What the data scientist role will look like in 2020

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The data scientist’s primary job function is to extract meaning from large sets of data that will drive strategic business – in an ideal world, at least.

In reality, data scientists, analysts and operations people in general spend a lot of time serving as the messenger – after uncovering key insights, they must communicate those insights to the rest of the company.

As we move toward the future of automation, though, data scientists could potentially benefit immensely from the rise of artificial intelligence and machine learning. These technologies hold the promise to free up the time data scientists would otherwise spend communicating so that they can focus on discovering ways to move business forward.

Let’s take a look at what these technologies look like, how they could impact the role of the data scientist in 2020 and beyond, and how those changes will improve a company’s bottom line.

Artificial Intelligence and Machine Learning Will Take on the Role of Storyteller

In a data-saturated society, we need interpreters to make sense of it all. Traditionally, these interpreters have been human data scientists explaining their findings.

But AI and machine learning have the power to take on the work of data storytelling. They can fulfill the communication duties of the data scientist by capturing data in a language everyone understands: stories.

These technologies can sort through vast amounts of enterprise data and translate it into reports, automated summaries, and other straightforward explanations of data, quickly and at scale.

If a dashboard offers the “what” of a company’s performance data, AI and machine learning-driven storytelling applications can provide the “why” and “how.” They can offer unbiased explanations of what’s happening so that employees can spend their time and energy responding to real-time conditions rather than interpreting graphs.

AI and Machine Learning Will Serve as a Data Scientist’s Assistant

Integrating AI and machine learning technologies won’t mean less work for data scientists. Instead, armed with smarter tools, data scientists will find they have more time to do more interesting work.

To understand what that might look like, imagine this scenario: a data scientist is doing some exploratory analysis on sales data. The dataset is vast and mostly untouched. But before they’re able to get far, the Chief Revenue Officer (CRO) comes over to ask about the latest leads dashboard. There’s some confusion about how leads are being reported and what the numbers mean.

The data scientist spends their day checking earlier reports and explaining where the numbers come from, then adjusting the report according to the CRO’s request. The exploratory project gets pushed back.

Now let’s imagine that scenario with AI and machine learning tools. Think of them as a data scientist’s assistant. They can distill important findings – like the information in the leads dashboard – into easy-to-digest narratives and share those narratives with all stakeholders.

Instead of asking the data scientist for an explanation of the numbers, the CRO can refer to the AI-generated summary.

Instead of digging through past data, the data scientist can continue their exploration – and, ultimately, discover ways to operate more efficiently and profitably. AI simply makes the human more valuable. In other words, the changing technology landscape isn’t just good news for data scientists. It’s good news for the entire business.

A Virtuous Cycle of Data Leading Innovation

Just as every unhappy family is unhappy in its own way, every company with inefficient data communication practices has its own unique problems.

But across companies of different sizes and industries, there are a few universal shortcomings. At smaller companies, budgets restrict hiring, so existing data scientists are often spread too thin. At larger companies with robust data science teams, the data sets are so vast and varied that communication problems often scale to match.

AI and machine learning have the potential to create a virtuous cycle for organizations of any size. Less busywork for data scientists means these high-skilled workers can do high-skill work. With an intelligent information platform, everyone in the organization can stay up to speed on data breakthroughs.

Collectively, these changes set the stage for a business to be driven by data without its data demands dictating how employees spend every hour of their day.

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