Should Entrepreneurs Have a Data Science Background?

Team of young entrepreneurs looking at a laptop computer in a tech startup office.

Being a data scientist has been described as the “sexiest job of the 21st century”. Couped with this, starting companies has gained popularity in recent years, with 2018 being the largest year ever for Venture Capital investment. Data science is relevant to all industries and all levels of a business, which makes foundations of data science ideal for someone interested in starting a company. For the data scientist who already has a background in business or obtains one in graduate school, starting a company is extremely feasible.

Additionally, data science is relevant across all types of companies, so founders with a data scientist background are helpful regardless of the company they’re interested in founding. Even for companies that don’t create a data product, using data tools to drive marketing and sales efforts will be effective in saving the company money. For many business minded people, the foundations of data science can be incredibly helpful.

AI has become one of the most powerful business tools and 69 percent of companies that have used AI report that it improved their traditional analytics methods. AI and machine learning were once tools only available in an academic setting or at companies large enough to store massive data sets. The cost of using AI and machine learning to make predictions has decreased significantly in the past few years. Given that AI and machine learning are no longer academic subjects but rather business-oriented tools to drive a company to succeed, having a founder with a data science background can be extremely promising.

Why AI and machine learning are some of the most important tools for founders

The outlook for data scientists as founders is positive because of their knowledge of AI and machine learning, as well as their data-driven approach to business development. Venture capital funding in AI and machine learning startups nearly doubled from 2016 to 2017, and this trend is expected to continue. As the amount of data stored grows, the need for companies built around this data also grows. Data scientists who become founders are more likely to use data to determine which problems need solving rather than rely on their intuition. For example, data companies in India are overhauling the diagnostic part of the healthcare industry, and founders with a background in data science are more able to see the value in a data-driven solution. Having AI and machine learning knowledge ahead of time enables a founder to anticipate the data needs of a product before it has been created.

Data entrepreneurs can use their knowledge of AI and machine learning to make a difference across many different areas of data science, including predictive analytics, big data, business intelligence, and more. They are more likely to start investing in data collection at the beginning, which is the best time for a startup to work with its data. When data is considered at the creation of a product, it’s more likely that enough data will be collected to use AI and machine learning tools later on to optimize the product’s success. For example, if Google had waited to start collecting data, it’s unlikely that its ad business would have been as robust. Data-driven product decisions are successful, so using data in the initial stages of creating a new company is imperative.

Additionally, data science and entrepreneurship require similar outlooks, which is that the chance of failure is high, but the benefits from success outweigh this. Indeed, 85 percent of data initiatives fail, which means they do not execute on their initial goals, whether those goals were measured in terms of revenue, cost savings, efficiency gains, or more. After just four years, 44 percent of startups shut down. Therefore, being willing to take a risk and explore a new initiative, even in the face of failure, is a quality relevant for both founders and data scientists. In fact, data science and entrepreneurship are both inherently exploratory, so there would be no way to ensure a project’s success before its undertaking. Having a founder who is aware of the risks is extremely important, both in data science and in entrepreneurship, so a data science founder is beneficial.

What should an entrepreneur do if they don’t have a background in data science?

Founding a company requires a data-driven approach, but an entrepreneur who doesn’t have a data science background has options. The foundations of data science are an acquired skill set.

For a non-technical or non-data-driven founder, hiring a technical cofounder can help them both with pitching the company and hiring a team. Often, data analysis is needed even at the pitching stage to convince investors that the idea is worth committing funding towards. Many data scientists who go on to become CEOs cite a data-driven mindset from the beginning as one of their biggest advantages in founding a company. Having one of the founders be able to speak to their data science experience can help with getting early rounds of funding.

Data science founders also have an easier time recruiting a data science team, so if a founder doesn’t have a data science background, hiring a co-founder with one is often e helpful. The demand for data sciences has grown over 100 percent in the last five years, and having a CEO or technical lead with a background in data science can help with hiring prospective candidates. In fact, many companies face a “first data scientist” problem in which the company is aware they need a data scientist, but they’re unfamiliar with the skills necessary in the field, so they’re unable to hire a qualified candidate.

