How to Add Data Science Expertise to Your Organization

Like most everything in life, business, or your morning trip to the coffee shop, data science requires making choices. One of the earliest and most important is deciding how to bring data science expertise into your organization. You want to harness data science to make your business smarter, innovative and more competitive. How will you do it?

Will your business leverage a big, name-brand consultancy? Is a specialized data science firm the better path? Is it time to build an in-house team? Each of these three options offer distinct advantages and challenges to consider. This short, simple guide will overview the pros and cons of in-house, big-brand and specialized data science resources.  

The In-house Data Science Option

Is it best to hire and train a team of data science experts? As you’ll see, the financial benefits are tempting, but timing and recruiting present challenges:

The Pros

• Proprietary Data Science Practices — In-house data science teams embraced by the company culture experience greater access and influence across the organization. When an in-house team advances data science approaches and practices, the entire organization benefits from greater collaboration and outcomes.
• Integrated effort — In-house data science teams are, by design, working directly alongside other members of your organization. As a result, their goals are more naturally aligned with the organizations. As an added benefit, knowledge transfer is markedly easier when you’re sitting next to someone on an ongoing basis.
• Lower Cost for Some — For businesses with diverse and ongoing data science needs, hiring an in-house resource or team can potentially be more cost effective. Retaining dedicated resources that build on company and process knowledge eliminates the expense of selecting, educating and integrating external partners.

The Cons

• Recruitment Challenges — Recruiting data science talent is not easy. It’s a new field and few recruiters have experience qualifying data science professionals or understanding the skills, knowledge and background that make a true data scientist. Does the business need someone with a background in machine learning or statistics? Knowledge of multi-level modeling or experimental design? Multi-lingual programming capabilities or simply a python guru?  And beyond those considerations, there is the challenge of finding a data scientist who has both excellent business sense and extensive data expertise.

• Hiring Process — A business must take into account the time and resources necessary to recruit, hire and integrate a data science team. Finding first-rate data scientists in today’s market when demand is high and supplies are low can be arduous. Businesses can lose competitive advantage during the months spent finding and hiring highly sought after data science professionals.

• Innovation Stagnation — When it comes to data science, businesses want big innovation and game-changing ideas. However, big company processes and corporate bureaucracy can drain the creativity right out of innovative thinking. For some organizations, hiring data scientists in-house might be a step away from, rather than toward, innovation.

The Big Brand Consulting Option

There is a lot of confidence that comes with choosing the resources of a big, globally recognized consulting firm. It’s a known—but pricey—quantity:

The Pros

• Deep Resources — Large, global consulting firms offer access to extensive resources. The experience and reach of a big firm can bolster your data science ambitions. Large firms also offer teams with depth. If one consultant leaves, there is a skilled, experienced and highly educated replacement at the ready to step in and maintain the momentum on your project.
• Less Risk — Large firms are known for their proven approaches to solution delivery and the proprietary methodologies they use. Tapping into solutions and processes built on successful track records reduces the risk of mistake and/or failure.

The Cons

• Big Costs — Big consulting means big spending. Be prepared to dig deep into your pockets to cover the cost of the Pros noted above.
• Rigidity — While proven paths and known methodologies can aid the efficiency of a project, they can also limit creativity and spontaneity. If a consulting firm gets caught in a conveyor belt approach to data science, critical elements that fuel innovation—improvisation and ingenuity—can be lost in the ultimate need to sell a product or the next big project.
• Less-than-Premium — There can be strong hierarchies in place. Unless you are one of the firm’s very top clients, big brand consulting firms might not give you the attention the company gives to its biggest and most lucrative clients.

The Boutique Data Science Firm Option

Data science expertise is definitely in high demand, and a lot of experienced talent flocks to smaller, often more specialized firms. Can going boutique box you in or expand your business opportunities?  

The Pros

• Personal and Collaborative — With smaller firms that specialize in data science, you benefit from focused attention and the chance to collaborate actively with the team of data scientists building your solution.
• The Bleeding Edge — By virtue of being helmed by highly experienced data scientists, these smaller, specialized firms are often better at keeping up-to-date with the latest algorithmic advances and problem solving disciplines.
• The Ingenuity Factor — Smaller firms are better at improvisation and adaptation. Where big firms may bring processes and proprietary methodologies locked in convention, a specialized data science firm can be more flexible. They have greater freedom to go “off book” and adjust a solution to reveal the data insights your business needs.  (If you follow our NBA and NFL themed data posts, you know some of us are into sports. If you do the same, we highly recommend this Radiolab episode called “Games,” in which they discuss the value of improvisation and define what it means to go “off book” in that world).

The Cons

• Higher Risk — At a smaller firm, risks are higher simply because resources are fewer. If a member of your data science team leaves, they take their knowledge and experience with them, which is hard and time-consuming to replace.
• More Resources Required — Whereas big consultancies can access their own offshore software engineers and QA teams to build end-to-end enterprise solutions, smaller firms may be limited in how far they can bring a solution without tapping into third-party resources.

You Know Your Business Best: Choose Wisely

In the race to conquer all things data, it’s important to be selective. We firmly believe that data science, when done well, can add great value to any business. Your best bet for getting the most from data science is to carefully decide which resource option works best for your unique business, industry, and objectives.