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Upskilling

You’ve probably heard the term “upskilling” in relation to machine learning and data science. But what does it mean, exactly, and why does doing it right matter for your organization? We’ve got the answers.

What is Upskilling?

Upskilling gets used in a bunch of different ways by different people. But for us, upskilling is continually empowering experts to harness the power of data science and machine learning by providing them with ways to produce tangible business impacts.

Phew! That’s a doozy of a definition. Let’s break it down a bit to see what we mean.

  • Continually empowering: Upskilling isn’t a one-and-done project. It’s a process that needs to be baked into the way that the members of your organization work together.
  • Experts: Your organization already has experts who understand the ins and outs of your workflows, processes, and data. You want to upskill those experts to make use of that knowledge. Likewise, you may already have data scientists, but they likely aren’t familiar with the subject matter that’s critical to your business. Folks in both of these camps are experts, but in different things. Continuous upskilling means getting these experts talking.
  • Harness the power of data science and machine learning: Data science and machine learning are changing the business landscape, and companies that aren’t using these tools are going to fall behind their competitors.
  • Providing them ways to build models: Machine learning is all about building models. If you want to get value of an ML initiative, it’s critical that it’s producing models that make it into production.
  • Produce tangible business impacts: All of the above points are irrelevant if what your subject matter experts produce doesn’t have any positive financial impacts on your business’s bottom line. It’s important to understand that the purpose of upskilling experts isn’t just to give them new skills, it’s to improve the financial situation of your business.

Why Does Upskilling Matter?

Every organization is sitting on data that could unlock insights if it was used for machine learning but figuring out how to derive insights from that data can be tricky.

And even companies that are doing things right aren’t doing enough when it comes to AI and ML. In 10 to 15 years, we’ll be in a position where AI and ML are as ubiquitous as computers are today. If you’re not setting yourself up today to take advantage of these changes, you’re already lagging behind.

To try and get ready, many organizations go the route of building out a data science team (see below for more), but you already have people who intimately understand that data that you have, how it’s being produced, and what you might do with it.

Likewise, you may already have a team of data scientists, but struggle to use their expertise to address complex business problems. They need a way to speak the same language as the subject matter experts so that they can collaborate

If that data is just sitting in a warehouse somewhere instead of being used to improve your operations, you’re leaving money on the table.

Three paths to data science impact

Once you’ve decided you want to do something with this data, how do you go about it? Let’s look at three scenarios:

1. Set up a data science team

This is the option that many organizations go with, but it’s not without problems. Your data scientists likely don’t have much subject matter knowledge, so their ability to have an impact is going to be hampered, at least initially, as they learn about your field. But even once they are up and running, it’s unlikely that they’ll ever develop the comprehensive knowledge of your subject matter experts.

2. Upskill your experts

This is a better option than the first path—your experts know their data and subject matter in depth and can jump in right away to start working with data, if you can teach them how to build models. However, this can be time consuming if you have to teach them to code (which is why we explicitly don’t think about teaching someone to code as upskilling in the truest sense), and their lack of data science knowledge might mean they aren’t building models as quickly or effectively as the could otherwise.

Many subject matter experts would love to learn more about machine learning, but simply don’t have time to learn Python or R, and you don’t need your business to turn into a software development enterprise to use machine learning; you just need to support them with a non-code-based approach.

3. Upskill your experts and your data scientists

The best option involves pairing the two above. Make sure to upskill your experts so that they can work alongside the data scientists to create models by letting them learn how to make and operate models. But on the flip side, don’t be afraid to bring in data scientists—after all, they have their own domain of expertise when it comes to how to build, evaluate, and maintain models. Plus, they’ll continue to be upskilling themselves alongside your subject-matter experts as they learn about your business and its operations.

To get these two groups working alongside each other, you need a platform that support model creation in both code-based and non-code-based environments so that everyone can work together, pooling their expertise to solve problems quickly and provide ROI on your ML investments. That’s the sweet spot for upskilling, where everyone is improving alongside everyone else.

Start Upskilling Today!

As you can see from the above, upskilling experts so that they can build models and work closely with data scientists—while also upskilling your data scientists to better understand the details needed to push your organization forward—has the potential have a significant, rapid impact on your organization.

All you need is a tool that let’s everyone collaborate to bring their own specialties to the table and work together to build models that improve your business.

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Learn more about RapidMiner and how you can upskill your team

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