From data science to citizen data scientist (and what that means for SaaS as a whole)

Love it or hate it, the data is here to stay.

If this scares you, you can try to ignore it. If that comforts you, you’ll probably want to bite into it. But the fact remains: Nowadays, every action we take generates data. Think about any day in your life and you will have created data. Use your GPS to get to work; your mobile phone to pay for groceries at the supermarket at lunchtime; your Google searches throughout the day when you take a short break. Each person generates mind-boggling amounts of data every day, just by going about their usual routines.

The good news is, although it scares you, there’s a lot of valuable information lurking in this data, which ultimately helps make our lives easier.

Whether it’s finding the fastest route to work, keeping tabs on our finances, or having a more intuitive search engine to rely on at work. Whereas at one time this data was the preserve of the IT team – BI specialists, data scientists and other skilled data experts – it is now for all of us. You no longer need a doctorate in data science, or 15 years of technology experience to understand a dataset. All you need is access to a BI dashboard and a passion for learning, and off you go.

Nowadays, anyone can be a citizen data scientist.

What exactly is a citizen data scientist?

“Citizen data scientist” was a term first introduced by Gartner.

They define him as: “a person who creates or generates models that use advanced diagnostic analyzes or predictive and prescriptive capabilities, but whose primary function is outside the realm of statistics or analysis”.

In other words, they are non-technical employees who use data science tools to solve business problems. They have a solid business background and are able to combine that expertise with user-friendly technologies to make sense of their data and make smarter business decisions.

These citizen data scientists don’t have to sit in IT and are spread out across the organization; from sales and marketing to customer service or human resources.

Above all, their business experience and knowledge of its priorities allow them to integrate data science results into business processes. And it doesn’t end there, because the ability to turn data into information isn’t just valuable in a business context; Informed individual data consumers are also starting to rely on data to make better decisions about their privacy.

What is this information that can help us make better decisions?

The main reason is to get smarter. Data helps us decide what might be the best course of action and makes our lives easier. More data-driven than finger in the air.

In a business context, this can be for e-commerce purposes, where sellers can use consumer behavior to decide how to structure a website to improve sales; or in logistics where historical travel data can help plan the fastest possible routes for deliveries. In a consumer context, it might be someone who wants to track their personal finances or save money more effectively using an app that records spending habits. It can even go as far as optimizing the use of your car so that it generates less CO2.

In each scenario, by giving consumers the ability to analyze their own data, in the moment they can make better, more data-driven choices.

You decide what to do with the data

There are many reasons that data consumers would want to get behind the wheel of their data and become a citizen data scientist.

First, it has become almost impossible not to use data in our daily working life. Most companies today measure performance using KPIs and more and more business decisions are made based on what that data tells them. They rely heavily on their data to take the next step, so if you don’t engage with the data yourself, you could be a few steps behind.

Second, there simply aren’t enough data scientists to meet the demand for information. And with the ever-increasing volume of data, a data scientist’s time is now spent on more complex analytical tasks such as data modeling, preparation, AI and ML algorithms, leaving less time for daily business analysis.

And third (perhaps most important), the technology is now available to not only control them, but also enrich the data by placing it in an important functional context.

Technology has paved the way for a mass democratization of data science, with drag-and-drop tools that make it easier than ever to slice and slice business data and remove complexity. And because business users have the subject matter expertise for their particular function, it means they can begin to understand and put data into context that was not previously possible with dedicated data scientists.

So why is this important to SaaS product owners in a business context?

Simply put, because the technology that empowers business users to become citizen data scientists means they can make relevant, data-driven decisions without requiring technical expertise. From an employee perspective, this is a revolutionary approach that will see SaaS companies / products drive positive change in their end user organizations. Equip users with the tools they need to analyze their own data:

  • Lighten the workload of data scientists so they can focus on basic analytics
  • Foster a culture of data best practices within an organization with business people encouraged to use technology for decision making.
  • Position the SaaS product as innovative, supportive, and designed to create actionable insights for better decisions.

Ultimately, no business user should be limited to a single data snapshot created by a data scientist with less business context. They should explore and create their own views on the data, based on the expertise and context they bring to the table. Consequently, the rise of the citizen data scientist and SaaS platforms compatible with analysis probably go hand in hand.

Karel Callens, CEO and co-founder,

Sean N. Ayres