Insights

Data Intelligence Before Artificial Intelligence – It’s Time for a Data Reality Check

Uttam Channegowda
Data and AI coming together
Consulting Office Space Abstract
One of the biggest challenges facing companies in their AI journey is figuring out where to start. With all the promise and hype, it seems like it should be easier. But anyone who’s tried realizes it isn’t.

What’s the hardest part? According to a recent survey, 46% identified “data quality” as the greatest challenge to realizing Generative AI’s potential in their organizations.

It’s Time for a Data Reality Check

It’s important to remember that Artificial Intelligence is just another tool available to help move your business from problems to solutions – a big, powerful one – but only if it’s given trustworthy, well-organized, and accurate data to work with. That’s why it’s never been more important to create trusted data. If you feed AI bad data, AI will accept it, multiply it, and give you worse results. 

Clearly there are benefits to getting AI-ready, and more and more companies are seeing their performance boosted by doing so. But if companies want to successfully use data analytics and artificial intelligence in core aspects of their business, they need to be honest about the quality of their data.  

Companies need to treat their data as an asset and not just lean on IT to manage the numbers. In many organizations, making this adjustment can be just as much a culture shift as it is a process change in how the data is collected and managed.  

To get the most out of AI and data, it’s time for a Data Reality Check. 

What Does it Mean to Treat Data as an Asset? 

Your data has value – clean data has even more value – and you should think of it as a valuable part of your business. As with anything your company produces, you could box your data up and sell it. Or – even better – invest in it as a strategic asset to make better decisions across business units.

Collaboration Between IT and Business Units 

There are four essential components to turning your data into a strategic asset, and we’ve covered them in depth here. Arguably the most significant adjustment companies need to make is to stop thinking of data as something that resides in an IT silo. Inputting the right data into the right place is a company-wide responsibility, which means IT and business SMEs must work hand-in-hand. 

Enabling Business Ownership of the Data 

Is your business creating bad data? Are your teams entering data in an ad hoc way? Until each appropriate team takes responsibility for their data – not just the IT team – nothing will change. 

Let’s think of IT as running a warehouse. In this warehouse, their job is to ensure that data is placed on the right shelf and shipped out correctly. But sales, marketing, and every other team is responsible for ensuring the right data is sent to the warehouse in the first place. If the business inputs incorrect data, IT won’t know it’s incorrect, and they will ship out that inaccurate data, leading to bad decision making. 

By enabling ownership across teams, you ensure that people who are involved throughout the process have the chance to recognize incorrect data and question the output; both things AI can’t do.

Start with Better Data Inputs

Let’s say that a company’s customer service team was focused on the metrics of call time, and they received good marks if they could solve a problem in the shortest amount of time possible.  

When the company looked at call time data, they were pleased because the time on call was going down. However when analysts looked closer, they realized most callers all had problems with the first option listed on the drop-down menu of issues used to track why a customer was calling.  

Turns out, to get shorter call times, the reps were picking the first and easiest problem, ushering the callers off the phone with expedience, but not really helping them. This led to inaccurate data, hindering product improvement and client satisfaction. 

Just because you have data doesn’t mean you have the right data. Feeding that data into AI, which can’t tell the difference, won’t get you the right results.

Take Your First Steps to Get Your Data AI-Ready

Preparing your data for AI is a crucial step toward leveraging its full potential. By implementing a structured approach, you can ensure your data is accurate, reliable, and ready to drive meaningful insights. 

Establish a Data Stewardship Program

Establishing a Data Stewardship Program will ensure your data is accessible, usable, safe, and trusted. Start by identifying data owners within business teams, such as Sales, Marketing, Customer Service, and CRM or tech platforms. A RACI matrix of all the data within your organization is an effective initial step to establish points of contact and accountability. This approach ensures that everyone knows what data they are responsible for and understands the importance of their role in maintaining data integrity.

Increase Data Literacy

Achieving data literacy starts with clearly defining data assets in straightforward, concise language. Specify what the asset is, highlight its key characteristics, and explain its function. Make sure each definition is comprehensive and can be understood without needing any prior institutional knowledge. This will ensure your data’s longevity, regardless of positional turnover.

Get Your Data Fit for Purpose

Giving your data a purpose ensures it is fit for AI solutions. By demonstrating clear use cases for your data analytics that identifies specific, quantifiable business problems you’re trying to solve, you can then define the critical data elements needed to solve those problems.

At this point, your data-literate data stewards can validate your data against the use case and make sure it is trustworthy and fit for its purpose – just like in the customer service story above. Then, IT can make your data more intelligent by categorizing it correctly and collecting metadata – because that’s their job in the “warehouse.”  

Use AI to Get Your Data Ready for AI

Once your plan is in place, you can use AI to get your data ready for AI. Tools like Databricks, Snowflake, and Microsoft’s Power BI can help organize and prepare your data by providing smart metadata tagging. However, it’s still up to you to ensure the accuracy of that tagging. 

Other ways to leverage AI to accelerate data quality improvements include executing queries to check data quality, profiling data to identify anomalies, and building faster pipelines to gather the right data. 

Prepare for AI with the Help of an AI Enablement Partner 

Even if you haven’t fully committed to AI, the good news is you’re not too far behind. Plenty of other companies are still in the exploratory phase. No matter how prepared for AI your company is, an AI enablement partner can help get your data ready to maximize the benefits of AI. 

As an AI enablement partner, G2O helps companies build a data foundation that can realize AI and data’s full potential for positive business outcomes. We’re not here to sell you fancy objects or a personal LLM; we want to solve your real business problems and get you ready for AI with our: 

  • AI Readiness Assessment: Identifying high-potential use cases for AI and data analytics supporting business goals, determining if AI or other tools can accomplish them, identifying necessary data sets, and understanding ROI. 
  • AI Enablement Framework: Developing a core data framework to ensure AI applications consume trusted data. 
  • AI-Enabled Analytics: Simplifying data exploration, categorization, testing, and self-service analytics. 

Unlock the potential of successful AI solutions with quality data. Reach out to us for a conversation about how we can help.