Data Maturity

Is your data ready to power AI?

Data Maturity assesses the quality, accessibility, and governance of your organisation's data assets. AI is only as good as the data it learns from — garbage in, garbage out is not a cliché, it's a guarantee.

Why It Matters

Only 32% of organisations have high data readiness for AI (Cisco AI Readiness Index, 2024).

80% of AI project time is spent on data preparation, not model building.

Poor data quality costs organisations an average of $12.9 million annually (Gartner).

Industry Benchmarks — Data Maturity

Finance5.5/10
Technology5.4/10
Media4.8/10
Professional Services4.3/10
Retail4.2/10
Healthcare3.5/10
Manufacturing3.2/10
Government3.0/10
Education2.8/10

Common Gaps

Data silos across departments

Marketing, sales, and operations each have their own data stores with no integration.

No data quality standards

Inconsistent formats, missing fields, and duplicates make AI training unreliable.

No data governance framework

Who owns the data? Who can access it? What are the retention policies?

How to Improve

1

Audit your top 10 data sources for quality and accessibility

High impactMedium effort
2

Implement a data catalogue (know what data you have)

High impactMedium effort
3

Define data ownership and stewardship roles

Medium impactLow effort
4

Start a data quality scorecard — measure and improve monthly

High impactLow effort

Recommended Tools

Data Platform

Snowflake / Databricks

Unified data warehouse for analytics and AI workloads.

Data Transformation

dbt

Transform raw data into clean, tested, documented datasets.

Data Quality

Great Expectations

Automated data validation and quality testing.

How does your data maturity measure up?

Take the free AI Readiness Quick Scan to see your score across all 8 dimensions, with industry benchmarks and personalised recommendations.

Take the Free Quick Scan

Explore Other Dimensions

Strategy & VisionTechnology StackTalent & SkillsProcess ReadinessGovernance & EthicsCulture & ChangeFinancial Readiness