One stakeholder to their DS team: “Don’t come up with a recommendation that I don’t understand and don’t agree with.” Develop your background to understand your team’s recommendations.Force Alignment on “Business Impact” Across the Company(This section has a lot of overlap with the “Be Intentional with Your Prioritization Process” section of the data science manager playbook)“Know What’s Possible and What You’re Asking For” fills in your blind spots about what your data science team is capable of, what’s easy, what’s almost impossible..Recognize that you possess asymmetric information as well, you know the business context..To align your data science team with the company, you must fill in the blind spots they have about the value of their work..This must be a stakeholder led conversation; if your teams can’t align on the “business impact” of data science projects, you can’t even attempt to allocate data science resources efficiently..From an SVP of Analytics: “You need to go through the exercise to translate anything into a common currency..You can turn anything into return on investment, the challenge is to weed out bullshit business cases and assigning projects because it’s someone’s turn for data science resources.” Repeating from the data science manager playbook, prioritization can be somewhat political (“It’s a small enough company to know if you’re overusing or underusing data science resources”) to highly political (“Every team we support gets equal access to our time”)..Your company needs to prioritize based on return on investment (in this case, data scientist time)..You must know how to estimate the required data science resources, and provide a true apples-to-apples business impact of the outcome in a way that can be compared to others’ potential projects.Include your data scientists in this conversation..By bringing the DS team into the room during prioritization talks, you can fill in the gaps in their business context, allowing them to source their own projects in the future..The best-functioning teams that I interviewed had this in common: projects could originate in a meritocratic/bubble-up manner, and it was expected of data scientists to suggest things to work on..People in great organizations figure out how to remove each others’ blind spots.Additionally, share the results of past projects with your team..Data scientists can feel like they work hard on a model, implement it successfully, and then: radio silence.. More details
- 7 Data Trends for 2020 (and one non-trend)
- What are Autoencoders? Learn How to Enhance a Blurred Image using an Autoencoder!
- Introducing Databricks Ingest: Easy and Efficient Data Ingestion from Different Sources into Delta Lake
- New Data Ingestion Network for Databricks: The Partner Ecosystem for Applications, Database, and Big Data Integrations into Delta Lake