Preparing Your Company’s Data for Artificial Intelligence (AI)
Getting a great Artificial Intelligence (AI) agent or model for your business will be cool… But you really need to sort your data out first before you can harness its power, there’s one vital ingredient you need to get right: your data.
In this post, we’ll walk you through the essential steps every business should take to prepare their data for AI. Whether you’re planning your first AI project or want to improve your existing processes, this guide will help you get AI-ready.
1. Start With a Strategy
Before diving into the data, be clear on what you’re trying to achieve.
Ask yourself:
– What problems are we looking to solve with AI?
– Where can AI add the most value?
– Which teams or processes will benefit the most?
Aligning your data work with business goals ensures your AI efforts deliver meaningful impact.
2. Identify and Audit Your Data Sources
Create an inventory of all the data your business collects — from CRM systems to spreadsheets, marketing platforms to customer support logs.
Audit each data source by checking:
– What kind of data is collected?
– Where is it stored?
– Who has access and ownership?
– How frequently is it updated?
You’ll likely uncover hidden silos and underused assets that could be valuable for AI.
3. Clean and Standardise Your Data
Messy data leads to poor AI outcomes. Clean, consistent data is crucial.
Focus on:
– De-duplication: Eliminate duplicate records.
– Correction: Fix typos, errors, and missing values.
– Standardisation: Align formats (e.g. dates, currencies, units).
– Enrichment: Fill gaps using trusted third-party data.
Use data wrangling tools or scripting (Python, R) to streamline these tasks.
4. Centralise Your Data
Fragmented data can derail any AI project. Consolidate your data into a single, scalable platform.
Options include:
– Data warehouses (e.g. BigQuery, Snowflake): Ideal for structured data.
– Data lakes (e.g. AWS S3, Azure Data Lake): Suitable for large, unstructured datasets.
This step is essential for building reliable AI models and maintaining performance at scale.
5. Put Data Governance in Place
AI needs not just clean data, but **trusted** data. That’s where governance comes in.
Create clear policies around:
– Data access and ownership.
– Compliance with data regulations (e.g. GDPR).
– Version control and audit trails.
– Data security and privacy.
This builds confidence in the data and keeps your AI initiatives compliant and sustainable.
6. Label and Annotate Data for Machine Learning
If you plan to use machine learning, you’ll need **labelled datasets**.
That might mean:
– Tagging product images with categories.
– Labelling customer support emails by topic or outcome.
– Annotating feedback with sentiment scores.
You can do this manually in-house, outsource it, or use automated annotation tools.
7. Keep Data Quality High
AI is not a one-off project — it’s an evolving system.
To maintain performance, you’ll need to:
– Monitor for data drift (i.e. changes in patterns or input quality).
– Re-train AI models as new data becomes available.
– Set up regular data quality checks.
Treat data maintenance as an ongoing process, not a one-time fix.
Final Thoughts
Preparing your data for AI isn’t just a technical task — it’s a strategic move. Clean, well-structured, and governed data lays the foundation for any successful AI initiative.
At High Digital, we help businesses get AI-ready with expert data strategy, infrastructure planning, and smart implementation.
**Need help preparing your data for AI? Get in touch with us to find out how we can support your digital transformation.**
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