Turning Can’t to Can – What Data Science Teams Can Learn From “Silent Cal” and a Frog
Calvin Coolidge, the 30th President of the United States, had a reputation as a quiet man and was nicknamed “Silent Cal”. But one of his quotes has stuck with me on my journey to building a Data Science program:
“Nothing in this world can take the place of persistence…Persistence and determination alone are omnipotent”.
Like many other groups, my team is being asked to produce advanced analytics (not just KPI reporting) and through my blog posts I’ve shared many of my recommendations and learnings as we transform our team. We’re far from perfect but we’ve had success because of a few core “virtues,” the first among them being:
- Persistence –There will be setbacks galore and more “can’ts” than you have ever heard: can’t get access, can’t use the data because it’s dirty, can’t house the data because it’s too big, can’t find anyone who knows the data – but don’t ever give up (I love this picture of the frog)! Focus on the quick wins and build on them. This is an iterative process and you build upon failures and successes equally.
Coolidge bashed “Genius” and “Education”, but I’ve found a few other “virtues” that were also key to our transformation from “can’t” to “can:”
- Influence – If you don’t have buy-in at the top, go get it! This shouldn’t be a tough sell and it’s critical to your success. Find an executive or two who are passionate about data-driven decisions.
- Knowledge – Get educated! Data Science is not Business Intelligence. There is a ton of training out there on how to run Data Science projects and how to become a Data Scientist. Find a course that suits your role. The cool part is that the training never ends – as tools/techniques evolve, so will your training needs. Who knows, maybe you will be teaching in the future.
- Resourcefulness – If you can’t access the data, you’re dead in the water. Work with your IT team to ensure you have access to both structured and unstructured data, and the tools you will need to use in your environment (Dev, Test, Production). And, dare I say it, Shadow environments (a vice) are ideal for testing. You control it, and who cares if it goes down? Just don’t get too dependent on it or you could lose days of work due to an outage. Sadly I learned this the hard way.
- Adaptability – You need a different type of team to do Data Science. But you can potentially leverage your existing BI people for some of the roles on a Data Science team, as I’ve described in a prior post. The Data Analyst and Data Engineer roles fit really well for many in my formerly BI team. You may have to get creative on where your funding for this team comes from – and evolving you existing team is a lot easier than asking to build a new one from scratch.
The Data Scientist role is, of course, the toughest one to fill! You really have three options, and I think you should do them in this order if you are just starting out:
1. Rent a Data Scientist
If you rent, bring in a good consulting team with a Data Scientist. You can get a few quick wins under your belt before hiring a Data Scientist of your own. If you hire too soon and aren’t set up so they can be effective, you could lose them. Just remember: consultants are used to analyzing Big Data opportunities, but they will still need actual access to the data. They can help you define what is needed and work through any challenges (resourcefulness!).
2. Hire a Data Scientist
Once you have a few successful advanced analytic projects completed, you can build on your foundation. At this point, you may be ready to hire a Data Scientist. In my experience the longest part of any project is getting the data ready. Using a consultant to help figure out if you have the capability do this is better than frustrating a new hire. You may have some serious IT work on infrastructure required to be effective.
Sometimes just figuring out how to get access to the data is a really painful path. Again, frustrating to a new hire and you can leverage the consultants on best practices. You don’t want a data scientist to be the person who has to figure out this part. It’s not a good use of their time or skills. This is why you should hold off on hiring a full time Data Scientist until you figure out your data. Establish a pipeline of work so you clearly have real advanced analytics or “Data Science” projects ready to go. This is also an effective tool for recruiting them! A well thought-out, complex, challenging hypothesis in need of testing will get their interest.
3. Build a Data Scientist
Building a Data Scientist – without a Data Scientist to teach them – is a stretch in my opinion. This is why you should Rent, Hire, and then Build. Once you have a Data Scientist on your team, you can hire potential Data Scientists that your existing one can mentor. Pretty soon you will have a team of Data Scientists, as these people are really intelligent and will ramp up fast. Having that pipeline of projects is essential. If there isn’t enough well thought-out work, they will get bored and move on – or spin their wheels not creating business value.
If you haven’t already started to build out an advanced analytics capability in your company, you are most likely behind your competition. If you’ve started and are struggling, you are most likely with your peers, with the exception of companies that do this as a business.
This is not easy, which brings me back to “persistence.” That Calvin Coolidge quote resonated with me early in my career and still holds true today. I’m more of a visual guy at heart though: The “Don’t EVER Give Up” frog was a poster I had on my dorm room wall as a young engineering student and a newer version is now on my office wall. It isn’t words of wisdom from a former President, but it re-enforces my will to “keep at it.”