Reflections on working in retail
When I first switched industries from Healthcare to Retail 4 years ago I thought retail was going to be easy. I’m a frequent customer of the brand, I understand the brand. The data is less complex (no more trying to say hyperbilirubinemia).
While some of my intuition was correct in that I understand the data well, the surprise came in how retail operates. All year long is a prep for Q4 where the money is really made. 3/4 of the year is spent talking about what we need to be successful in Q4 and prioritizing these projects. Then in Q3 we would come to the realization that Q4 was almost upon us and start a mad rush to get projects and changes done. By October the nerves start to settle in: Are we stable enough? Have we done enough? What could possibly happen that we haven’t thought about? By early November we were facing code freeze. Now is the time to sit and watch and hope everything you’ve done throughout the year was good enough.
I thought that working in business intelligence and data would make me immune to code freeze and Q4 stress. However, the business cannot run and make quick decisions without data. Even though our customers wouldn’t see our data warehouse go down, they would be indirectly affected because the buyers might not know inventory levels or marketing won’t know the bidding strategies that are working. To put it succinctly, my customers or users knew where to find me if they didn’t have data.
My team spent countless hours this year tuning our ETL jobs, restructuring data, tweaking settings and coaching users on effective ways to pull data. We were so well prepared for our busy season and it’s been fun to sit back and just watch everything hum. On Black Friday our primary task was to watch our systems to make sure they were performing smoothly. My team was bored because everything was going so well that we started betting on what sales revenues each hour would be. Ironically over the summer I worked with our intern to create an hourly sales prediction model. The person who kept using the prediction model for his bets was the one who won all of our money that day! It made me really happy to see how accurate our model ended up being on such a huge, critical day for the company.
In the end, retail has been a fun, competitive environment to be a part of. I’m glad I had the opportunity to influence by helping transform the company from data silos to a data rich organization that uses data for every decision now. I’ll miss the excitement of the entire company pitching in together to make cyber week successful but I also look forward to having the day off after Thanksgiving in the future.
Originally published at datacheesehead.wordpress.com on December 4, 2015.