The most overlooked part of a strong AI strategy isn’t algorithms or dashboards – it’s your data foundation.
At Stratus, we’re passionate about helping clients uncover profits through data and AI. And as AI becomes more effective and more accurate, 92% of companies say they plan to increase their AI investments over the next three years, according to McKinsey.
But here’s the hard truth: Most companies aren’t ready for AI – not because they lack the tools or the talent, but because they’ve skipped a critical first step.
The first question we always ask clients is: Is your data actually ready?
Teams usually come to us with goals that seem straightforward. They want to leverage predictive analytics, utilize real-time metrics, or automate their processes. In short, they want what everyone right now wants – to use AI to do business better.
But quality AI depends on quality data. AI learns from data, and if you have incomplete or inaccurate data, then no matter how sophisticated the AI is, it’s going to give low quality results. “Garbage in, garbage out.”
And in many cases, a company’s data is siloed, soiled, and inconsistent. Without a solid and organized data foundation, any AI strategy is just a house of cards.
A Solid Data Foundation
Here’s what we mean by a solid foundation:
- Can your sales, operations and product data talk to each other?
- Can you trace a customer from lead to purchase to renewal?
- Do you have your ABCs—the essential data elements to answer your business questions?
Here’s what we look for when we review data:
A – Access
You can’t analyze what you can’t access.
Do you have access to the data? Are there spreadsheets, databases, or APIs in place? Is it stored in systems you can query, extract, or integrate from (i.e. ERP, CRM, GL, HRIS)? And does the data even exist? Some data can’t be accessed because it doesn’t exist and needs to be instrumented. In practice, it’s not a whole dataset that’s missing, but key fields or specific pieces of information that’s not tracked, or not tracked robustly.
B – Baseline
Without shared definitions, your numbers won’t mean the same thing to everyone.
Do you have clear definitions for key metrics (i.e. revenue, gross profit, churn)? Do you understand the baseline data model – where the data comes from, what it represents, and how it’s structured?
C – Cleanliness
Dirty data = bad AI, no matter how nice the dashboard looks.
Is your data clean and consistent? Are there missing fields, duplicate entries, mismatched formats, or conflicting records across systems? Does the data reflect the true reality of your customer, business, and product?
Data In Action
Here’s an example:
A well-known snack manufacturer came to us for help with their data management and reporting. They knew they were missing key opportunities, and their data was getting in the way.
They faced a number of obstacles that were limiting their ability to make decisions and grow. There was no centralized data source. Different groups within the company relied on separate data sources, leading to discrepancies and the inability to trust or use the data. Leaders were drowning in data reports, many of them outdated, redundant, inaccurate, and inconsistent across departments.
Stratus implemented a modern data warehouse solution that created a “single source of truth”, integrating disparate data sources. We eliminated manual reporting, providing real-time dashboards that were accessible across departments. Ultimately, the company saved more than 1,000 hours across its IT team, executives, and fund sponsors, and they were able to identify lucrative key vendor opportunities.
The Power of Good Data
In order to fully take advantage of AI and generate value – and ultimately profit from it – you need trustworthy, organized data.
Here’s how our process works:
Track the data → Clean the data → Organize the data → Curate the data → Use the data.
For example, if you have a physical product company and want to forecast demand (who will buy, how much, and where), you’ll need to build an algorithm. That starts with understanding purchase behavior and the factors that influence it . Are you capturing marketing campaigns, sales calls and meetings, conference attendance, holiday calendars, and promotional calendars?
Without this foundational data, there’s nothing for an algorithm to learn from. You need to start by tracking the right behaviors, clean it for accuracy, organize that data across systems, and make it usable. Only then can you move from gut feeling to predictive insight.
Ultimately, AI tools and custom dashboards can be transformative for a business, no matter what industry. But first you need trusted, consistent data so you’re building your business on solid ground and not on sand.