Industry buzzwords like data driven, machine learning, and artificial intelligence are regularly thrown around in conversation, and unfortunately, often out-of-context. Lofty titles like Data Scientist are imparted to individuals who have little to no experience with using data to make decisions with an understanding of real ramifications.
I have worked with numerous young data professionals. Many of them are talented data engineers who can build statistical models with leading edge technology, but don’t fully understand the underlying subject matter, raw data, or business domain in which the models operate.
This makes me wonder, is it important that our data scientists/engineers understand the data domains?
When I reflect on my own professional journey, I recall that every exposure to a new data domain was an occasion for me to substantially increase my skills in data analytics. It also made me realize that each master data domain is wide and vastly different from any other. During my career, I had the opportunity to build data solutions in different domains like retail business, finance, software products, media, manufacturing and higher education. Each new data domain I learned had gains in velocity, multiplied insights, and improved accuracy etc.
Not long after I graduated with a computer science degree, I joined the Information Technology division at a reputed financial institution, in the data warehouse team. As part of the training, I worked as a teller, cashier, back office clerk, and various other “duties as assigned” in a bank branch. Reflecting on that period of my career, I now recognize that the specific exposure to those real banking scenarios provided me sufficient exposure to the fundamentals of the banking data domain. I have leveraged that domain knowledge many times, in many ways, in the financial institutions that I worked later.
In the early 2010s, financial institutions were given a mandate to comply with the Bank Secrecy Act and Anti-Money Laundering program (BSA/AML). I was working at a financial institution at the time, leading a project implementing the BSA/AML solution. The initial solution was implemented with very little understanding of data that would support the objectives of the applications. This lack of understanding of program objectives and the data domain created multiple holes in the solutions. Later, I had the occasion to work in an Anti-Money Laundering program with another institution, this time working alongside AML case investigators. That provided me with a unique exposure to the AML data domain; awareness of how investigations happen, real-life scenarios, and a host of patterns that suggest presence of illegal activities. That knowledge, combined with my technical knowledge, put me in a position where I could architect and deliver data to make it successful.
For any data professional, data is a team sport. The better the team, the better the output. As we add members to our data teams, we have to be mindful of how we can ensure our team members grow and thrive. If we act as if buzzwords and lofty titles will get the job done, we do a disservice to our organizations.
While our technology and tools may shift rapidly, the people behind the tools don’t always evolve as quickly. Our universities, parents and teachers do an excellent job familiarizing future data scientists with the basic tools for their profession. However, the most sophisticated tool these data scientists will ever use is the one lodged in their cranial cavity. Like the many things we should be investing in, the mind has the capacity to deliver things that surpass our expectations.
Deliberate immersion into data domains, preferably by an experienced data professional, is mutually beneficial, providing an edge to both employee and organization. Serial immersion hones skills and augments talent. Providing the employees exposure to business models, decision making process etc. will lead to better results.
There are many ways organizations can incorporate this. Usually, a data professional’s career starts with engaging different IT teams and understanding the data received by backend systems. Instead of having employees start from backend systems, organizations can consider putting them in the front, facing customers. They can try to align these resources to interact directly with business data stewards from the beginning by creating opportunities for newcomers to spend at least a week with data entry teams and customer support teams. Having an understanding of how and why date is collected as well as understanding real life use cases with customers broadens domain understating. Altogether, a slight realignment of an organization’s approach to onboarding data professionals can bring better results.
If we neglect to develop our young talent because they have abundant confidence using a specific technology or tool, we leave them prone to being dismissed, sidelined, overwhelmed, and frustrated when information or insight cannot be delivered at the speed of business. Teaching newcomers the basics of data analysis, exposing them to different data domains and business models, and getting them real life experience is what ensures we produce good data professionals.
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