You can bring external resources to supplement your in-house staff or you can outsource entire projects or services.
Outsourcing projects facilitates agile development and allows you to focus on your core strengths. In terms of agility, outsourcing allows you to quickly secure very specific expertise in technologies and data science applications. A third party may be able to start work on a project within a few days or weeks, rather than the several months sometimes needed for internal resources that would need to be re-allocated or recruited (both of which are difficult for proofs-of-concept).
Owing to their specialized experience, a small team of externals might complete a proof of concept within a few weeks, whereas an internal team without
comparable experience could easily take several months and would be more
likely to fail. This tremendous boost in speed allows you to quickly determine which analytic initiatives bring value and to start benefiting as soon as possible.
The daily cost of external resources may be several times higher than internal salaries, but when you consider the difference in development time, they may well be more cost-effective. When you move the technology from proof of
concept to production, you will want to move the expertise in-house but will then have the business case to support the long-term investment.
Many organizations hire externals to supplement in-house staff, putting externals within their internal teams. Supplementing staff with externals serves three purposes.
1. It provides quick access to otherwise difficult-to-hire talent.
2. It gives you the flexibility to cut headcount when necessary (this is particularly valuable in countries with strong labour laws, such as within Europe).
3. It impacts your financials, lowering headcount and providing options to move OpEx to CapEx, both of which may be interesting for investors.
Keep in mind
Bringing in external experts may be the best way to jump-start a project or do a proof of concept.
A word of caution on outsourcing: it can be quite difficult to find high-quality data science consultants. Quality varies significantly even within the same company. Since your projects will by nature be R&D efforts, there is always a chance they will result in little or no tangible benefit, regardless of the strength of the analyst. Thus, it is especially important to maximize your odds of success by bringing in the right people. If possible, look for boutique consulting firms, where the company owners are involved in monitoring each project.
In the end, if you’ve managed to assemble a strong internal team and a reliable set of externals to call on when needed, you’ve probably done better than most of your peers.
For small companies
If you are leading a smaller company or working alone, you probably won’t have the resources or the requirements for a full data team. With only a few end users, you won’t be as reliant on the skills of specialized data engineers. You also won’t
have enough consumers of reports and dashboards to justify hiring a reporting specialist, and you’ll probably not have the resources to commit to a full machine learning project.
Your ‘minimum viable product’ for a data team in a small company would be to place the web analytics responsibility within your marketing team and to hire an analyst who can cover business analytics and reporting. The minimum skills for this analyst are:
A strong mathematical background, including an understanding of basic statistics.
Database skills, including experience working in SQL (standard query language).
Good communication skills, including the ability to create clear graphs and tables.
The ability to be a thought partner in solving business problems.
Although you typically won’t launch internal machine learning projects, at this stage you can still take advantage of the pay-per-use offerings of some of the larger vendors without needing to understand how they work. Examples include the image and text recognition software of Google Cloud Vision API, Salesforce Einstein and Amazon AI.
Takeaways
The term ‘data scientist’ is too broad to be useful in recruiting.
There are 6–7 key skills you should have in your team for big data and data science projects.
Recruiting analytics leadership is difficult, but important.
Traditional recruiters may lack the expertise to recruit the roles you need.
Consultants can be extremely helpful in starting new initiatives, but carefully check whether they have the relevant skills.
Larger companies are increasingly scaling their analytics talent through acquisitions.
Ask yourself
Which of your recruiters (in-house or external) understand the requirements for each of the seven data roles described in this chapter? If none, start speaking with new agencies.
Who is the most senior person in your organization with a vision for data and analytics? Many companies are appointing C-level leadership in data and
analytics. How would such a role fit within your organization?
If you were to acquire a smaller, specialized company to quickly build your analytics capacities, what would that company look like? Think of location, size and skill set.