A recent VentureBeat article emphasized a critical success factor for deriving value from big data analytics initiatives—collaboration between data scientists and engineers. According to the piece, “Even if you have a capable team of data scientists on hand, you still need to clear the major hurdle of putting those ideas into production. In order to realize true business value, you have to make sure your engineers and data scientists work in concert with one another.”
While these groups are aligned in their desire to drive positive outcomes through data, there are some distinct differences between the roles that can impede the progress of initiatives unless organizations find ways to remove existing friction.
So, how to do this? Read on for tips to improve relations between data scientists and engineers and pave the way for greater data success.
- Cross-training initiatives. The VentureBeat article states, “It’s not enough to simply put a few scientists and a few engineers in a room and ask them to solve the world’s problems. You first need to get them to understand each other’s terminology and start speaking the same language.” Through cross-training initiatives that pair data scientists and engineers together, companies can encourage these employees to learn more about their respective goals and workflows and, ultimately, foster a more efficient development process.
- Focus on clean code. Greater alignment between the data science and engineering disciplines means that companies can place a higher value on clean, easy-to-implement code and avoid numerous roadblocks associated with code development done in silos.
- Create a features store. According to VentureBeat, “One of the best ways to maximize value from clean code is to ‘productize’ it internally, creating an environment where both engineers and data scientists can lean on their strengths.” This “features store” can feed curated data into machine learning algorithms, enabling consistency between models, increasing algorithm stability, and improving overall efficiency.
Big data analytics and machine learning already offer companies countless ways of improving decision making and operating more competitively, and this will only increase as these technologies mature and innovations like 5G become a reality. As such, there’s no better time than the present to ensure your data scientists and engineers have a collaborative relationship that supports current and future analytics initiatives.