If you’re an analytics professional who can code, the notion of collaborating with non-coders to create machine learning models may seem counterintuitive.
When you’ve spent time and effort learning a coding language like Python or R, simplifying data science projects for non-coders can feel like a step in the wrong direction—especially given the access to libraries and frameworks for AI/ML that those languages offer.
On the surface, that’s completely fair. However, the hard truth that companies and their analytics teams must face is that less than 1% of machine learning models have their desired impact. And that comes at a time when organizations are looking to increase the ROI on artificial intelligence, machine learning, and advanced analytics efforts to 6.7 times in the next two to three years according to Accelerate Your Data-Driven Transformation, a commissioned study conducted by Forrester Consulting on behalf of RapidMiner. Regardless of individual preferences for how to create a model, most can agree that those results won’t cut it in a competitive business environment.
So what’s going wrong
What’s Wrong?
While there are a few factors creating this dilemma, most research points to the fact that companies are overinvesting in building advanced analytics functions without promoting organization-wide data fluency. To put it in plain English, execs hire a bunch of employees for roles they’ve never previously hired for, isolate those employees from the rest of the business, and expect them to magically find insight that’ll create a sustainable competitive advantage. Truthfully, it’s a wonder that this approach works even 1% of the time.
On a (much) more encouraging note, companies that recognize the need for widespread data fluency are already seeing stronger results than their siloed counterparts. A recent McKinsey study found that 70% of organizations that invest in helping all their employees understand data report positive business impacts that exceed those investments, and it’s not difficult to see why. Cross-functional buy-in facilitates the informed sharing of data and insight, which goes a long way when it comes to making data-driven decisions that will impact multiple areas of a business.
Addressing the Elephant in the Room
Before we go any further, let me make an important point: no one is suggesting that you should take an entirely code-free approach to data science.
While many popular data science platforms do have automated and visual interfaces for non-coders, you’re not bound to them. The right end-to-end platforms (😉) also offer notebook environments that are designed for skilled programmers, allowing them to leverage a language like Python and its associated open-source libraries.
If your organization is fortunate enough to have data scientists who can code, you’ll be all the better for it. You can share custom-built models as operators so that business experts can reap the benefits in visual workflows and lean on those experts to gain valuable perspective on the problems you’re trying to solve. In a collaborative data science environment, everyone wins.
Why a Collaborative Environment is Crucial for Data Science Success
Knowing that you can productively work within a data science platform, let’s talk about why you should.
Get deeper context on business problems
As you likely already know, data science projects can positively impact most business functions. Insights gathered from company data can help production teams to improve yield, supply chain managers to ensure on-time delivery, and marketers to make more relevant offers to potential customers. While many companies understand this, they often rely on data science teams to do all the work (which, as mentioned above, doesn’t usually work out.)
In siloed environments, it can be challenging to even find the right data to create a model from, as it requires inputs from multiple areas of the business. If you’ve ever had a project stall because you couldn’t get access to relevant data, you know this all too well. Most departments have their own ways of collecting and storing data, not to mention unique security protocols that govern access.
If you do manage to get your hands on relevant data, making sense of it can be a burden. The problems that your company is trying to solve with data science are usually complex, and can involve physics, chemistry, or even psychology depending on your industry. Learning about these topics takes time and dedication, especially when you need to understand the field well enough to make sense of source data.
By engaging business experts early, regardless of their ability to code, you gain useful insight into business processes, common challenges, and things you may not have accounted for if you’d been left to create a model on your own. To be clear, you could learn some of these details through your own research, but why spend the time if you have a knowledgeable resource who’s ready to help?
Companies that take a more collaborative approach to data science create a mutually beneficial dialogue between analytics teams and business experts to drive business impact.
Put more projects into production
87% of data science projects never make it into production. While this is a troubling statistic for companies that are investing in the area, it’s especially unfortunate if you’re the one that’s doing the work.
Similar to other digital transformation struggles, it’s often a lack of collaboration that gets in the way of successfully completing a data science project. We’ve already established that siloed analytics teams struggle to get access and establish the right context around source data—but even those who find insights struggle to share them without the proper buy-in and processes in place.
If you manage to create a data model without looping in business experts early in the process, your work is still far from done. You then have to explain that model to executives in the functional area you’re trying to help—how it works, why it works, and what your recommendations are based on it. As you can probably guess (or have experience with), this stage in the process is often met with confusion, hesitancy, or some combination of the two.
When advanced analytics teams don’t effectively collaborate with the rest of the business, the projects they’re working on can feel more like “nice to haves” than something that can completely overhaul and transform business processes. When projects don’t have early executive buy-in and can’t be easily explained, it can be difficult for businesses to justify putting them into production. Most people don’t inherently trust what they don’t fully understand, which is especially true when the thing they don’t understand has significant financial implications.
Companies who invest in data fluency and build collaborative environments address these challenges head-on. When buy-in is coming from the executive level, business leadership is just as invested in getting projects off the ground as the analytics teams they employ. When those execs, business analysts, and domain experts are all co-collaborators on your project, it’s not left to you to convince them why it should be put into production.
Sustain value over time
Even the simplest models become less effective over time if they aren’t actively managed. As your business and its data changes, models must be updated to reflect that.
There’s a good chance that you’re already familiar with the first challenge this presents—ongoing model management isn’t particularly fulfilling work for data scientists, who are (understandably) more interested in applying the latest developments in ML to new problems. When your attention shifts toward the creation of new models, the ones that are already in production can quickly become an afterthought.
The second major challenge is just how high the demand for data scientists is. A Burtch Works study found that the average tenure of data scientists who had changed companies in the past year was 2.6 years. The Bureau of Labor Statistics reports that the demand for data science skills will drive close to a 30% increase in the field’s employment by 2026.
This research points to an inevitability for any company investing heavily in data science today—turnover. While this may be great for your job prospects, it’s important to recognize just how much knowledge you’ll take with you when you leave an organization. If your company hasn’t facilitated effective collaboration between your team and the rest of the business, they’ll likely be left with outdated configurations. Not to mention the models that you worked hard to create will deliver little (if any) business value once you move on.
By contrast, the companies who promote widespread data fluency have a shared understanding of the models they’re relying on. By making machine learning accessible to non-coders, they allow business analysts and domain experts to maintain models over time and explain them to new data scientist hires.
Wrapping up
If you have a coding background, working within a code-optional platform may seem restrictive and unnecessary at first glance. While it’s true that visual workflows and automated machine learning are designed to help non-coders make sense of company data, end-to-end solutions like RapidMiner often provide notebook environments for more experienced data scientists to write code, which gets everyone working side-by-side.
This facilitates effective collaboration between teams, allowing you to develop a deeper understanding the data you’re working with, bring more models into production, and trust that the models you create will be effectively managed once you hand them off.
If you’re interested in learning more about how you can create machine learning models that your coworkers who aren’t coders can benefit from, check out our past webinar: You Say Code-Free Data Science, We Say Code-Enhancing.
You can also download our ebook Building the Perfect AI Team if you’re interested learning more about the various players that contribute to a successful AI project.
