I discussed barriers to Analytics adoption in a recent post, noting that both McKinsey and Gartner cite finding talent as the primary barrier, with Gartner suggesting that companies train existing staff into “citizen data scientists”. I like these recommendations, and would like to add some learnings:
- Divide the “Data Scientist” role into “Data Acquisition and Integration (DAI)”, and “Algorithm Application” roles and Give the DAI role to Business Intelligence (BI) architects. The “big data” technologies (e.g., data clusters and factories) are quickly absorbed by data architects – in fact, most are thrilled with what they discover (solves a lot of historical frustration). They have experience with integration (from years of ETL), and can understand the security and scalability issues necessary to build robust data pipelines. BI is a mature capability – it’s been around for 20 years (Kimball’s “Data Warehousing Toolkit” was published in 1996 – my how time flies!). And it’s still evolving with a lot of new releases in 2016. In fact, BI serves as a platform for analytics – it really needs to be in place before companies start building an analytics competency (e.g., analytics algorithms consume and create new data which are often persisted in BI stores).
Keep in mind though – not every Analytics project needs “big data” technology – from my experience, it’s often not needed. And I wouldn’t recommend scoping it into the first few projects unless there’s a compelling business case.
- Then focus on filling the “Algorithm Application” role. Let’s call this “Analytics Analyst”, and the role can be filled from anywhere, but BI Analysts are a good place to start. It’s important though, to recognize that the roles are very different. BI Analysts build reports, dashboards, and functional models, but not mathematical models (and just to level-set here: Power BI and Tableau are not analytics tools – they’re BI tools). So, BI Analysts will need some math (light calculus and linear algebra) and machine learning (a couple of courses), but with the right mindset a BI Analyst can get there. People in Engineering roles are also a good place to look – many are already using algorithms, understand the math, and the mind shift isn’t as great. People in Accounting and Audit too – they know the corporate data at a business level, and do forensic and abstract modeling. The nice thing about filling the role internally is the domain knowledge of the business is already in place – no small issue. Just open up a req and see what happens…
Filling that key role can get you started. There are caveats:
- Start Small. If possible, start with small projects (e.g. deeper analysis of corporate transactions and forecasting). If the vision is larger and urgent, then you’ll need to bring in consultants. But in that case, the roles discussed above still need to be filled so competencies can be transfered, and continue.
- Mindset. The work really does require a different mindset – for management and analysts. Analytics is about building a deeper understanding, and it often requires long periods of continuous, cumulative thought (which gets lost if interruptions occur). And forensic curiosity – what Julia calls the “scout mentality” (this is may be a little tangential, but I really love this ).
So, most companies should be able to get Analytics started up using internal resources, and build from there. When to tackle the larger, more complex projects depends on the business case.