The path to AI success begins with data maturity

Excitement about artificial intelligence (AI) and its applications is palpable, with companies eager to dive in.

While business leaders may be ready to explore AI, their data may not. 

To utilize AI efficiently, the information—data— fed to the software must be fully matured. That upfront preparation is a long-term investment, as AI performs strongest with a detailed and completed roadmap. However, the long-term results can transform business operations.

The challenge we face is that many business leaders need help tracking down their data or determining how much organization it needs. 

In this article, we’ll explore the practical steps for improving data maturity, common misconceptions about AI readiness, the role cloud infrastructure plays, and how MichiganLabs helps organizations overcome these challenges. 

Data maturity and practical steps to improve it

Data is often spread out in a business. To understand its maturity depends on how much of it there is, what the data informs, and how it is organized. 

Want to take steps toward improving your company’s data? Here are practical solutions business leaders can implement to improve data maturity. 

1. Track down your data: Data fragmentation is always going to be a challenge, regardless of how large or small a company is. It’s important to know where data is stored to ensure quick access and efficiency, so take the time to track it down.
2. Ensure data ownership: Do you have access to your data? Passing data ownership to a third party may be a quick and easy solution, but it will not benefit you in the long run.
3. Build safeguards: Data is sensitive information and there must be safeguards in place to keep it from getting lost, erased or fragmented. Are you thinking about cybersecurity threats? Do you know who has access? How do you manage passwords? These must be considered and tended to regularly. 
4. Create standard practices: When data comes into your platform, you need to know where it is stored, how it is being used, where it originated, and how to transfer access safely among users. Creating a standard of procedures that supports your data maturity goals and considers your data’s security will keep you organized and on track. 
5. Know which data is valuable: Data maturity is not always about the quality or quantity of a single data source, it is also about the breadth of data you have. Do you have the data to accomplish what you want? For example, let’s say a company wants to reduce energy costs and only has data on the total electricity usage of its building. While this helps, it doesn’t provide a complete picture because variables like specific equipment electricity use, peak usage times, and environmental factors also impact energy consumption. 

Take these steps to improve your data maturity and you’ll see a more efficient company. 

Is your data AI-ready?

Even without AI, it’s important to understand what's going on with your product. Are you taking care of it and making sure it’s high-quality data? Investing in data maturity ensures both happen. When a company is not monitoring and improving on its data, getting that data to a mature place is a huge leap and takes a lot of time.

A few areas that prolong the process include:

• Reviewing and introducing historical data: What data is important to keep? What is no longer relevant?
• Determining how often (daily, weekly, monthly) data comes in: The higher the frequency, the bigger the investment needed for data maturity. 
• Knowing how much data is collected: Having a clear idea of how much you’re working with will help us know where to start. 
• Strategizing what to do with the data: How do you plan on using this information?

All these steps will ensure your data can maximize AI’s benefits.

While we’re talking about AI’s benefits… 

…a big misconception about AI is that it’s a silver bullet that can solve anything. The reality about AI is it’s only as good as the data it is fed. 

Understanding how much data you have and how to use it is the best way to use AI to its full capacity.

Cloud infrastructure’s role

At MichiganLabs, we’re often asked about cloud technology’s role in supporting data infrastructure. 

Cloud technology is a secure network of servers that host data. Managed offsite by experts, these servers take data-hosting off businesses' hands. 

Utilizing cloud services to host your data is one of the best things you can do to improve data maturity. 

In many cases, we are pro-cloud. Here’s why:

• Ease: You’re not personally managing the infrastructure. 
• Data management: Servers, backups, and SLA is all taken care of.
• Scalable: Cloud infrastructure is easy to adjust based on growth.
• AI integration: AI training loops can be supported by cloud technology.
• Cost: Cloud infrastructure is cheaper than in-house solutions.
• Permanence: Purchasing servers and hiring staff for in-house management is a bigger and longer-term investment than hiring a cloud company.
• Overall less risk-averse: Cloud management could be the difference between canceling a contract and tracking down an employee who left. 

A unified data approach is how MichiganLabs provides solutions to clients and cloud technology is one of the ways we get there. With data always expanding and evolving, trusting it with a cloud network is the best way to organize it so it has room to grow. 

How MichiganLabs overcomes these challenges 

Data maturity is key to solving a problem, however, most business leaders don’t know how to get there. Leaders come to us with requests and learn that data maturity is often part of how we solve their requests.

When business leaders see how data maturity improves day-to-day operations, it becomes a practice they want to continue. Teams often start data-focused projects because they see how investing in data maturity supports their mission, goals, and bottom line.