You’ve decided to make better use of your data, but what are the roles you need to hire? It’s not just the ‘data scientists’. If you want to make more data-driven business decisions and use analysis and machine learning to change how you operate, you’ll need to hire six types of data people.
There is a lot of important data waiting to be collected and used, but getting it moved efficiently and stored in ways that allow effective usage are no easy jobs. In fact, preparing data for analysis is more time consuming than doing the analysis, and finding qualified staff for these specialize tasks is getting quite difficult. Even if you purchase expensive specialized software, you’ll still lose time and performance if you don’t have specially trained people who
- Have expertise in using multi-purpose ETL tools as well as data manipulation tools for big data (e.g. Pig, Storm, etc.)
- Have expertise in designing data warehouse tables. Depending on your tooling, this may include OLAP cubes, data marts, etc. If the database tables are poorly designed, your reports and data queries may become unusable due to instability and lag.
If you don’t get specialist data engineers, others in your data team will waste time covering this critical but specialized task, with at best mediocre results. I’ve seen it before, and it’s not pretty.
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 beat the world champion at Go, or recommend your next favorite movie on Netflix, or understand that now is the right time to offer the customer a 10% discount on a kitchen toaster. They are forecasting your Q2 revenue figures and predicting confidence intervals for the number of customers you’ll see next weekend.
Look for people with expertise in statistics, mathematical optimization, 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.
Most of the ‘data scientists’ you hire will be what I would call ‘business analysts’. They answer basic but important data questions asked by your business units. They are really proficient at Microsoft Excel.
Where should they sit within the organization? Some companies group them centrally, while some embed them within the business units. Each approach has advantages and disadvantages, but the decentralized model probably occurs more often in small to mid-sized enterprises.
Customer online behavior 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. They will also assist in merging company data with web analytics data, which may be done on the organizations databases or on the web analytics server.
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.
You’ll benefit greatly if you hire or train staff skilled at creating good graphs and tables. This requires a mixture of art and science and should be done by people who excel in
- Selecting the table or graph 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. This is rarely done well.
- Leveraging principles of gestalt and pre-attentive processing
- Selecting shapes and colors that minimize 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).
Data opportunities span the breadth of your business, but you won’t get very far with your data initiative unless you have a senior analytics leader who can set the vision, win support, prioritize 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.
Pulling your data team together is a challenging task, particularly in today’s labor market, but the competitive benefits you’ll eventually reap will make you wonder how you ever managed without such a team.