A complete data science team requires more than just data scientists
Organizations across the spectrum, from startups to enterprises, in industries from finance to healthcare, are realizing the benefits of artificial intelligence (AI).
In fact, 14 percent of global CIOs have already deployed AI, and 48 percent will deploy it by 2020, according to Gartner. However, building out the necessary team to successfully undertake these AI projects is more complex than simply hiring data scientists.
Yet many organizations operate under this misconception. Many don’t see that the building and executing of successful, ethical, and insightful AI solutions requires a well-rounded data science team, and not just a few idealistic data scientists who can do it all.
For AI to deliver business value, organizations need to identify the right business use cases. Predictive insights from AI need to be made consumable through data stories, and a deeper understanding of human behavior is essential for the right decisions.
As well as bringing in the perceived “core roles,” data science teams might not realize they need roles such as data translators, data storytellers, and even behavioral psychologists to achieve these goals.
The essential roles
It’s arguable that no one role is more important than another when it comes to creating a complete data science team. There are, however, some roles that spring to mind sooner than others. One of the first positions companies are keen to hire for is a data scientist, who typically uses statistics and machine learning (ML) to analyze and identify predictive insights.
Organizations advancing along their AI journey will also identify their need for a visualization designer, who should have information design and UX skills to bring to life the visual intelligence layer of the data insights. A machine learning (ML) engineer also plays a key role in data science teams as they package the ML models into an end-to-end application. They use their deep programming skills with a mastery in handling data to automate the entire workflow.
…and the ones teams don’t realize they’re missing
In addition to these well-known roles within data science teams, there are some that fly under the radar, but can arguably be just as important. Companies should recognize the importance of data science translators, who act as the bridge between business users and data science engineers by identifying the most impactful projects and business challenges that can be solved by data. In fact, McKinsey estimates that demand for translators in the United States alone may reach two to four million by 2026.
Bringing in a behavioral psychologist can help data science teams interpret patterns into actionable insights that power decision-making and deliver business value. Behavioral psychologists understand why people behave the way they do and can help data scientists by giving insights into purchase decisions or customer churn.
For example, a company seeking to predict server failure would only need to look at past performance in terms of the memory usage level and the load on the server. However, throw real people into the mix, and a data-driven approach needs to be complemented by a human dimension. A company looking to predict employee attrition would need insights on a lot more than employee history or performance. Here’s where a social scientist would come in to provide insights into factors such as demographics, personal preferences, career stage, and past emotional response to improve the accuracy of the predictions being made.
Finally, any growing data science team that wants to effectively communicate the context and narrative of data insights will need a data storyteller. Data storytellers do much more than just creating visualization dashboards: They craft captivating narratives from the data insights that are digestible for even the least technical of team members. It’s the data storyteller’s job to help non-data-scientist team members and executives understand the insights through captivating data stories and help them convert them into business decisions.
In fact, the importance of these roles coming together in data science teams was accurately predicted by Google’s Chief Economist even a decade ago, when he said that “The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it – that’s going to be a hugely important skill in the next decades.”
Building a well-rounded data science team is a lot more demanding than many organizations realize. As they progress towards an environment where data ultimately becomes culture, organizations will find a growing need for each of these roles. Yet as the tech skills crisis continues, the efforts of teams keen to progress along their data journey and hire the necessary talent will remain hindered. Once this skills gap is filled, organizations can move full steam ahead towards AI adoption and reap its many rewards. In the meanwhile, being prepared to fight for talent and expertise is essential to stay ahead of the competition.
This article was contributed by Ganes Kesari, Co-founder and Head of Analytics at Gramener.