Excel & AI - Demystifying Machine Learning

Data Literacy
Future of Work

For many business owners, artificial intelligence and machine learning appear to be almost magical, miraculous tools that unfortunately seem out of reach

Maybe you’ve seen the headlines about how other businesses are using AI to detect fraud, increase revenue and even reduce churn — but don’t you need a team of expert data scientists to get those kinds of results?

Using machine learning to solve challenges and gain insights into your business is a lot simpler than you think. 

Finding new ways to improve business performance and stay competitive in a struggling market is more important than ever, and AI can give you a powerful advantage. Thankfully, you don’t need to invest hundreds of thousands into a data science team, or pause your business for a couple of years while you study for a Ph.D. in artificial intelligence. 

In fact, if you’re comfortable using Excel, then you can use AI. 

The challenge of getting started with AI

These days, the running joke is that most companies starting to build AI make the mistake of hiring a data scientist instead of a data engineer. Sure, it’s important to have someone who can make sense of your data, but that’s impossible without someone to help you organize your data first.

While having your data structured and easily retrievable is the ideal start for building AI/ML, it almost always requires a big investment of time and resources. However, there is a tool that even the most antiquated companies use to store and structure their data: spreadsheets.

Businesses have been using spreadsheets for decades to capture, manipulate and display data. With a little bit of knowledge you can also program spreadsheets and use your data for basic data modeling. 

However, just as digital spreadsheets replaced pen and paper ledgers, modern AI tools dramatically expand what’s possible and open up a wealth of new opportunities for your data. 

Making the jump from spreadsheets to machine learning

If you’re familiar with spreadsheets, you’ll have a big advantage when getting started with machine learning. Data on spreadsheets is easily shared and is usually sufficiently structured to be used with AI (or can be quickly reformatted if necessary). 

It’s also likely you have some background knowledge in data hygiene and analytics, as well as what a ‘sensible’ predicted outcome looks like. You understand data formatting and why you’re collecting that data in the first place — exactly the kind of knowledge needed for a successful ML project.

If you’re already organizing your data or conducting basic analyses with spreadsheets, then the next logical step is to use the same principles with machine learning. While this used to require specialist knowledge, no-code solutions like Obviously AI mean it’s now much simpler to implement. 

Using no-code solutions to leverage your data

Previously, making the jump from Excel to AI would involve lots of heavy coding and learning new complex technologies. However, the latest solutions make this far easier. Just as WordPress and Webflow have enabled people to put websites together without having to learn HTML and javascript, no-code technology has made AI much more accessible. 

For example, the team at LearningLeaders wanted to use their historical data to improve the student experience and increase conversions. They had all the right elements to build an AI model, they had a basic understanding of analytics, but they lacked the necessary experience and skill set to create that model from scratch. 

Instead of hiring an expensive data science team or outsourcing the project, they turned to Obviously AI. In less than a week, LearningLeaders was able to build an AI model that could predict student success with 80% accuracy — far higher than the 57% accuracy they saw with other tools. In addition, they were able to get fresh insights into the top drivers and areas that students should work on to increase their chances of a favorable outcome.

Final thoughts

Although AI and machine learning may seem to be a tool exclusively for tech giants, that’s no longer the case. Just as you don’t need to be a financial analyst to use spreadsheets, you don’t need to be a data scientist to use predictive analytics. 

So while you plan out the big-picture vision of your data transformation, why not continue to enable the teams already leading your analytics? Whether they’re working on business intelligence, sales operations or financial planning, you have a team already positioned to take the first steps in AI. All they need is a tool that can be an extension of their workflow, like Obviously AI.

Our no-code platform allows any tabular data to be converted into an AI model in minutes, with accuracy and transparency. Instead of waiting for your data lake to be complete before building AI, you can take initiative now with the data you already have. Instead of waiting on the data science team to help you build a model, you can take the first step on your MVP. Instead of taking weeks or months to code out the models yourself in Python, you can have thousands of iterations built for you in just a few clicks.

If you know Excel, you know how to take the first steps toward AI because you’re already familiar with using a no-code tool to improve your analytical abilities!

See for yourself how Obviously AI can transform your historical data into actionable insights. Book a demo today!

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