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Interview with Head of Genentech People Analytics

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People Analytics Interview

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Interview with Head of Genentech People Analytics

  1. 1. gartner.com/ceb© 2018 Gartner, Inc. and/or its affiliates. All rights reserved. CLC172509 Detail about CEB Inc. and its subsidiaries, now part of Gartner, can be found at gartner.com/ceb-offices. What does your team look like, and what does the day-to-day look like for a team member? My team comprises 14 people (direct and indirect) with a variety of backgrounds. This includes: • Data Science & Engineering Team (4 people) • Client-Facing Strategy & Solutions Team (4 people) • Management/Support (2 people) • HRIT Support (2 people) • Shared Service Center Support (2 people) There is no such thing as a “typical day” at Genentech, but common activities comprise (1) understanding customer needs; (2) code reviews; (3) project planning; (4) survey and/or experiment design; (5) project reviews and readouts; (6) testing & bug fixing; (7) training and change management; (8) providing business solutions. What tools does your team rely on most to do your analyses? We try and avoid anything manual (e.g., PowerPoint, Excel, Tableau) whenever possible, and we strive to create scalable processes that we can automate if it becomes valuable to do so. We use: programming languages (R, Python), web services and databases (APIs, SQL), version control and integration (git, jenkins), visualization (R, Python, Plotly, Tableau, Powerpoint), data forensics (SQL, Instagantt), and surveys (Typeform). Specific to your HR effectiveness work, what metrics do you look at to measure the outcomes you mentioned in the webinar (ability, motivation, and opportunity)? We model motivation from survey data. We first have to define and validate what survey questions effectively represent motivation (and most companies have a place to start if they perform some sort of engagement survey). And then in a perfect world, we can send before and after surveys to these individuals (and their colleagues) who are participating in various HR activities to determine if there is an effect on motivation. Surveying colleagues allows you to have a control group who has not participated in the HR activity, but are likely experiencing many of the same team dynamics as the HR activity participant is. How do you prioritize talent analytics projects that come to your desk at Genentech? We prioritize projects based on a number of business factors. These are some of the items we consistently take into consideration: • Impact (not necessarily measured by dollar value) • Alignment to the broader HR Strategy and PA Strategy • Effort required to execute • Others (project that provides access to new stakeholders or a new line of business, etc.) FAQs on Genentech’sTalent Analytics Innovations CEB Corporate Leadership Council™ Genentech’s Head of People Analytics, Chase Rowbotham, recently joined us for a webinar to discuss how his team has been able to use analytics to improve HR’s effectiveness and the employee experience. See the answers below to the questions most frequently asked by our webinar audience. Company Overview Genentech Industry: Biotechnology Employees: 14,500 Headquarters: South San Fransisco, California, USA
  2. 2. gartner.com/ceb© 2017 Gartner, Inc. and/or its affiliates. All rights reserved. CLC172509 Detail about CEB Inc. and its subsidiaries, now part of Gartner, can be found at gartner.com/ceb-offices. What is your biggest advice for small teams just beginning their talent analytics journey? My biggest advice is to go where there is energy, and you can tell where there is energy based on the level of involvement from and access to the decision maker as well as the amount of money or resources that he/she is committing to the project. You can also gauge potential energy based on how a project aligns to the goals that the decision maker is on the hook to deliver. The most successful people analytics teams start small and quickly obtain a win (it could be something really trivial in hindsight), and with that win, they not only create momentum (and a delighted customer) but also use the win to grow. Having more demand for your services than you can supply is a very good problem to have, and team growth is a likely outcome from this phenomenon. You also never have a second chance to make a first impression, so teams should be ready to deliver from the very beginning. What is your vision of in-house HR analytics given the fact that there are a lot of AI vendors out there in the market? AI vendors can be largely over-hyped. HR data is small (there is just not a lot of it), and successful AI requires lots of data. If you think of areas where AI has generated success (photo recognition, voice recognition, etc), these areas are rich with data, and there is little-to- no loss of nuance when you aggregate the data. As we think about HR, many vendors are aggregating data from different companies in order to solve the challenges of small data. This can work to solve problems that are not company-specific (e.g., parsing a resume into experiences, skills, capabilities, and competencies to separate candidates). However, all other things being equal, a model trained on data from multiple companies will not perform as well as one trained on company-specific data, because there are nuances that are company-specific. So for example, at Genenetch we offer employees a 6- week sabbatical after every 6 years of service. An outcome of this benefit is a slight spike in turnover post-sabbatical. An externally generated model applied to the Genentech population would have no ability to consider this phenomenon. Back to the main point of the question, I think AI vendors will be valuable for HR functions that do not have a robust and mature people analytics practice, and they can certainly be used as a complement to the team in areas that a robust people analytics shop does not want to own and manage. In addition, AI is likely to become common for small non-analytical tasks like scheduling meetings and ordering lunches. And, these AIs will likely be supplied by vendors. But for analytics, I believe AI and machine learning will almost always perform better if trained with data that is specific to your company (i.e., people analytics model performance will beat those from vendors). What advice do you have combining HR data with non-HR people data and collaborating with other internal business analytics groups? This is such a good question, because it is rich with opportunity and challenge. I don’t think you want to start mixing and matching data that sits in different organizations (or outside the company) unless it serves a very specific purpose. To make this question even more complex, I would consider adding information that is external to the company (i.e., some other vendor possesses this potentially relevant and meaningful information). To list a few things that have to be thought through: (1) governance; (2) privacy; (3) platform compatibility; (4) data context and interpretability; (5) automation. So, let’s say that some sales leader comes up to you and wants to understand how team behaviors affect sales performance. This is not an unreasonable question and could be one that very likely finds its way to your desk at some point. Before engaging on this project, I would want to obtain the following: 1. Executive sponsorship (clear decision maker and shared accountability through goal setting or a funding/resource commitment) 2. Clear definition of the opportunity or problem statement 3. Alignment on measuring the opportunity (to understand the baseline and if any subsequent actions deliver their intended effects) 4. Clear understanding of what the needed and accessible data elements are, what they mean, how accurate they are, how often they are updated or changed, and any anomalies that may exist that need to be accounted for 5. Clear timelines with milestones 6. Clear roles and responsibilities Finally, I would want to make sure that whatever analysis is contemplated and ultimately executed is repeatable and automatable so that if it needs to be updated regularly, time can be spent on interpreting data and not gathering data.

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