Are you an executive who’s trying to break into the data-science space? Are you a project or program manager running a large operation and attempting to figure out how to get more involved in data science? I’m going to provide some insights.
Hi, I’m Peter Nichol, Data Science CIO.
Today, we’re going to focus on what data science is, the typical areas of data science, and how to get involved in untapped opportunities. Let’s get into it.
What is data science? Data science utilizes the scientific method to better understand how to use algorithms and other inferences applied to structured and unstructured data to generate insights. We’re talking about data and trying to get insights from that data.
Conventionally, there are five significant areas in data science:
- Computer science
- Data technology
- Data visualization
- Statistics
- Mathematics
First, we have computer science, the study of computations and information. Second is data technology, which focuses on data that’s derived from machines and humans. Third, data visualization offers the graphical representation of data and other aspects to provide insights. Fourth is statistics, which is a summary of information that typically provides conclusions on data. Fifth, we have mathematics, comprising the study of logic and the reason behind a lot of data-based decisions.
You might be thinking, “I don’t really have a background in dimensionality reduction or P values,” or maybe you can’t recall Bayes’ theorem off the top of your head. Perhaps your sampling techniques aren’t on the bleeding edge of technology innovation. That’s okay. Much of data science has nothing to do with those aspects but is just as critical.
One area of data science that’s rarely discussed is vendor management. When I speak to many world leaders—from those pioneering logistics to those running biotechnology companies—there’s one underlying theme that’s always present: Nobody has the money to hire an army of folks to go out and generate all these new insights. Hiring data scientists and data analytics leaders is expensive. Many companies don’t even have a formal Chief Data Officer. In many cases, data-science resources—even if you do have the funding for them—are difficult to source and hire because they’re niche to specific domains.
Even knowledge-data resources on your teams today likely don’t have the deep expertise required to make sustainable data-analytics transformations. So, what happens? As executives, we have to engage other types of leaders and partner with other companies to improvise these internal capabilities. In short, we contract out capability needs, usually in the form of fixed-price contracts. This model introduces new skills where we have additional gaps.
We require leaders who understand vendor management. Specifically, we need resources with general-vendor and contract-management experience and well as expertise in contract terms, sourcing, and category management. We also need experience contracting and leading the procurement activities of data-specific initiatives. These types of questions might help to determine if you have that necessary experience:
- Do you have experience negotiating vendor contracts?
- Are you comfortable identifying players in a niche domain space; e.g., vendors that do data ingestion and data governance, vendors that integrate with Snowflake or other data-lake solutions, etc.?
- Can you facilitate discussions to help narrow down potential vendors based on limited requirements?
- Do you have experience with data-driven contracts?
- Have you contracted for cloud compute and cloud storage services provided by third-party vendors? Are you comfortable with the terms and conditions of these types of contracts?
- Are you familiar with cloud-based data-transfer costs and approaches to control or limit cost overruns?
- Have you previously drafted a contract for data services or data-enablement initiatives?
We require resources that can internalize and enable organizational capabilities around data science, data insights, or data analytics. If you have a background in vendor management and are curious about data science, explore the untapped area that many leaders don’t want to talk about—vendor management.
Resources that are able to join teams, quickly come up to speed, and build capabilities by leveraging third-party partners are priceless. You might even have experience working with consulting companies or data-specific vendors with deep expertise. This works too.
Typically, executives don’t have the luxury of fat budgets to bring in 20 resources permanently to address data demands. To fulfill the growing asks that are pushing against our data enablement teams, we need to expand our capabilities. Usually, this means creative contracting.
To meet our business partners’ enormous data demands, we need to get creative, strategize, and find data-literate partners. The best tip I can offer is that, if you’re interested in learning more about data science or data-science capabilities, focus on the business’s vendor-management side. This is a crucial shared priority for the CIO and the CFO. Data-vendor management is here to stay and has vast untapped potential.
Hi, I’m Peter Nichol, Data Science CIO. Have a great day!