BI Before AI: Why It’s High Time to Get Your Data Right

AI has dominated discourse across virtually every sector of our lives in 2026. It’s been no different in the world of higher ed technology. 

During Slate Summit in Nashville this summer, Technolutions touted the capabilities of Slate AI to transform the way we work in admissions, advancement, and student success. After launching Agentforce, Salesforce has spent the last year promoting the myriad ways its AI agents can make institutions more efficient. And Ellucian continues to roll out new AI features across its platforms.

We’ve seen early adopters race to realize quick wins with AI. In the process, many were confronted with an age-old truth dressed in new clothes:

AI is only as good as the data you feed it.

And it’s been our experience at SIG over the course of almost 40 years serving more than 1,000 colleges and universities, that many institutions are working with dirty data.

Before we can fully leverage AI in our work, we need to ensure that our data is clean. And to ensure that our data is clean, we must shift our focus from AI to BI (Business Intelligence). 

Business Intelligence: A Definition (And Data Governance: A Place to Start)

Business Intelligence refers to the tools, technologies, and practices used to collect, integrate, analyze, and present raw institutional data in ways that drive outcomes on behalf of, in our case, a college or university.

If that seems like a broad definitional net to cast, let’s focus on the foundational element of BI that we must get right first: data governance.

Good data governance will achieve the following:

• Define, approve, and communicate data strategies, policies, standards, architecture, procedures, and metrics
• Track and enforce conformance to all of the above
• Sponsor, track, and oversee the delivery of data management projects and services
• Manage and resolve data-related issues
• Understand and promote the value of data assets

BI and Data Governance are not Nearly as Sexy as AI

But truthfully, feeding AI dirty data is a recipe for disaster.

For example, using AI to create an enrollment stage visualization won’t be accurate (or useful) if different departments have varying definitions of “enrolled.” In addition, creating a marketing strategy with AI would be difficult, and incomplete, if your advertising data isn’t connected to your admissions or fundraising data.   

Getting your data in order will help you realize immediate efficiencies and improvements in your day-to-day work that will set you up for long-term success — and for the optimization of all the shiniest AI tools Salesforce, Slate, and Ellucian have to offer.

Over the course of the next month, we’ll be rolling out insights, advice, and expertise centered on helping you get your data in order, as well as tactical pieces that share hacks for using, wait for it, AI in the process of data cleaning. So meta.

If you need help with data cleaning, data governance, or any BI-related activities that will help prepare you to realize the promise of AI, SIG can help.