As more companies across industries shift to become “data-driven,” data science has taken center stage. But becoming data-driven requires you to know where your data comes from, the best ways to analyze it, and how to communicate its value effectively. Moreover, the reality is that most organizations still struggle to hire and retain coders and data scientists with the skillsets needed to execute data analysis and use AI/ML.
Low-code tools have the power to help. Expanding the use of these technologies and training more employees to use them can make a transformational impact and help organizations scale data science across business units. Yet too few organizations are off and running in the low code journeys.
Here are six key reasons that business leaders should consider investing in building code optional data science capacity throughout their organizations.
1. Leverage Subject Matter Expertise
Too often, the people within an organization who use data analytics tools aren’t particularly close to the business problems that data might solve. We can’t expect data scientists to be experts in finance, marketing, customer service, manufacturing, logistics, and all of the other departments and functions that make a company run. Even if they have access to data from different lines of business, subject matter expertise is important to putting data to good use. One way to solve this is to simply reverse the equation – bringing one new skill (data science) to departments across the enterprise, rather than expecting data scientists to master every other aspect of the business. Data scientists should still partner with each team to make sure rigorous analysis is applied, but they don’t need to “own” every analysis.
2. A Diversity of Ideas
When more people in an organization have data analytics skills, they can try out a greater number of ideas – increasing the likelihood that they’ll land on valuable insights. Often, the answers lie in surprising places. One car manufacturer that I’ve worked with used data science to help business leaders figure out how to reduce manufacturing production delays. A number of stakeholders in the company had assumed that the problem lay with the high complexity of a certain power train. But in fact, analysis revealed there was substantial opportunity in the installation of a collection of seemingly minor changes in accessory packages; an exploration that wouldn’t have happened only focusing on the most obvious problems.
3. Faster Time-to-Insight
For most organizations, the time between someone having an idea about how to solve a problem with analytics, and actually being able to test that idea out, is far too long. The COVID-19 pandemic showed just how quickly conditions on the ground can shift, and businesses need to be able to quickly arrive at data-driven insights to adjust before they’re left behind. Data science teams are typically already overloaded with projects, making it difficult for them to respond rapidly to new additional developments. But by creating analytics capabilities throughout the organization, companies can tighten up their analytics timelines.
4. Improved Collaboration
The past few years have shown us just how important collaboration is to productivity. Collaborative authoring tools like Google Docs have upended traditional workflows, offering benefits from version control to faster edits and iteration; live video conferencing has kept teams close and productive. So while data science was historically done by individual contributors in code, the next generation of analytics tools are collaborative and no-code. New innovation lets teams access the benefits of real time discussion to enhance insight discovery, and no code lets everyone participate. Users, whether newly exposed to data or working alongside expert data analysts, will solve problems faster together than they could apart.
5. Awareness of Pitfalls and Fallacies
Mark Twain once said that there are three kinds of lies: “lies, damned lies, and statistics.” That is, data and statistics are sometimes used to support misleading conclusions. A common example, but if you confuse correlation for causation, you might decide that carrying an umbrella causes it to rain. Most people without analytics training aren’t familiar with common data science traps, yet everyone will be exposed to data in some way. Have you ever heard of Simpson’s paradox, for example? Have your employees? Proper no code tooling can help warn you against faulty analysis as the team is still learning to use data.
6. Building a Talent Pipeline
When it comes to data analytics talent, you can either compete for it, or you can create it. Applying no code data science in a widespread way results in exposing and training teammates already on staff to data science concepts, and results in companies having ready access to a wider pool of data science-experienced individuals to draw-on to meet on-going needs. And longtime loyal employees who acquire analytics skills may be might even be more likely to stick around than formal data scientists who will continue to be bombarded by offers, even after they are hired.
The idea of becoming trained in analytics may at first seem intimidating to line-of-business employees. But remember: The idea of getting trained in Microsoft Excel was once intimidating to people. Today, basic computing skills are simply part of the job for nearly all knowledge workers. In the coming years, we may see the same thing happen for data science. With the proper training and development, we companies can create a robust stable of citizen data scientists who can quickly and easily do the analysis they need to drive business forward.
About Tim Kraska
Tim Kraska is an Associate Professor of Electrical Engineering and Computer Science in MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), where he co-leads the Data Systems Group. He is also the Founding Co-Director of MIT Data System and AI Lab (DSAIL) and co-founder of Einblick Analytics, Inc. His research is focused on dramatically increasing the efficiency of data-intensive systems and democratizing data science through machine learning (ML). Kraska is also lead instructor of the new MIT Professional Education course, “No-Code Analytics and AI”, which debuts in 2022.