Creating a data culture in biotech
Data Cultures
Biotech companies know data is important. They need it for every step in technical product development and business operations. Gosh, data is even plastered on billboards along SF's Route 101 (”We, at cool AI company, are a data-driven product helping patients!”). But just because leadership talks about the importance of data doesn't mean the company actually has a data culture. To me, a data culture is:
An organizational mindset where data shapes all decisions. Scalable data pipelines are invested in as mission-critical for a company starting with data schemas, through collection methods to storage, analysis protocols and governance.”
The ROI for a data culture (and more tangibly a data platform) is hard to measure. It’s not like the addition of a new feature on a website that a company can evaluate against changes in daily profit. The initial spend in the data will allow quicker business decisions and accurate product development but not direct or immediate ROI. It’s closer to company’s investing in cybersecurity which also doesn’t translate to ROI but is critical to the company’s future development.
I’ve spent enough time in the data space at different organizations to share some musings on how to build a data culture.
How to build a data culture:
Hire early data leadership that can invest in data strategy at the very beginning of a company’s product development.
Map upstream processes that may give (or are already giving) incomplete or bad data that won’t serve anyone downstream. Data leaders need a seat at the table of the team responsible for designing the upstream process to influence effective data strategy downstream so the data is useable.
Situation #1: You are a B2B biotech company. The legal team is writing their first commercial agreements with customer companies interested in buying your product. There is language in the agreements around how a customer’s data can be used that is highly restrictive to your product development and technically challenging to implement. Before thousands of customers sign these agreements, a data expert can partner with the lawyers to define contract language that is scaleable, agreeable to customers, accelerates your company’s product development and is less costly to technically implement.
Situation #2: When outsourcing a process to a vendor, include data specialists in vendor discussions to ensure technical compatibility with the end-to-end data pipeline before signing agreements. This will ensure there are no technical integration hidden costs after the contract is signed.
Focus early on data access and data trust worthiness across the company.
Constantly communicate to all teams and all levels about the value of data.
Showcase quick data wins to create stakeholder champions.
Be compassionate and build templates for basic data tools like excel and google sheets. Don't take a super principled approach and abolish tools people rely on because they are hard to integrate - instead, create templates that make data integration easier while meeting user needs. Over time you can ween folks off of these.
