Data scientists are no longer ‘nice to have’ and are considered essential across virtually every sector and industry. Yet, Forbes states that less than 50% of businesses have successfully become data driven after a decade of investment.
Why? Because a skills shortage is holding companies back.
It’s perhaps unsurprising that data science and engineering are burgeoning fields with a huge demand surplus. Unfortunately, there are simply not enough professionals to go around, and businesses and organizations are scrambling to find the best talent to push their operations to the next level.
For businesses and organizations looking to become more data-driven, there are two main options:
- Hire in-house teams of data scientists and related practitioners
- Outsource teams of data scientists and related practitioners
This article will explore in-house vs. outsourced data scientists.
In-House Data Scientists
Hiring in-house means employing a dedicated team of data scientists and related practitioners, probably consisting of analysts and data engineers.
In-house hiring is the go-to for larger, more mature businesses, but this is changing. Some 57% of businesses are outsourcing teams to invest more in their core business operations, and these relationships are largely positive.
Nevertheless, hiring in-house provides the long-term solidity and management required to evolve into a data-driven business.
Here are some of the pros and cons of hiring in-house data scientists:
Pros of In-House Data Scientists
- Accountability: In-house teams are accountable and easier to manage. By hiring in-house, a business retains tight control of their teams’ activities, helping focus their efforts and track KPIs from centralized reporting systems. This is a boon in data science, where it’s beneficial to measure impact as part of a business’s internal enterprise resource planning (ERP) suite or analytics dashboard.
- Skills Variation: Assembling teams of in-house data practitioners provides flexibility. In-house teams may handle customization better than outsourced teams. If team members need to utilize new tools or technologies to achieve their goals, the business is likely to pay for what’s needed. Outsourced teams aren’t liable in the same way - they may be resistant to using tools or technologies other than what they are used to.
- Intellectual Property and Ownership: The product of in-house teams is less complex when it comes to matters of ownership and intellectual property. This can be mitigated with contracts when dealing with outsourced teams, but ownership is easier to govern with in-house hiring and project management.
Cons of In-House Data Scientists
- Cost: In-house teams almost invariably cost more. Hiring top talent in-house can be extremely costly, and it’s tough to attract talent away from other jobs or projects on a long-term basis. This problem magnifies further when you’re trying to assemble a whole team. As a result, in-house hiring is almost always more expensive than outsourcing.
- Legal: Legal employment is slow and unagile. In-house hires will need to be found in a competitive environment and attracted with competitive pay and various benefits and perks. This is both costly and legally complex. Also, an employee’s contract may last much longer than it needs to be to complete a project. In-house hiring is cumbersome in this respect.
- Scarcity: It’s super-tough to find talent in today’s competitive market of data scientists. As mentioned, this comes with an array of challenges that only enterprises and larger businesses can typically overcome. But also, this competitive market has led to an influx of relatively inexperienced data practitioners who are charging inflated rates for their experience level and aren’t able to deliver relative value to that experience level.
Outsourced Data Scientists
Outsourcing is extremely popular today, with over half of all businesses outsourcing in some way. Outsourcing ranges from employing freelancers and personal assistants to employing huge teams of customer services representatives. Businesses generally report high success and satisfaction rates from their outsourcing activities.
Here are some of the pros and cons of hiring outsourced data scientists:
Pros of Outsourced Data Scientists
- Cost: Cost is by far the biggest advantage of outsourcing data scientists and is the chief motivator behind some 70% of businesses’ outsourcing choices. Outsourcing allows businesses to access global talent at hyper-competitive rates. In addition, data is somewhat of a universal language - businesses needn’t confine themselves to local talent pools.
- Flexibility: In tandem with diversity and cost is flexibility. Outsourcing can take advantage of shorter-term, more informal relationships that don’t lock either party into an excessively long relationship. This benefits both parties; businesses pay for what they need, and employees are free to move from project to project. Once a project ceases, both the business and outsourced team can simply move onto the next milestone without legal friction.
- Availability of Experts: Outsourcing professional data scientists and analytics services puts businesses in contact with the experts they need. Data science is a diverse field - companies might need front-end or back-end data developers, machine learning specialists, dashboard developers, customer data specialists, tracking specialists, etc. Outsourcing enables businesses to tap into a richer, more diverse talent pool.
Cons of Outsourced Data Scientists
- Control: Data is often sensitive and business or organization-critical. Handing over sensitive assets can be nerve-wracking, and businesses should apply rigorous scrutiny here. While in-house hiring hardly guarantees security, it’s essential to develop contracts and policies for outsourced teams. Moreover, the intellectual property of any product of projects or operations must be signed off to the business by default.
- Difficulty Finding Talent: Outsourcing provides far greater choice than traditional hiring, but that makes it more challenging for businesses to develop a plan for prospecting hires. It’s necessary to locate the required talent online and screen talent to ensure that they have the expertise required for the job. In addition, outsourced employees need to be audited and interviewed thoroughly prior to signing off work, the same as in-house employees.
- Communication: Communicating with in-house teams is simpler than with outsourced remote teams. Luckily, many businesses and organizations already have experience in remote working today - it’s essential to implement remote working procedures and technologies to make things run smoothly.
The Verdict: In-House vs. Outsourced Data Scientists
It might be a cliche, but the verdict depends on your business, project, and work culture. There’s clearly a reason why so many businesses are turning to outsourced teams, particularly in fast-developing fields such as data.
Outsourced data scientists provide the unique mix of knowledge, flexibility, cost, and availability required for modern businesses to access the talent and skills they need.
Stats and surveys report that businesses are generally delighted with their outsourced projects and skills. There’s no doubt that the prevailing wind is blowing toward outsourcing data science services and related roles.
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