Last month, I shared my thoughts on moving from Chief Data Officer to Chief Ecommerce Officer. Now eight weeks in, I have roughly completed half of my first 100 days in the role. While many books have been written on what one should achieve during this period, I have found it more interesting to reflect on my expectations of the move, compared with the day-to-day reality. As they say, the gap between theory and practice is greater in practice than in theory.
Here are five data-related lessons I have been confronted with.
Knowing what questions to ask is difficult. Even with over a decade of data expertise, I have found it challenging to prioritise what questions should be answered with data. I am lucky to have large amounts of data and tens of data experts to call upon, yet those are secondary to identifying the big questions. Listening is part of the solution; I wrote about this when I started my last job. Still, narrowing down your list to the three top questions is hard.
Decisions need to be made with partial data. When you are in a data role, you often have the ability to tune a product until you are satisfied. In a commercial role, where you are beholden to customer trends and trade calendars, you have a lot less agency. You are forced to make decisions with limited time and information. It is comforting that this apparently holds true whether one is running a commercial team or the world's largest economy.
Getting reporting right really does matter. As a data leader, reporting and business intelligence are some of the least exciting areas to explore. They are often finicky projects and nowhere near as satisfying as those involving statistics or machine learning. However, without great reporting it is difficult to align on a single source of the truth. Automated reporting also frees up time as people no longer need to manually compile spreadsheets and charts.
Fast, Cheap, Accurate. Choose two. In a growing business, every data demand is pressing. However, trying to ensure timely answers while balancing investment entails realising that you cannot have everything. You can have accuracy, but it will either take time or require significant resources. I am finding that developing a greater tolerance for directional data is very important to prevent our teams from disappearing down the rabbit hole.
Finding data talent with commercial nous is not easy. There has been a lot of talk of analytics translators and data transformation over the last years. Quite frankly, at a certain point as a data leader one becomes a bit blase about these topics. However, in my new role I fully appreciate how important these people are. Without this type of talent no organisation can unlock the full potential of their data. If you are one of these rare talents: we are hiring!
As I was writing this article, I realised I would likely be facing a similar number of challenges had my career taken me in the opposite direction. In other words, there are important misconceptions between data and commercial leaders. Resolving these issues requires trust and transparency, but are critical to accelerate any organisation's journey to become a truly data-driven one. Stay tuned for part two, after I complete the second half of my hundred days!