It’s also common for companies to treat data scientists like software engineers if they don’t have an existing data team, and this is not the optimal use of a data scientist’s skill set. If one of the co-founders is already a data scientist, this problem is solved. If a founder doesn’t have a background in data science, they can either find someone who has this background to help them, or they can build out their data science skill-set by completing a data science graduate program.

A data background in the foundations of data science is still beneficial even with a technical co-founder. Many companies led by data scientists bake data science into the foundation of their products. Data scientists as CEOs and entrepreneurs must balance the need for data with the importance of having a clear product vision. Using tools like Google Analytics is recommended at the very first stage of a startup, so a background in data can help. Having the first person working on a product understand the importance of data collection can benefit the company greatly, so a person interested in becoming an entrepreneur may benefit from developing a background in data science. This background helps deepen the analysis that they must do every day in their role.

Examples of founders and co-founders

There are numerous examples of companies founded by technical data scientists, non-technical professionals, or both. To understand the impact of any founder’s background, consider several case studies.

Tech founder

Shashi Upadhyay, the founder of Lattice Engines, has a Ph.D. in physics from Cornell and analyzed massive data sets as a graduate student. Lattice Engines provides big data services for large sales teams, and it delivers real-time data reports to sales representatives who can then optimize the lead generation process. It was founded as a machine learning product, so Upadhyay’s knowledge of AI and machine learning was critical to envisioning what Lattice Engines would be. Lattice Engines baked data into the foundation of its product, and as a result, it was easier for the company to hire data scientists and get the analysis they needed. In just six years, the company has grown to have over 65,000 users in 25 countries. This is a clear example of how a foundation in data analytics can really move the needle and make a business grow.

Non-tech founder

Melody McCloskey, the founder of StyleSeat, a beauty appointment scheduling app, did not have a technical background. This did not stop her from seeking on new learning opportunities. She was aware of the importance of data and understood that she herself didn’t have the skillset to create a data-driven product, so she sought outside help for that work and all the analysis she needed to bring her company to the next level. She cites one of the secrets to her success as networking and seeking out experts in the areas she knew less about, including engineering and data science. In just eight years, she grew her company to over 350,000 beauty professionals in 16,000 cities because she sought out technical expertise early and often.

Non-tech/Tech co-founder

Non-technical founders can create successful tech startups as long as they invest in hiring a technical team or co-founder early. For example, Brian Chesky, the founder of Airbnb, worked as a designer before starting the company. However, he brought on Nathan Blecharcyzk, his former roommate, to be the CTO and co-founder initially, and wound up driving the technical vision of the product. It’s no secret that Airbnb is an extremely successful company, and using data to recommend the homes a user would like is a large part of their success. Bringing on a technical co-founder greatly benefited the company. They were able to see their business opportunities differently with more access to data.

Why entrepreneurs should invest in sharpening their data science skills

Ultimately, for an entrepreneur looking to start a company, data science graduate degree—such as UVA’s online Master’s in Data Science (MSDS)—is the perfect choice to get the data educational science background they need. UVA’s online MSDS program offers online flexibility with its courses for someone whose company is already underway, or is currently working in a different industry. It’s a worthwhile investment for anyone who has already started a company and wants a data science foundation, or for someone looking to start a company in the future. It’s a program that will give you more than the foundations of data science.

The coursework prepares future and current entrepreneurs. UVA’s online MSDS program offers courses in the foundation in AI and data science, as well as advanced courses in subjects like Risk Analysis and the Decision Sciences that would be particularly applicable to an entrepreneur. The program also offers a capstone project, so a prospective entrepreneur would be able to work at an existing startup to understand what it takes to start a company. You will apply what you learn in a valuable way.

UVA’s online MSDS is an extremely well-established program with alumni in many different fields and valuable courses in its offering. Networking is critical for entrepreneurs, and UVA offers them a chance to meet like-minded people to grow their businesses and discover interesting ways to solve the problems they may be facing in their business career.