I recently had the chance to interview Alice Chung, Senior Manager of Field Operations & Information Management at Genentech about understanding the value of data-driven decision making and how to engage the business to meet its needs and overcome resistance.
Alice will be a speaker at Predictive Analytics World for Business in Chicago, June 20-23, 2016. The conference is the leading cross-vendor event for predictive analytics professionals, managers and commercial practitioners, focused on delivering on the promise of data science. PAW Business covers a wide range of business applications across industry sectors, including marketing, credit scoring, insurance, fraud detection, optimization, and more. Join Eric Siegel and APQC when you register today with 15% off code APQC15.
Q. What is the biggest challenge you have faced using text and predictive analytics models to provide insightful guidance for research purposes? How have you addressed that issue?
The biggest challenge for any organization is to understand which questions truly need to be answered and understanding what data (available or not) can support answering those questions. Regardless of how great the model is, if the data doesn’t answer the right questions, or if the data is not a good fit, the effort doesn’t create value.
There are two main parts to the equation: (1) understand what questions need to be answered and (2) know what data you need to answer the questions. When the analytics team runs different kinds of models, the question becomes, “will the data be able to fit into the model?” The analytics team needs to understand not just the question, but also its value or purpose, so it can help determine the fit of the available data or if capturing the information a different way could answer the question more effectively. Based on what the analytics teams knows about data and its applications in modelling, it can provide recommendations on how the data can be used or better captured (what questions are being used) to optimize for business’s needs. To accomplish this aspect of the effort, regular interactions with decision makers through two-way collaborations is crucial.
Q. What approaches have you used to engage skeptics on the value of data-driven decision making?
The best approach to engaging skeptics is to actively engage them in conversations about their fears or concerns to understand their perspective. In some cases, it’s simply distrust or fear of the analysis, while for others it’s really about the adoption or other issues in the organization that the person cannot control. Once the analytics team understands the skeptics’ concerns it can address them through:
- Demystifying analytics—have additional dialogues and coaching to overcome the lack of understanding about what the data and analysis truly mean.
- Proving concrete examples—show the value of the approach by showcasing results or how other teams in the organization are already using the actual models.
Q. What lessons learned or advice would you share with an organization that was trying to become more data-driven?
The best piece of advice for success, according to our experience, is, “to lead by example.” This means that the individuals have to adopt the behaviors they want others to adopt. By actively using data and demonstrating how to make objective decisions will allow others to follow their footsteps, allowing people to relate to see the impact and benefits for them personally as well as for the organization. When organizations focus their change efforts on recommendations or communications about the goals only, the changes won’t occur, however, this is the simplest way for anyone to claim their efforts.
Disclaimer: This interview represents individual opinions and does not necessarily represent the views of Genentech.
Check out the full interview with Alice and learn more about analytics by visiting our analytics expertise page.