As we mentioned previously, advanced analytics is a team sport. Dozens of skill sets are involved in developing a successful data analytics program. Nevertheless we still hear from seasoned analytics leaders that they have unrealistic expectations about what analytics professionals can achieve on their own.
If you look again at Conway’s Venn diagram of skills and roles and the diagram of data science, you can begin to envision how you could use this concept not only to assess the capabilities of talent you are trying to hire, but also as a framework for thinking about current team member and other employee skill sets. Maybe some of your employees in IT have great programming skills (hacking). Others, such as those in Finance, may have research or analytical presentation skills.
We mentioned before the concept of “T shaped” skills in advanced analytics teams. This is one reason to invest in regularly assessing and developing team member skills. You can assess current employee skills in light of requisite advanced analytics skills and then build individualized development plans to improve their analytics capabilities.
For example, if you needed to add data engineering capabilities to your team, rather than going through a very expensive and time-consuming hiring process, you could instead develop a junior Data Scientist’s data engineering skills. You would assess the current capabilities of this individual across some of the data engineering skills depicted in Step 3 and then put together a personalized training program or curriculum to advance them along the different dimensions.
As for data democratization, a similar approach is valid. While your goal might not necessarily be to turn a business manager into a Data Scientist, you could assess managers for data science skills and then in light of the results develop training in key areas to help them learn to “think like a Data Scientist” and understand how best to collaborate with analytics team members.
QuantHub offers the ability for organizations to assess what skills their current advanced analytics and business talent has and what areas to target for individual development. We believe that this is a critical building block to developing successful analytics teams and capabilities. It allows companies to take a large team of analytics and business professionals and create a “baseline” level of skills to build upon and to inform future human capital management decisions related to analytics.
Remember the role of the CAO we discussed in Step 1? You’ll need many “bridge builders” in the organization to encourage the perception that data is valuable. This will require an investment in personal development and skills training.
Here’s a few ideas/concepts:
- Cross-pollination – This concept was first directly explained to us by the VP of Data Science at Protective Life. Essentially, you put a team with a diverse but complementary set of skills together in the same room and let them drink lots of coffee together. In this way you encourage team members to move out of their comfort zone and get exposed to new ideas and concepts, something that will also help with retention efforts.
- Cross functional collaboration – Closely related to cross-pollination and directly related to organizational structuring is the need to facilitate cross functional collaboration. Designers, marketers, product managers, and engineers all need to work closely with the advanced analytics team. This is one key reason to implement a center of excellence structure.
- Downtime – Data science and analytics professionals are naturally curious and seeking new challenges. They need to learn and grow to feel satisfied in their role. Many are Kagglers and like to compete in data science challenges. Allow team members a bit of free time during the week to seek new challenges and refresh their skills. This will also make them happier and likely result in better retention.
- Cross train/hybrid career paths – Move people from the advanced analytics organization into other analytically grounded areas such as operations management, digital marketing, or customer relationship management. This can be a nice half step away from the pure data science role while still learning new skills and encouraging business knowledge.
- Provide lifelong learning and development opportunities – This can be MOOCs, monthly topic presentations from outside or internal experts, scientific paper discussions, sponsored degree programs, data and analytics conferences and workshops.