Why Data Science Part 1
I’m in the first wave of what they call “Digital Natives”. I grew up with Oregon Trail in my kindergarten classroom and programming digital turtle movement with commands in grade school. I was introduced to Excel in middle school, using it for reports and calculations. When I joined Accenture, I learned about vlookups and pivot tables, working alongside experienced business analysts who flew across spreadsheets, writing macros and synthesizing information without ever touching the mouse.
Over the years I learned about queries, applying them in SQL to summarize financial reports at a my first internship – skills that I would refresh while implementing PeopleSoft, running queries to validate system test results as we validated our configuration. These skills allowed us to understand the business and deliver value that would have been impossible otherwise.
In College I learned about Statistics and Econometrics, but never grasped the possible applications for regression analysis in the real world. AtGrinnell I applied this knowledge using arcGIS, looking at Watershed & Ecological data for the state of Iowa. We created maps and analyzed topological data to highlight probable habitats for native plant and animal species. My senior project examined the relationship between proximity to urban areas and housing foreclosure in the 2007 recession – and i’ll write a separate blog post about that in the coming weeks.
Little did I realize that this was a real-life application of cutting edge technique with a globally leading tool-set – at the time, it was just more homework. Looking at what arcGIS has become, with default base maps and living atlases that provide information that simply didn’t exist when I learned the tool, I am blown away by what this represents from a capability perspective.
At Accenture we implemented JDA for a client, a supply chain management software that used historical data to forecast stock out dates and replenish store level inventory just in time to avoid lost sales. By implementing this system, analyzing historic data, and tuning the forecast algorithm, we were able to adjust input values to save our client $60,000,000 in carrying costs. I consider this my most significant professional achievement, an inspiration for what was to come.
At around the same time I became aware of Alteryx – a profound development in my understanding of what was possible in data Analysis. Alteryx was the first tool I saw that enabled analysts to break down a data transformation into a visualized, sequential set of processes that could be examined or modified as needed. I was enamored with the capabilities of this tool, but I was pulled away by a change in project and a change in employer.
It wasn’t until this 2018 that I had my first exposure to Power BI which opened my eyes to the scope of the potential offered by these new tool sets. I started working with the tool and found almost immediately that all of my skills from Excel, SQL, and Statistics translated directly into Power BI. The foundational language of the tool (DAX) is built on the same logic used in Excel, but all of the common functionality can be executed at the click of a button. The foundation for Runway Analytics had been set.