Every company has data quality issues. There's no shame in it.
What matters is how organizations handle them and what they cost the business.
For over two decades, Bespoke Analytics has helped businesses in Bermuda transform their data into actionable insights. One truth stands clear: data quality problems affect every organization, regardless of size or maturity level.
"It's pervasive. It doesn't matter the organization in so far as how advanced they are, how big they are. Everybody's got data quality issues. It's a fact of life. It's nothing to be ashamed about. It just happens," explains Paul McLeod, President of Bespoke Analytics. "But the challenge is that data quality costs companies money."
The Real Business Impact
Poor data quality hits the bottom line in three ways:
Lost revenue opportunities
Inefficient processes that inflate costs
Decisions based on incorrect information
Financial: Poor customer interactions due to inaccurate data and inability to provide unified billing to customers.
Risk/Compliance: Lack of compliance with regulations and potential privacy or data protection violations.
Productivity: Increased need for reconciliation of reports and poor supply chain management.
With artificial intelligence becoming mainstream, these issues compound.
As Paul McLeod, Founder at Bespoke Analytics points out:
"AI is coming. If you've got underlying data quality issues in your systems, what AI does is take your data and gives you its opinion based on the data it's got. If your data's not good, you're not going to get as much out of these tools as you could."
The Three Core Data Quality Challenges
Through work with financial services, insurance, and retail firms, organizations consistently face three main types of issues:
Missing Data: Simple omissions like blank email fields that break customer communication
Incorrect Source Data: Misclassified information that leads to wrong business decisions. As McLeod illustrates, "Somebody's putting in a loan into a banking application and they categorize a corporate loan as a personal loan. That filters up to reporting. You've got executives looking at their loan book and saying, 'we're doing really good in our personal loan portfolio.' It's because you've got a $100 million corporate loan in there."
Integration Issues: Discrepancies between systems that create reporting headaches
Why Manual Processes Fail
Most organizations rely on manual data quality checks - running reports, sending emails, following up on fixes. Paul describes the common scenario:
"You're writing queries and identifying issues, then the question comes from the end user: 'How can we track this?' We do some queries, hand it off, give it to people. The problem is these are all manual processes. You're writing a report, figuring out who to send it to, emailing it to someone saying, 'Hey, we've got some problem with the data here.' Then you go back to what else you're doing, try to remember if they got back to you, send a follow-up email... Each time everybody's doing it from scratch."
This approach:
Wastes valuable time
Creates inconsistent processes
Makes it impossible to measure improvement
Erodes trust in data
A Practical Approach to Better Data Quality
The solution isn't trying to eliminate all errors - that's impossible. Instead, organizations should focus on:
Creating Clear Ownership: "You almost need to define an approach of how you're going to handle data errors in your environment. You need an ownership mechanism - this is how somebody needs to have ownership of the process for managing data errors in the systems."
Implementing Automated Tools: Through Bespoke Analytics' partnership with TimeXtender, organizations gain access to advanced data quality tools that:
Automatically detect issues before they affect business operations
Route problems to the right people for quick resolution
Track correction rates and improvement over time
Build trust in data across the organization
Building Trust Through Transparency: "When you get to that more mature environment with your data landscape, the end users need to trust that the data they're getting, if they're going to rely on it, is good,"Â Paul emphasizes*. "If you don't have that stuff in place and you're not good at correcting those data issues, it can destroy the trust in those data repositories, and all the money and time and effort spent building them can be undermined by end users building their own reporting infrastructure because they don't trust what comes out of the data warehouse.*"
Real Results in Action
Organizations achieve significant improvements through automated data quality management:
An insurance sector client cut audit time by 63%
A banking client identified $200,000 in missed billing opportunities from misclassified accounts
Multiple clients have reduced their data correction cycles from weeks to days
The Path Forward
The journey to better data quality requires collaboration between IT and business teams. As McLeod notes:
"IT can put that stuff together and say, 'these are the problems.' But unless the business steps up and says, 'this is important and needs to be addressed,' then it's too easy to ignore."
Bespoke Analytics helps organizations move from reactive to proactive data quality management. The approach includes:
Assessment of current state and critical data flows
Implementation of automated monitoring tools
Development of clear ownership and resolution processes
Ongoing measurement and improvement tracking
Taking the First Step
Organizations don't have to accept poor data quality as "just the way things are." The process starts with a simple assessment of the current state. Bespoke Analytics helps businesses:
Map critical data flows
Identify high-impact quality issues
Build a practical improvement plan
Implement automated monitoring
Ready to turn data quality challenges into business advantages? Contact Bespoke Analytics to discuss specific situations and solutions.
Contact Bespoke Analytics today for a free consultation on improving your data quality management.