are already using predictive analytics to transform the way they do business.

    The good news is that we now finally moving in to a world where the same analytics can be used in HR and Talent Management.

    Notes:
    Company: Amazon
    PA Application: Product Recommendations
    What’s predicted: What clients are interested in buying.
    What’s done about it: The company reported a 29% sales increase to $12.83 billion during its second fiscal quarter, up from $9.9 billion during the same time last year. A lot of that growth arguably has to do with the way Amazon has integrated recommendations into nearly every part of the purchasing process from product discovery to checkout.

    Company: Chase Bank
    PA Application: Mortgage risk prediction
    What’s predicted: Which customers will prepay and terminate the relationship (refinance with another bank)
    What’s done about it: Mortgages are valued accordingly in order to decide whether to sell them to other banks
    More on the Chase Bank Story: Chase Bank developed and used an algorithm that better predicted future prepayment and default behavior of homeowners. It correctly classified 75% of prepayers and 90% of defaulters. This allowed Chase to reduce risk and boost profits $600 million in one year.

    For example, in the mid-1990s, Chase Bank witnessed a windfall predicting mortgage outcome. By driving millions of transactional decisions with predictions about the future payment behavior of homeowners, Chase bolstered mortgage portfolio management, curtailing risk and boosting profit.

    Organization: Schneider Electric
    PA Application: Equipment Failure
    What’s predicted: Subtle changes in equipment behavior that are often the early warning signs of failure, enabling operations and maintenance personnel to address equipment issues before they become problems that significantly impact operations.
    What’s done about it: Unscheduled downtime can be reduced because personnel receive early warning notifications of developing issues. These advanced analytics solutions can identify problems days, weeks or months before they occur―creating time for personnel to be proactive.

    Predictive analytics software allows for better planning which in turn reduces maintenance costs. Parts can be ordered and shipped without rush and equipment can continue running while the problem is being addressed.

    More background on Schneider’s use:
    A large North American power utility was able to save an estimated $8.9M in avoided costs in one year because of the early warning detection provided by Schneider Electric’s Avantis PRiSM predictive asset analytics software. In one catch, plant engineers were alerted via an email notification from the software that an aging steam turbine experienced a vibration step change. The appropriate personnel verified that a proximity probe and casing vibration had both changed, and further analysis indicated a likely loss of mass in the turbine blade path. They immediately suspected shroud material had been lost, based on the unit’s history. It was determined that the unit could continue to run at a reduced output, under increased observation, until a more convenient and strategic time to bring it offline. Once it was brought offline, a borescope inspection verified missing shroud material and several other segments that were close to liberating. Had this issue not been identified with predictive analytics software, it could have caused immediate unplanned downtime, loss of generation, possible catastrophic failure and danger to personnel. The early warning notification and the following staff action resulted in a potential estimated savings of more than $4 million in lost revenue and repair costs with this one catch alone, in addition to maintaining the safety of the operating engineers.
    For more information, check out our recent article “Optimizing Electric Utility O&M with Predictive Analytics” in Electric Light & Power magazine.

    Resources: http://www.instepsoftware.com/company/instep-software-events/14-instep-software-company “Proactive, predictive maintenance and analytics can save up to 20% per year on maintenance and energy costs.”
    White paper: http://www.schneider-electric.us/documents/buildings/wp-predictive-maintenance.pdf
  • Existing approaches to mining data are highly manual, take months and teams of people to execute, and are weak at identifying what changes will drive the greatest impact. The opportunity here is to harness the power of predictive analytics to quickly make well-grounded, far-reaching decisions that solve important business problems and push employee productivity to the next level.

  • Talking Points:

    We see talent management being an integral part of your HCM strategy be we understand the administrative part is also important. We have built a solution to help you work through the administrative part so you can focus more on your talent strategy.
  • Cloud computing enables our ability to collect the massive amount of data in our system …and now use it to improve our clients’ talent and business results.

    We hold, in total, about -- just over a petabyte of data, of pure database data.

    We have been collecting talent management data for over a decade from all our clients globally across millions of users. We have a massive data set about people and about various aspects of people – their organization, geography, their role, performance history, etc. We’ve been able to aggregate and anonymize this data to be able to glean insights from it.

    One of the advantages about having all of your talent data in one unified system―especially a system as large as Cornerstone that’s shared by thousands of other companies that are also generating data―is that you actually have the data you need to start answering some really important questions…

    Because our system touches every element of the employee lifecycle, there is an opportunity to analyze all this data with a state of the art machine learning platform and use evolving algorithms and statistics to uncover unique patterns and insights and make predictions to help our clients.


  • Utilizing our powerful machine learning technology and massive amounts of data, we are creating four distinct products that will allow client to make smarter, more-informed talent decisions.

