3/18/2021
This blog is taken as an excerpt from the Learning Tree eBook, Building Your Data Science Dream Team. Click here to access the full eBook >
Many executives today know that data science is important, but they're not sure where to start, or which projects to start advocating for on the ground level of a data science strategy. There are two major topics that every CEO should read up on and care about. Early investment and understanding of these two facets will pay off in dividend of value later. They are:
- Data Hygiene
- Decision Analysis
Today's data protection regulations can significantly impact the type of data your organization has and how it's managed. Regulations like COPPA, GDPR and CCPA clearly need to be adhered to. If you're in a regulated industry, like health or finance, then there are even more regulations that need to be considered. And If you are in breach of data protection regulations, then it's not a C-Suite problem---it's a CEO problem. You need to act.
If you are still struggling with proper handling of data, there is a good chance you're also struggling to meet the minimum requirement of these regulations. This can lead to a serious data toxicity problem, where you're essentially breaking the law through improper data storage.
To prevent this nightmare, basic hygiene factors have been addressed. And it must be addressed from this perspective: data science isn't a technology issue -- it's a cultural issue. How do you make decisions in your organization? Do you require experiments and evidence before you sign off? If the big decisions don't demand data science, why would the small ones? The C-Suite is responsible creating an environment in which scientific decision-making is the gold standard.
Companies like Chevron have decision analysis as part of their DNA. It just obvious to them that you'd use that kind of process for making your decisions. It's embedded in the culture -- from the top.
The risk of holding too much data should be a C-Suite concern. There's a balance to be made between competing priorities. Data scientists want as much data as possible but holding that data results in reputation risk and increased security costs. The risk vs reward calculation must be performed and acted on.
The simplest privacy policy is "We don't collect any data. Period." So, starting from there, the next question is, "What data do we need to generate business value?" Then it becomes a question of how much value you get vs how much risk you must shoulder to generate that value.
The C-Suite should be looking to promote a culture of respect for data -- its value and its risks. They should be providing the resources for the business units to understand the implications. And, they should be removing any impediments the business units have in making effective use of data. But, the day-to-day work of deriving value from data is probably best conducted at the business unit level. If you have a small team, then sharing data science resources may still be necessary, but the projects should still be owned by the business units.
The Bottom Line: A successful data science program is built on data hygiene best practices and good decision analysis processes -- and it must be championed from the top down.
To learn more about advancing your data science strategy, explore the resources below:
- Get Access to the Full eBook, Building Your Data Science Dream Team >
- Download the Data Science Training Brochure >
- Discover bundled data science learning paths at: LearningTree.com/DSBundles
- Explore the full curriculum at: Learningtree.com/DataScience