While most of the businesses want to be agile, they are struggling to leverage their data and include meaningful, accurate data insights into their day-to-day business processes to improve business performance, and benefit from the power of their data.

This is leading to a large issue with data debt. The Data Debt is like Technical debt. It can be measured as the cost associated with mismanaging data and the amount of money required to fix the data problem.

Many companies are not fully seeing the ROI or expected benefit from some of the data investments they are making, and a backlog of data debt is affecting any new initiatives in data management. To address the data debt challenge, organisations must invest more in data quality. Without data quality standards, checks, and processes to manage how data is captured, maintained, and leveraged, data debt will just continue to accrue and grow.

Some of the causes of data debt are:

• Lack of Data Fabric layer: to eliminate data copying and offer easier collaboration and faster access to data
• Data silos: Data duplication, storing data across multiple areas within the organization in different formats, that are difficult to access and understand.
• Limited data sharing and collaboration: Poor communication between teams and misalignment with requirements and business expectations, is causing more bugs, leading to wasted effort in finding and fixing problems.
• Poor Data Management: Sensitive data can create risk for businesses due to misuse, poor quality or compliance issues, leading to data breaches and huge fines from regulating authorities. Storage and infrastructure costs are also increasing, as well as admin resources overhead.

For organizations to address the challenge of data debt, everyone has to take ownership of data. This involves not only investment in technology but also people and cultural changes.

Organisations need to measure their current costs and risks associated with poor data quality to ensure these costs aren’t contributing to their Data Debt.

Implementing Data Governance and DataOps principles is an effective way to pay down Data Debt and avoid accumulating it in the future. ConnectIQ is a cloud-native DataOps and AI platform that combines AI-enabled data-driven automation, data governance, data protection, delivery and sharing of high-quality data across the business. ConnectIQ empowers banks to unlock more value from their data and become data-driven businesses.

These capabilities help increase efficiency, productivity and data quality, in order to provide a self-service automated data pipeline to the right people at the right time from any data source.