Even for SMEs, data science has quickly gone from a fringe tool for marketers to a central tool for business strategy. The power and accessibility of training AI models, along with the ubiquity of customer data, have brought down the costs and expanded the insight of using data. Today, it’s growing in all industries, from healthcare and finance to TV production and video games. But, how should businesses be going about driving this data team — in-house data science or with an agency?
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Building an in-house data science team
Developing an internal data science team can take some time, but it has a few advantages that could potentially strengthen your organization’s analytical capabilities. An in-house team ultimately has more knowledge of the business they work for, and its operations, than any outsourced vendor could. As a result, they may have a better understanding over the nuances and potentially even access more data sources.
This deep institutional knowledge can help create more accurate models, as well as exercise greater control over the data analytics projects. A priority based on immediate business needs could be set, but pivot mid-project as requirements change. These changes can be quick, so long as they require a similar scope of resources.
In-house can build strong protection of intellectual property as this sensitive data and proprietary algorithms remain in place. While NDAs and such can be signed with agencies, it may still open them up to the knowledge that could be used, even if indirectly, with competitors.
Limitations of an in-house data science approach
Despite its advantages, there are some issues to overcome. First and foremost, recruitment is extremely challenging and time-consuming. Hiring a single data scientist could take up to a year, costing thousands, not to mention the substantial and ongoing salary. Firing and rehiring can create such an obstacle that companies end up sticking with poor workers.
Plus, if a project suddenly becomes more demanding – maybe it’s reaped an immediate positive return on investment and so greater expansion is desired – there is a long delay. This inflexibility makes it harder to adjust to the changing scope of data science projects.
Plus, when knowledge is held by in-house personnel, it can create dependencies. When said person leaves, the organization may find themselves back at square one or with stunted initiatives.
How data science consultancies deliver value
Data science consultancies offer immediate access to talent. The headache and cost of recruitment disappear, as the company can dip into pre-selected talent right away. Plus, this talent is adaptable — if the project turns to a different, unexpected niche, the agency is better equipped to find the right specialized personnel. And, as the project winds down, workers are reduced, which reduces costs. This is perfect for lull periods when there is a break in data science efforts.
When using a consultancy, knowledge isn’t held by a single employee. It becomes more over-reaching, as the project must exist independently of individuals, which is what drives the aforementioned “modularity” of plugging in different talents to ongoing projects.
Consultants bring a fresh set of eyes on existing inefficiencies, as they have the experience of seeing how hundreds of different firms operate.
Digitalsense is one example of a consulting service that helps businesses extract value from their existing data (which may be more extensive than the business even realizes). It does this with capabilities ranging from descriptive analytics to cutting-edge generative AI solutions.
Metyis is another example of how organizations become empowered to make smarter decisions. Metyis combines domain expertise with proprietary platforms and AI-powered analytics so that revenue growth can be delivered, along with customer satisfaction. Valcon is similar in its predictive analytics, but focuses more on cognitive intelligence and helping operational processes.
Finally, Jumping Rivers is a flexible data science consultancy that works on internal processes, but gets hands-on in its implementation. This consultancy in particular is backed by expert knowledge in statistics with experience across public, private, and even academic sectors.
Which is costlier?
The financial implications of in-house versus outsourced data science are tricky, as both immediate and long-term factors need to be considered. Long-term isn’t the be-all and end-all, because cash flow is the primary concern to SMEs.
Instead, firms should look at their existing (or planned) costs for recruitment, training and payroll to get a picture of what they’re spending. Those with existing data scientists will bear fewer costs, as the initial recruitment is already done.
Next is to understand how steady the work is. If it’s project-based, working with a data science consulting firm can have an end date, and this is much easier to roll down than making employees redundant.
Furthermore, consultancies generally access global talent. This means that firms operating in areas with high data scientist salaries (i.e. the U.S.), the greater the geographical arbitrage opportunity from using a consultancy.
A hybrid approach could be one solution, as a small internal team keeps things ticking, such as routine data collection and building pipelines, while a consultancy could be brought in to help put this data into action.
The choice between agency and in-house data science depends entirely on your unique strategic priorities and resource constraints. Success in either model requires clear objectives and strong data governance, but a hybrid approach can be a pragmatic route.
