In the age of micro-inspection of our data you can pinpoint what a customer will do and when they will do it with great accuracy—only if you know how to make sense of your data. 

For most companies, data collection has never been an issue. We all have more data than we collectively know what to do with - but not everyone knows how to understand and have meaningful conversations about that data. 

To make data-driven decisions requires building a data-literate workforce to understand, quantify, and communicate the value of their data. 

Data Literacy

Being data literate is defined by Gartner as the ability to read, write, and communicate data in context, including an understanding of data sources and constructs, analytics methods and techniques applied, and the ability to describe the use case, application, and resulting value.   

It’s also defined as having digital dexterity. That is, an employee's ability and desire to use existing and emerging technologies to drive better business decisions.

By 2023, data literacy will become essential in driving business value. In fact, a data literacy survey by Accenture of more than 9,000 employees in a variety of roles found that only 21% were confident in their data literacy skills.

In a world full of more data than we know what to do with, the companies with more data-literate people are the ones that win. 

That’s why it’s important to make data science so effortless.

Data Democratization in the Workplace

For a lot of organizations, ownership of data (and its analysis) has traditionally been in the hands of a few specialists. 

But as we generate more data than ever, we’re approaching a point where data needs to move out of the hands of the few and into the masses, before that data becomes unleveraged (and a liability).

And this could be a big reason for why so many organizations run into issues with data literacy: When data is democratized, there is increased efficiency and improved accuracy - both of which produce greater value for the company as a whole. 

But Data Alone Isn’t Enough to Empower Employees

True data democratization requires AI democratization. And that’s because data needs to be understood and contextualized.

That means: employees need to not only be able to have access to and interpret data, they need to also understand why that data matters and what they can do with it. 

This is where AI and machine learning can help. And this is exactly why no-code AI tools are so beneficial to organizations who typically don’t have a data scientist to make sense of their data, draw meaningful insights, and build complex machine learning models from scratch. 

Why Everyone Needs Access to Data: A Use Case

Let’s say you’re a Business Analyst in a small organization. You’re waiting on important information that could potentially make a huge impact. 

Or, maybe you’re the head of BI. You want to empower your team with AI so they can make transformative decisions which will help drive business. You also want to be able to show your boss how you and your team are transforming the organization.

The problem is that you don’t have the skills or training to build complex machine learning models. Data science isn’t easy. It might take an army of data scientists or machine learning engineers (something even with PhD degrees) to crunch lines of code and build models - and that can take a ridiculous amount of capital. Not to mention the time it takes to build a model can be entire months.

Related Reading: The Ultimate Guide to Machine Learning

And the reality is that not everyone has an army of data scientists. You likely have one person, or a very lean team, who can build, interpret, and explain the results of those models. 

But that also means you’re waiting for information alongside every other department, as they’re probably getting a lot of technical questions from non-technical people in marketing, HR, and finance, etc. and they have to translate the findings into actionable insights for the whole company.

You think: "How easy it would be if I could just learn how to unlock that data by myself so I can get the answers I need to make an impact." 

Despite this being an obvious solution to a common problem, it can be overwhelming to know where to start.  

AI is Slowly Moving into The Hands of Business Users  

Despite moving from the realm of data scientists and into the hands of everyday users, in most cases, AI is still concentrated to a small percentage of the workforce. 

Like we said before, true democratization occurs when machine learning enables decision makers and business users to easily apply and change machine learning models to their specific needs. 

But, the whole machine learning process doesn’t have to be technical.

With a no-code machine learning platform, you can create accurate Machine Learning predictions on your own, without having to write a single line of code. 

No-code Tools Speed Up the Democratization of Data Science

Here's what happens when you adopt a no-code machine learning platform.

1. You put data in the hands of the decision maker

Most often, it’s the decision-making departments, like customer experience, marketing, HR, or finance teams that are primed to benefit from greater access to data from across the organization. 

No-code enables business users to easily explore and build machine learning models to make accurate predictions without writing any code. When business users can work off of live, up to date information, they’re making informed decisions.

Having access to these kinds of machine-learning generated insights can be all the difference between closing a sale or losing one.

And, if using a no-code machine learning platform like yours truly, they’re making those decisions quickly, accurately, and scaling their efforts.

2. You can create machine learning-driven products (and scale them)

Customers want personalization, efficiency, and content and product curation. To do that, products need data input and output that appeals to the user’s needs. 

The problem is, while most businesses may have the fuel to build these products, if they aren’t leveraging the speed and accuracy of no-code machine learning, they fall behind competitors who use predictions to:

• Make informed decisions about their product
• Reduce time to market
• Deliver one-to-one experiences
• Improve UX

Machine-based learning personalization provides a more scalable way to deliver the kinds of unique experiences your customers and prospective customers expect.

 3. You increase efficiency for technical teams

The high demand for model-building and tuning to run those models is time intensive, and can creates major bottlenecks for both technical teams and those relying on all that data. 

Putting the power of predictive analytics into the hands of anyone who works with data enables data scientists and other technical teams to be more efficient with their time. 

Summary

The key to addressing a lack of data literacy is to democratize data and AI.

The good news? By having a no-code AI tool like Obviously AI, you can easily understand what your data is telling you. Not only that, you can  visualize your data, create shareable reports, export results, and take meaningful actions from the platform generated predictions on your data. 

When everyone has access to, and can understand, their data and how to pull insights from it, then everyone has the confidence to ask the right questions and make better decisions.

Get started addressing the lack of data literacy in your company with no-code machine learning.