Big Data has generated a lot of hype. However, it has also given wings to a widespread misconception, the existence of big data can offer a business with actionable insights and a positive business outcome. The reality is a bit sobering because getting value out of data requires a team of capable data scientists who can sift through it all. Most corporations understand this, as evidenced by the 15x to 20x growth in data scientist jobs from 2016 to 2019.

However, even if you have a capable team of data scientists, you must still clear the major hurdle of putting these ideas into production. To realize true business value, you must ensure that your data scientists and engineers work in tandem together.

The Big Gap

At the very core, data scientists are innovators who are committed to extracting new thoughts and ideas from the data your company consumes daily. On the other hand, engineers build off those ideas to create sustainable lenses for viewing our data. These data scientists are tasked with manipulating, merchandising, and deciphering data to get a positive business outcome.

To accomplish this feat, you must perform various tasks that range from data mining to statistical analysis. Organizing, collecting, and interpreting data will be done in the pursuit of identifying relevant information and significant trends.

Even though engineers work in tandem with data scientists, there are some distinct differences between the two roles. The fundamental difference is that engineers place a decidedly higher value on the ‘production readiness’ of systems. From the security and resilience of the models generated by data scientists to the actual format and scalability, engineers want their systems to be reliable and fast.

In other words, data scientists and engineering teams have different day-to-day concerns. That begs the question, “how can you position both roles for success and for extracting meaningful insights from your data?” The answer lies in dedicating time and resources to perfect data and engineer relations. It’s important to reduce the clutter of ‘noise’ around data sets and smooth any friction between the teams who play vital roles in your business success.

Here are some of the three crucial steps towards making this a reality:

1.      Creating a Features Store

One of the best ways to maximize value from clean coding is to ‘productize’ internally and create an environment where both data scientists and engineers can lean on their strengths. This is known as ‘features store,’ which is essentially a centralized location for storing documented and curated features. The purpose of this data management layer is to feed curated data into the machine learning algorithms, so apart from ease-of-use and standardization, the main benefit for the team is that their features store allows consistency between models.

The proliferation of machine learning and big data at the organizational level has created new challenges and new opportunities. The first phase was the realization that big data wasn’t going to help you create new efficiencies so that innovative thinkers are making sense of it.

The second phase is about helping those good people, the data scientists who are great at finding value, and put their ideas into practice in a manner that meets the rigors of an engineering team that is operating at scale.

2.      Placing Higher Value on Clean Code

With your data and engineering teams speaking the same language, you can always focus on tactical aspects, such as clean and easy-to-implement code. Whenever a data scientist is in the earliest stage of working on a project, the experimental and iterative style of their workflow can seem chaotic to an engineer working on production systems.

The mashup of inputs, both external and internal, are manipulated as they start to train their models. They can operate within a fluid environment, which is commonplace for data scientists but can pose a major problem for engineers. If the code from experimentation or prototyping the phase is passed to engineers, you will hit a roadblock. That will manifest itself in the model that falls short for overall speed, scalability, and stability.

To counter this roadblock, most teams invest time and resources into standardization. The result is that the data scientists and engineers are aligned on various parameters from coding standards, security standards, and data access patterns. That framework will give the data scientists the means of writing code that is based within the ecosystem, and still allows them to focus on overcoming challenges that are specific to their expertise.

3.      Cross-Training

It’s not just enough to put a few scientists and engineers in a room and ask them to solve the problems of the world. You must get them to understand each other’s terminology and speak the same language. One way to do this is to cross-train the teams, but pairing engineers and scientists into pods of two, and you can start encouraging shared learning and break down barriers.

That means learning coding patterns for data scientists, as it allows them to write code in a more organized way, and most importantly, understand the infrastructure trade-offs and tech stack that is involved and turning a model into production.

When both sides are in sync with one another’s goals and workflows, we can create a more efficient software development process. In the fast-paced tech world, efficiency gains can be realized with clear communication and continued education, as engineering is a massive win for any company.