The Challenge
Why Poor Data Undermines Analytics and AI
Duplicates, gaps and inconsistent definitions lead to wrong reports and unreliable AI results.
Is This Relevant?
Is This Use Case Relevant to Your Organization?
The people, moments and signals that usually bring this need to the surface.
Relevant Teams
- Data and analytics teams
- Business data owners
- Finance and operations
Common Triggers
- Reports that people do not trust
- An AI or analytics initiative
- A system migration
Warning Signs
- Duplicate customer or product records
- Manual data fixes before every report
- No data owners
Typical Environments
- Business applications, databases and spreadsheets
- Cloud and on-premises data platforms
Benefits
What Better Looks Like
Intended benefits. Actual results depend on your environment and starting point.
Our Approach
A Practical Approach
Built in increments, so useful reports arrive early and improve over time. Each stage is tailored to your environment.
Assess
Review data sources, reports, definitions and data quality.
Prioritize
Rank what you find by business impact, effort and risk, and agree where to start.
Design
Agree the data model, definitions and priority reports.
Implement
Connect sources, build the data platform and automate reports.
Validate and Optimize
Check numbers against source systems with business owners.
Solutions
Solutions That Can Help
The solutions that address this use case, starting with the primary one.
Services
How Signisys Can Support You
The services that deliver the approach, from the first assessment to ongoing improvement.
Technology
Technology Options
Where technology fits in the approach. Specific platforms and partner status are confirmed for each engagement.
Before You Start
What to Consider Before Getting Started
A little preparation makes the first assessment faster and its findings more reliable.
- Which data matters most
- Quality rules for each data domain
- Who owns and fixes each domain
- How quality will be monitored
- Who will own the work and act on recommendations
- Engagement model and ongoing support
Measuring Success
How to Measure Success
Illustrative measures. Agree a baseline first, and keep targets separate from measured results.
Data Quality Score
Records passing agreed quality rules.
Duplicates
Duplicate records in key data.
Manual Fixes
Time spent correcting data.
Domains with Owners
Data domains with an accountable owner.
Our Approach
How We Deliver Results
How Signisys works, from the first assessment to handover and beyond.
FAQs
Frequently Asked Questions
Common questions about this use case.
How do we know our data quality is a problem?
If reports need manual fixes, people distrust the numbers or AI results are unreliable, data quality is likely the cause.
Is data quality a one-off clean-up?
No. A clean-up helps, but rules, ownership and monitoring keep quality from slipping again.
What information is needed to get started?
The data that matters most, known quality issues, the systems involved and who uses and owns each data set.
Which environments does this apply to?
Business applications, databases and spreadsheets, on cloud or on-premises data platforms.
How is progress measured?
Agree a baseline for measures such as data quality score, duplicates, manual fixes and domains with owners, then track them over time.
Related Content
Related Outcomes, Use Cases and Guides
Where to go next: the outcome this use case supports, nearby scenarios, technology partners and practical guides.
Business Outcome
Put AI and Automation to Work
Problem Explainer
What Causes Poor Data Quality? Common Problems and Warning Signs
Coming soon
How-To Guide
How to Improve Data Quality for Analytics and AI: A Practical Step-by-Step Guide
Coming soon
Checklist
Improve Data Quality for Analytics and AI Checklist: What IT Teams Should Assess
Coming soon
Decision Guide
How to Choose the Right Approach to Improve Data Quality for Analytics and AI: Options and Trade-Offs
Coming soon
Improve Your Data Quality
Share your current challenges and priorities with Signisys to explore an approach suited to your environment.