AI-driven insights start with clean data

The saying "you are what you eat" applies just as much to AI models as it does to people.

AI is good at eliminating human error by looking at raw data and identifying patterns. But at the end of the day, it applies its logic to the data you feed it.

If your data is spread across disconnected systems, with inconsistent labels and formats, the insights you get from AI will be just as messy. To get meaningful results, your data needs to be accurate, consistent, and all in one place.

Here's how you can get there:

1️⃣ Audit data sources and definitions.

Start by listing every system where financial data lives, like your ERP and CRM. Then, document how key data points, like departments, accounts, and regions, are currently labeled in each one. This will give you a clear view of where inconsistencies may lie.

2️⃣ Establish data governance standards.

Next, define how data should be labeled. Set clear naming conventions, formats, and structures (e.g., whether departments are labeled as "Marketing" or "MKTG"). Assign someone to manage these standards and ensure consistency across teams.

3️⃣ Map and consolidate your data.

Bring all your data into a central repository. Then create rules to transform and align inconsistent formats, and build a reconciliation process that ensures everything rolls up cleanly. This turns fragmented inputs into one consistent language AI can understand.

4️⃣ Introduce automation.

Before layering in AI, start with basic automation. Focus on routine, repeatable processes, like syncing systems or checking for missing inputs. Automation reduces manual work and makes your data environment more stable and scalable.

Ready to take this process a step further?

Dive deeper into the connection between data quality and AI, explore key components of clean data, and more. Read the full guide here.

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In this exclusive walkthrough with Cube's CPTO, Suresh Bala, you'll see how to:

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Watch the full fireside chat on demand.

Stories worth bookmarking

1. CFOs are already data leaders and perfectly positioned for AI. (CFO.com)

2. AI models analyze data using 4 key approaches. (Corporate Finance Institute)

3. These are the top 4 ways CFOs can prepare for 'The Year Of Agentic AI'. (Forbes)

4. From automation to AI: CFOs are evolving. (Cube)

Navigating a strategic finance transition? We can help. Book a free consultation to diagnose where your team is now and how to plan for tomorrow.

P.S. Our Strategic Finance Hierarchy of Needs guides you through the strategic finance transformation process step by step.

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