What does that have to do with developers?
This is a very important question that we'll spend a lot of time developing in the coming chapters. Let me start by looking back at my professional journey; I spent most of my career as a developer, dating back over 20 years ago, working on many aspects of computer science.
I started by building various tools that helped with software internationalization by automating the process of translating the user interface into multiple languages. I then worked on a LotusScript (scripting language for Lotus Notes) editor for Eclipse that would interface directly with the underlying compiler. This editor provided first-class development features, such as content assist, which provides suggestions, real-time syntax error reporting, and so on. I then spent a few years building middleware components based on Java EE and OSGI (https://www.osgi.org) for the Lotus Domino server. During that time, I led a team that modernized the Lotus Domino programming model by bringing it to the latest technologies available at the time. I was comfortable with all aspects of software development, frontend, middleware, backend data layer, tooling, and so on; I was what some would call a full-stack developer.
That was until I saw a demo of the IBM Watson Question Answering system that beat longtime champions Brad Rutter and Ken Jennings at a game of Jeopardy! in 2011. Wow! This was groundbreaking, a computer program capable of answering natural language questions. I was very intrigued and, after doing some research, meeting with a few researchers involved in the project, and learning about the techniques used to build this system, such as NLP, machine learning, and general data science, I realized how much potential this technology would have if applied to other parts of the business.
A few months later, I got an opportunity to join the newly formed Watson Division at IBM, leading a tooling team with the mission to build data ingestion and accuracy analysis capabilities for the Watson system. One of our most important requirements was to make sure the tools were easy to use by our customers, which is why, in retrospect, giving this responsibility to a team of developers was the right move. From my perspective, stepping into that job was both challenging and enriching. I was leaving a familiar world where I excelled at designing architectures based on well-known patterns and implementing frontend, middleware, or backend software components to a world focused mostly on working with a large amount of data; acquiring it, cleansing it, analyzing it, visualizing it, and building models. I spent the first six months drinking from the firehose, reading, and learning about NLP, machine learning, information retrieval, and statistical data science, at least enough to be able to work on the capabilities I was building.
It was at that time, interacting with the research team to bring these algorithms to market, that I realized how important developers and data scientists needed to collaborate better. The traditional approach of having data scientists solve complex data problems in isolation and then throw the results "over the wall" to developers for them to operationalize them is not sustainable and doesn't scale, considering that the amount of data to process keeps growing exponentially and the required time to market keeps shrinking.
Instead, their role needs to be shifting toward working as one team, which means that data scientists must work and think like software developers and vice versa. Indeed, this looks very good on paper: on the one hand, data scientists will benefit from tried-and-true software development methodologies such as Agile—with its rapid iterations and frequent feedback approach—but also from a rigorous software development life cycle that brings compliance with enterprise needs, such as security, code reviews, source control, and so on. On the other hand, developers will start thinking about data in a new way: as analytics meant to discover insights instead of just a persistence layer with queries and CRUD (short for, create, read, update, delete) APIs.
