The short answer to this question is that AI can involve coding, but doesn’t necessarily always have to — this will depend greatly on your background, skill set, and general preference. For the purposes of this article, though, we’re going to piggyback on the notion that, last year, a significant chunk of organizations realized that they are not going to scale AI impact without enlisting non-experts to the cause. As a corollary, low- and no-code AI is gaining immense popularity as everyone (especially analysts) wants a piece of the action.
When asked about how low-code and code-free environments are paving the way for new innovation across the enterprise, Dataiku’s CEO Florian Douetteau said,
There are many approaches to helping companies manage and make use of their data so that business users can access it just as easily as data scientists. Perhaps the biggest and most successful move data platform providers have made is providing code-free tools that average non-coding employees are comfortable with. People in all positions across organizations are leveraging data (via data exploration, visualization, and more) to answer business questions in a way that doesn’t need to involve code.”
So, how can we — in practice — help data analysts, business analysts, and other business-savvy people who are not formally trained data scientists, work with data every day?
1. Ensure Common Ground for Data Experts and Explorers
Increasingly, we’re observing business users (who may have operated with data in the past, but with a low data and analytics maturity) become empowered to co-build analytics workflows with experts because they have more access to data and the experts are more inclined to work with them to ensure the proper business subject matter expertise and context. This ability to collaborate on projects with coders and other technical practitioners — combined with upskilling and proper tooling (such as Dataiku) — can lead to faster time to impact.
It’s important to note that the value of low- and no-code tools for sophisticated data science teams is tremendous because it makes so many of the things that become “busy work” for expert data scientists easy. For example, with AutoML, data scientists can speed through the minutiae of building models to get a quick and dirty first model to work with much faster than without it, enabling them to spend more time on other, high-priority projects. Giving up a tiny sliver of individual customization on the most universal parts of a project (i.e., accessing and preparing data, deployment and monitoring, etc.) actually helps them focus on what they joined the team to do in the first place: find innovative solutions to pressing and valuable problems.
So it’s not only a win for the analysts and business users who get to work with data teams to increase efficiency and integrate data analytics and AI into their daily workstreams, but for the data teams as well who ensure they’re writing code when it will make the most difference (and, in Dataiku, they can always turn a step in a visual flow to code and customize when needed).
2. Make Connecting to Data Hassle Free
Analysts are responsible for gathering data from disparate data sources for analysis and reporting, a task that can be tremendously cumbersome with diverse datasets. More often than not, they need to source the data (which is often rife with delays), use a different set of tools for analysis, and then a third set for visualization and reporting. To avoid the disconnect between having the data and being able to act upon the data, teams need a no fuss, no-code connection to data sources. With Dataiku, they get that and more — teams can combine large volumes of structured and unstructured data from several systems and locations and ensure seamless connections to each one.
Further, analysts can connect, cleanse, wrangle, and transform data with no code at all, via a visual interface. But for the advanced analysts and citizen data scientists out there, no need to fret — there are advanced capabilities for when you need them so you can perform basic analytics and reporting functions under the same roof where you experiment with AI and machine learning (ML) initiatives.
3. Meet Them Where They Are
Whether analysts are focused on improving financial analytics and reporting like the team at Bankers’ Bank or advanced enough to create ML models, the beauty of Dataiku is that it meets them wherever they are on that journey. Want to focus on improving reporting through improved data prep and transformations (while moving out of spreadsheets)? Dataiku has easy-to-use visual recipes to clean, filter, join, and aggregate data, easily traceable in a custom visual flow to outline each step. Want to experiment with modeling? Non-coders in Dataiku can use visual ML capabilities to train algorithms, start making predictions, identify clusters, and extract useful information about features without writing a single line of code.