    Our analytics product portfolio includes:

    Cornerstone Reporting, our standard and custom reporting tools that are embedded within the core Cornerstone product suite. It is available to all clients.

    Cornerstone View is a new product that we will be introducing in the first half of 2016. It’s a data visualization tool that allows our client executives to slice and dice data with highly customizable, visual dashboards that will help them answer burning questions to help drive your organization forward.

    Cornerstone Planning is another new product that will be introducing in the first of 2016. It is focused on headcount, workforce planning, and forecasting. It will give you the ability to put together a headcount forecast and identify where key competencies, skills or talent gaps exist throughout the enterprise. You can then put together a proactive scenario based workforce plans around those.

    Cornerstone Insights delivers this last, most sophisticated level of analytics with machine learning—the ability to forecast what will happen and help you make even better talent decisions with data-driven recommendations.
  • Here’s an overview of how Insights delivers a course of actions for employees to address critical business questions.

    First, you need a single talent management system that collects and stores all of your data that might be relevant to the question you’re asking. At Cornerstone, we’re ALREADY doing this for you.

    But the other thing you need is a predictive, prescriptive model that can quickly analyze the data, make predictions and recommendations based on that data, and then improve those predictions over time based on the actions you take. And this is the piece we’re adding with Cornerstone Insights.

    We determine which questions TM leaders want to know most and then determine whether we have the right data to answer these questions.


  • There are opportunities to use predictive analytics across the different facets of Talent Management to foresee what may happen in the future and take actions now to improve the outcome.

  • Talking Points:
    Since Cornerstone Insights is backed by powerful data science and machine learning developed for the world HR in mind, organizations can answer critical workforce questions about how to intelligently develop and foster talent.

    Cornerstone Insights offers a variety of actionable dashboards that help organizations:
    Discover actions for optimizing talent processes
    Simulate scenarios to view the impact of these actions and
    Get predictions about and recommendations to address specific business challenges

    Within your organization, you can further look to increase:
    Employee engagement and retention
    Compliance and operational efficiencies
    Leadership development
    Training content effectiveness

    Ultimately with Insights, you can make smarter, more strategic and informed talent decisions that maximize your business results through your people.
  • Talking Points:
    Insights removes the barriers for organizations who want to answer important talent management questions but aren’t quite sure where to start.
    Some of the initial questions we’ve set out to answer include those in compliance, succession planning, and training & development
    Insights includes actionable dashboards for each of these questions
  • why predictive and prescriptive analytics are so important

    Deeper Insights: You’ll get deeper insights into what’s driving performance in your organization and be able to understand the real dynamics behind your business.

    Data-Driven Suggestions: You’ll have better intelligence around potential challenges and opportunities before they happen. Predictive, prescriptive analytics solutions generate actionable predictions and recommendations.

    Smarter Decisions: You’ll be able to make smarter talent decisions that are backed by data.

    Cost Savings: You’ll be able to customize the KPIs to align with and inform your strategy to achieve your strategic workforce and business goals.

    By understanding the impact of future decisions, organizations can drastically improve their decision-making. Companies with the best analytical capabilities leveraging predictive analytics outperform the competition by wide margins.

  • A study by the consultancy Bain & Company found the following results among companies using sophisticated, predictive, big data analytics techniques:

    Their leadership pipelines are 2.5x healthier
    They are 2x as likely to deliver high-impact recruiting solutions
    They are 5x more likely to have faster decision making
    Their stock market returns are 30% higher than the S&P 500

  • Using some of the big-data technologies, the stuff that comes with big data, makes it a lot easier to slice and dice and manage massive amounts of information in a way that you can use it.

    Benchmarking
    Very manual process today, but machine learning can open up the opportunity to let clients really see how they stack up across all the HR metrics vs. others in their space
    We think we can automate the whole benchmarking process and make it just much more effective, more accurate, more timely, more useful, more cost effective

    Utilizing data feeds from other systems
    You don't know what you find until you start to analyze it. But once you connect the dots, sometimes you get interesting results. So of course, the more data you have, the more interesting this can be.
    Using data from outside of the system and correlating that with your employee data:
    Data specific to environment – weather patterns in a particular part of the country, stats about a city (e.g. people are likely to stay longer in jobs where there’s a higher walkability in the city)
    Social media data from LinkedIn or Facebook – looking at activity and social media and correlating that with employee performance
    Travel information, travel of expense, frequency of travel, how you travel – do these things have a bearing on TM





  • Notes:
    1995: Back in 1995, Talent Management was very process centric.
    2005: Integrated – The need for shared information between HR processes drove the need for integrated talent management.
    2016: Data Driven – Today, organizations need to leverage the immense amount of data they have to gain deeper insights to solve for increasing skills gaps and talent shortages, developing future leaders, and improved employee engagement.

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