Hiring data experts is notoriously difficult, and many organisations end up recruiting a general data scientist for their analytics needs when data-driven business decisions require much greater specialisation. Here's who you should be hiring.
It seems nearly every company I speak with struggles to recruit good data people (‘data scientists’). Although most companies recognise the importance of analytics for maintaining competitive advantage, they find recruiting data experts notoriously difficult.
Not only are they in short supply, but even seasoned recruiters struggle to specify the skill sets needed and screen the candidate pool to separate the wheat from the chaff.
Don’t get caught in the trap of recruiting for the general ‘data scientist’ role. If you want to make data-driven business decisions and use analytics and machine learning to change your business, you’ll want to hire the following six types of data people.
If you only hire one or two people, they should be what I would call ‘business analysts’. They answer basic but important data questions asked by your business units. These are the people proficient with MS Excel. If they are your only analysts, they should also be proficient with your web analytics system. If they form part of a larger team, including the other five roles below, you’ll likely have them delivering analysis for all your business units (Note that common applications of business analytics really do span all departments. See, for example, chapter 4 of my book Big Data Demystified).
Where should they sit within the organisation? Some companies group them centrally, while some embed them within the business units. Each approach has advantages and disadvantages, but the decentralised model probably occurs more often in small to mid-sized enterprises.
Your company should almost certainly have a data warehouse and probably a Big Data system. You’ll need people to collect your data, structure that warehouse, assure the data pipeline flows smoothly, and prepare data for particular analysis. These tasks are much more time consuming than doing the actual analysis.
This is a specialised skill set, and you’ll regret it later if you don’t have a specialist do it (I’ve seen this tried many times before, and it’s not pretty). Even if you purchase expensive systems, you’ll still need experts on staff.
Your most innovative projects will be done by experts using mathematics, statistics and artificial intelligence to work magic with your data. They are writing the models that predict customer demand, or recommend your next favourite book on Amazon, or understand that now is the right time to offer customers a 15% discount on a vacation package. They are forecasting your future revenue and predicting which customers are going to churn.
Look for people with expertise in statistics, mathematical optimisation, prototyping tools (KNIME, RapidMiner, H20.ai, SAS EnterpriseMiner, Azure ML, Google Cloud ML, etc), and strong coding skills. They should have a strong background in mathematics, usually a degree in math, statistics, computer science, engineering or physics, and they should have experience writing and coding algorithms in a rigorous language such as Java, Scala, R, python, or C/C++. They should preferably be experienced in object-oriented programming. They should have something on their C.V. that demonstrates they are really smart.
If you have a team of analysts, you’ll probably want a dedicated web analyst. Some companies put these people in their marketing department. Customer online behaviour is a very important data source. You can choose from are a broad selection of mature web analytics products, but whichever tool(s) you choose should be managed by a trained specialist who keeps current on developments in web analytics and related technologies (including browser and mobile OS updates).
The web analyst will oversee tagging and ensure effective collection of customer activity on the website and mobile applications. Some web analytics tools can collect data from any connected digital device, not only browsers and apps, and the web analyst can assist with this data consolidation. The web analyst will set up conversion funnels and implement custom tagging and will monitor and address any implementation problems that may arise, such as data errors related to new browser releases.
The web analyst will also be an expert in extracting data, creating segments, and constructing reports using available APIs and interfaces. For this reason, they may be actively involved with A/B testing, data warehousing, marketing analysis, customer segmentation, etc.
Anyone can create mediocre dashboards and repots, but you’ll benefit greatly if you hire or train staff skilled at creating top-notch visuals that can communicate insights clearly and effectively. This is one of the key areas I focus on when I give in-house trainings to junior data scientists.
Effectively communicating analytic results in a visual format requires a mixture of art and science and should be done by people who excel in:
- Selecting the visual most suited to the use case. For example, trends will jump out from graphs much more quickly than from tables, but tables are better for more sequential tasks.
- Selecting the layout and format most appropriate to the data. For example, reports with time series data shown vertically are not intuitive.
- Reducing visual clutter, freeing the recipient to focus on the most important data.
- Selecting shapes and colors that minimise confusion.
On a technical level, the reporting specialists should be comfortable writing database queries to extract data from source systems and they should be trained on your BI tool(s).
Here’s the hardest one. To make data a core part of your business, you’ll need a senior analytics leader who can set the vision, win support, prioritise the roadmap, manage stakeholders, and build and lead an outstanding data team. I’ve written more on this in my article Recruiting a chief data scientist.
Recruiting your data team
One of your key challenges will be to either identify a good analytic recruiting company or to bring your internal recruiter up to speed in this area. Most recruiters I’ve met struggle initially to understand the skill sets required of data experts, a problem complicated by the fact that many candidates misrepresent themselves or use overly-general terms on their CVs. Often during my consulting engagements, I’ve been pulled in by senior leadership and in-house recruiters to help scope roles and screen candidates and to help manage external recruiting firms.
If you go the route of outsourcing your data roles, make sure you screen each individual consultant. If you aren’t screening, expect to get the least qualified people put on your assignment. Most consulting companies simply don’t have enough strong consultants to go around.
Finally, be realistic about your data ambitions and hire people with skills that fit those ambitions. Candidates will be quickly disillusioned if the tasks they are given do not match their expectations.
Pulling your data team together is a challenging task, particularly in today’s labor market, but, done right and combined with proper strategic support from the top, the competitive benefits you’ll reap will make you wonder how you ever managed without such a team.
David Stephenson PhD is an internationally recognised expert in the data science and big data analytics. You can find more information on recruiting a data team in his new book Big Data Demystified: How to use big data, data science and AI to make better business decisions and gain competitive advantage.