Data Science, It's a Cultural Thing

'It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.'

- Sherlock Holmes,  A Scandal in Bohemia

I’ve always loved this quote from Sherlock Holmes.  I used to battle this every day (much less now) when I was starting my data analytics journey.  Old assumptions die hard.  Just because it happened before, or we observed a fact, it didn’t mean that was the answer forever.  New data and new facts can change assumptions, and if we’re lucky, change our direction if we can spot it early enough.

Luckily I’m several years into my data journey, and don’t have to press (as much) when I say we need to look at the data.  I believe it’s because I’m at the center of an organization that has successfully endured a cultural shift in regards to understanding the importance of interpreting the data, using it to make good decisions.

I recently came across a study published by IBM's Center for Applied Insights titled: Breakthrough experiments in data science: Practical lessons for success.  I wish they had published this when I began my journey, it might have made it a bit less bumpy.  I’ve decided to look at the main points and give my comments on the practical advice from the researchers. 

1.    Infuse data science into the culture

This wasn’t easy, but I agree is of paramount importance.  Your organization has to come to a point where the trust in the data is essential because they use it to make decisions for their business.  Having the mindset that “how will we capture the data on this new process so we can learn from it?” will take you a long way to success.

2.    Design a data science capability

     

     This seems like a no-brainer, but it is so important to success. How are you capturing data?  What sorts of governance have you applied?  Have you started with the end in mind?  Data is so valuable, and so important to good outcomes, that having a good foundation program capability will set you up for later breakthroughs.

3.    Equip with the right technology

I love spreadsheets.  I cut my analytics teeth using Excel (okay, Lotus 1-2-3) and various add-ins. They work well, but aren’t the best tool in all cases.  They require a lot from the user in order to manipulate the data for the desired outcome.  I’ve found that statistical tools like SPSS and Watson Analytics can do a lot of heavy lifting when trying to analyze data without a lot of (comparatively speaking) work.

4.    Showcase your results

Who doesn’t like to say “I told you so?”  Maybe not the best attitude to win over converts, but you get the idea.  There are so many anecdotal examples that companies run for long times on that it becomes something not to question.  One thing I’ve learned is that big prizes are to be won when we question orthodoxy.  What better way to do it than with data?  People don’t know what they don’t know.  It’s the data scientist’s job to do just that.

 Link to Breakthrough experiments in data science: Practical lessons for success