What exactly is data debt?
Data debt is a form of technical debt that occurs when teams fail to classify, clean, and categorize their data. It reduces productivity and increases computational expenses for the organization. Using proactive data governance technology that alerts you when and what vital data is outdated, confused, or undocumented is the best approach to keeping ahead of data debt.
These are six steps data teams must follow to avoid or eliminate data debt issues
1. Build governance within analytics capabilities.
DevOps teams should use left security and quality assurance procedures to tackle poor code quality, defects, and security vulnerabilities. Data governance protocols should be included in analytics, ML models, and data pipelines. The risk of data debt may be mitigated by managing data sources, models, and lineages using technologies such as data catalogues, lineage tools, and metadata management systems. Issues may be found and fixed with the use of data quality techniques like profiling and cleaning. Best practices must also be used by data teams, including selecting access patterns, upholding governance, integrating version control, and separating derived data from data with a source of truth.
2. Empower the data and analytics teams with supervision
Data governance standard procedures and technology should be used by agile data teams to oversee continual progress. Establish roles for data stewardship to maintain data models, guarantee correctness, and reduce data debt, such as those of data architects, analysts, and engineers. Organizations can decrease risk, save expenses, boost productivity, and lay the groundwork for development by putting a top-down plan into place and creating a flexible model.
3. Create trust benchmarks that promote debt relief
Data teams should increase data credibility by categorizing and analyzing use levels. They should use metrics to assess data quality such as correctness, completeness, consistency, speed, uniqueness, and validity. Data satisfaction may be calculated by surveying leaders and users and providing a data satisfaction score.
4. Make data history and reliability a priority
Data debt may hamper decision-making by influencing KPIs for data usage, quality, and satisfaction. Data operations teams must reverse engineer data lineage and modifications from source to destination to overcome data debt. Introducing data observability at every stage of the data process can change data lineage and enable business users to explain data flows. Grant Fritchey, Redgate Software’s DevOps advocate, underlines the significance of data reliability in communication, audit trail support, and compliance audits. Redgate Software’s Jeff Foster, director of technology and innovation, underlines the necessity of data reliability in assuring compliance and ethical data usage. Data operations will become increasingly crucial in comprehending the data sources employed in large-scale machine learning models as AI/ML pipelines get more complex.
5. Be wary of data that has been locked within closed systems
If data management systems are unable to satisfy company needs, it can lead to data system debt. To avoid data being held captive, Erik Bledsoe, content marketing manager at Calyptia, recommends adopting vendor-neutral solutions using open standards. To eliminate lock-in, automate data removal from SaaS apps, and leverage centralized information platforms for reporting and analytics, such as data lakes or warehouses. Archiving older data helps meet compliance standards without overloading visualization and analytics tools.
6. Choose the best data management solutions for every type of data
Architects must think about the best database and data management systems, such as graph, key-value, columnar, and document technologies. Inadequate platforms might lead to data debt problems. Graph technology can assist firms in reducing data debt by allowing them to connect data freely and intelligently aggregate data. Graph databases have the potential to be adaptive and aid businesses with more intelligent data connectivity.
Conclusion
Data teams must manage data debt effectively as companies strive to be more data-driven in decision-making and create models using machine learning for a competitive edge.
