In this market, it’s proving incredibly vital to be able to see what’s coming. And as this need grows (and the technology becomes more available), businesses are increasingly turning to AI to:

• Be proactive
• Improve critical processes
• Become more agile 
  

However, there’s no flipping a switch and turning AI on just like “that.” Traditionally, AI implementation takes time and preparation. AI also has a reputation of being costly and only available to large companies with data science teams.

It’s this thought that has many businesses wary of approaching AI. 

Luckily, AI is more attainable than you think—regardless of the size of your company or if you have a data science team. And it boils down to answering one simple question.

Are You Ready for AI? Ask Yourself This one Question

That question is: What is the state of my data?

Let’s unpack this. 

To make intelligent decisions requires having all the information (the right information) in front of you to focus on what actually matters, and usually that is only 1% of the data a business will collect.

With more data available today than ever before, the challenge of a lot of businesses today is not a shortage of data, but rather that of knowing what to do with it. Too much data can be a bad thing. For businesses, it means they either:

• Don’t know where to start to make sense of all that data; or
• Create cluttered dashboards and spend time on numbers that don’t give them the insight they need
  

As simple as it sounds, that’s all it takes: having a strong, organized foundation of data. 

Why Does This Matter?

Having the right information at the right time is crucial to stay agile, competitive, and proactive. 

To stay viable, businesses need to be good at anticipating what’s next and react in real time. The average business stores a massive amount of data, and it's often already in the cloud these days. Most of the time, however, that data is unstructured or siloed. That makes it useless to even the best AI applications. 

In order for AI to provide accurate outputs, it needs accessible, structured data. 

Data science can’t overcome bad data. If the data that fuels that technology is inaccurate, scattered, not tagged, or carefully organized in siloes, it won’t matter at all if you have the number one platform in the industry. A weak data foundation means weak insights.

The Data Science Hierarchy of Needs 

The best way we know how to explain this problem that so many businesses are in right now is with an analogy. Let’s borrow Monica Rogati’s analogy between Maslow’s Hierarchy of Needs and data science. 

Maslow believed that people have an inborn desire to be self-actualized, that is, to reach their full potential. To achieve this goal, a number of more basic needs must be met such as the need for food, safety, love, and self-esteem.

Unfortunately, progress is often disrupted by a failure to meet lower level needs. Therefore, not everyone will move through the hierarchy in a uni-directional manner but may move back and forth between the different types of needs.

Maslow and AI

We can say the same thing about businesses: To reach their full potential, certain foundational needs must be met, i.e., before you can do AI or deep learning, you need to take care of data literacy, data collection, and your infrastructure.

This gave birth to Rogati's "Data Science Hierarchy of Needs:"

This helps illustrate that while most businesses are striving for the top of the data science hierarchy of needs (artificial intelligence), many more basic, foundational requirements must first be met. And when one need is met, then a business can move on to addressing its next need. Says Rogati:

“Think of AI as the top of a pyramid of needs. Yes, self-actualization (AI) is great, but you first need food, water and shelter (data literacy, collection and infrastructure).”

To reach the top of that pyramid—and leverage AI to generate business value—businesses need to have a strong data foundation. Successful AI initiatives hinge on organized, accessible, and democratized data.

A Strong Data Foundation Comes Before AI

The reality is that you can’t skip any steps to get to AI faster - the most advanced data analytics tools will only get you to the wrong outcome faster. 

The problem is that there are plenty of businesses who do have a strong data foundation but lack the talent to create machine learning predictions. That's because the skills required to develop models and solutions are hard to come by, which makes proprietary development not only costly, but time-consuming. 

Another problem could also be that they have a large collection of data that is either unorganized or messy (i.e. not prepared or cleaned enough to be machine-learning ready) and therefore the company can’t create machine learning predictions.

Typically, a business runs into one of three problems:

1. The company doesn’t have a data scientist 
2. The company might have one or two data analysts, but the people wearing these hats often don't have the programming skills to create machine learning models. 
3. The company can afford a large team of data scientists, but they are unable to work with speed, since they have a large collection of unorganized data, and creating these models from scratch can take months—and that's on top of everything else they need to do. 
   

On top of all this, too, are issues of rampant miscommunication. It is not always easy to ensure business executives and data scientists are on the same page. Miscommunication due to differing priorities can lead to delays and complications, which impedes on decision-making.

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Related Reading: How Much Does it Cost to Build a Data Science Team?

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No-code AI

The good news is that, more and more, businesses are turning to no-code AI solutions to help them get to the top of the pyramid. These types of solutions democratize AI by making it widely and easily available at a low cost. 

Which is huge. In an age where the demand for software far exceeds the supply of coders, no-code tools are helping an increasing number of businesses lacking technical expertise escape the coding skills shortage.  

For instance, a small business that lacks the budget to hire a data scientist or machine learning engineer can leverage no code platforms and move the power of innovation into the hands of a team of business users.

Machine learning-powered teams can work off of live, up to date information, which means they’re making informed decisions quickly, accurately, and scaling their efforts.

Summary

Implementing AI used to take months, if not years, to implement, and it was costly. It also used to be reserved for companies that had the budget to implement it. This created barriers for a majority of businesses.

No-code AI is removing that barrier. It’s now easier and faster than ever to build AI models. Now, a non-technical employee can build and deploy an AI model over their lunch break using a no-code machine learning platform. 

No-code AI is the most affordable way to implement AI, which has the power to enable any organization to advance up the analytical hierarchy of needs and achieve their ultimate goals.

Ready to see what no-code can do for you? Book a demo with our team today!