Q Ethan McCallum
Dinner conversation turns into a career retrospective. Food for thought for leaders and leaders-to-be.
Toss Bhudvanbhen co-authored this post.
Over a recent dinner, my conversation with Toss Bhudvanbhen meandered into discussion of how much our jobs had changed since we entered the workforce. We started during the Dot-Com era. Technology was a relatively young field then (frankly, it still is) so there wasn’t a well-trodden career path. We just went with the flow.
Over time our titles changed from “software developer,” to “senior developer,” to “application architect,” and so on, until one day we realized that we were writing less code but sending more e-mails. Attending fewer code reviews but more meetings. Less worried about how to implement a solution, but more concerned with defining the problem and why it needed to be solved. We had somehow taken on leadership roles.
We’ve stuck with it. Toss now works as a principal consultant at Pariveda Solutions and my consulting work focuses on strategic matters around data and technology.
The thing is, we were never formally trained as management. We just learned along the way. What helped was that we’d worked with some amazing leaders, people who set great examples for us and recognized our ability to understand the bigger picture.
Like so many of my projects, my latest O’Reilly Radar report was born out of a random conversation. The kernel that grew into Business Models for the Data Economy began as casual chat with my coathor, Ken Gleason. We often examine business models as a kind of gedanken exercise, and that day our attention drifted to data. It struck us as odd: data is seen as the new gold rush, yet it seemed many business models in that arena focus solely on analysis.
Granted, the attention to analysis is well-deserved. Turning raw data into actionable insight can lead to improved business decisions, which can in turn drive cost savings, reduce risk, and expose new avenues for profit. But are there other opportunities to make money in the world of data? Are companies leaving money on table if they only concern themselves with analysis? Should other companies and services exist?
Ideas on avoiding the data science equivalent of "repair-ware."
Mike Loukides recently recapped a conversation we’d had about leading indicators for data science efforts in an organization. We also pondered where the role of data scientist is headed and realized we could treat software development as a prototype case.
It’s easy (if not eerie) to draw parallels between the Internet boom of the mid 1990s and the Big Data boom of the present day: in addition to the exuberance in the press and the new business models, a particular breed of technical skill became a competitive advantage and a household name. Back then, this was the software developer. Today, it’s the data scientist.
The time in the sun improved software development in some ways, but it also brought its share of problems. Some companies were short on the skill and discipline required to manage custom software projects, and they were equally ill-equipped to discern the true technical talent from the pretenders. That combination led to low-quality software projects that simply failed to deliver business value. (A number of these survive today as “repair-ware” that requires constant, expensive upkeep.)