Streamlining Data Management: Methods to Lower Expenses and Simplify Complications
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In today's digital age, data has become an essential asset for businesses worldwide. However, the sheer volume of data being generated can lead to significant challenges, particularly when it comes to managing data overhead. This issue, characterised by unused data leading to inefficiencies, costs, and complexities, can be addressed through a combination of strategies in the areas of data governance, data lifecycle management, de-duplication, and data observability.
Data Governance Strategies
Implementing a strategic framework that sets clear rules and responsibilities for data quality, security, and compliance across the organisation is crucial. This framework should establish clear roles such as data owners, stewards, and governance councils to enforce policies collaboratively while balancing central oversight with domain-specific expertise.
Dynamic data access controls, such as attribute-based access control, can help protect sensitive data, enforce zero-trust principles, and apply just-in-time access provisioning. Embedding data governance into the company’s values and securing leadership buy-in, supported by data governance steering committees, is essential for sustaining momentum. Leveraging AI, such as Large Language Models, can enhance metadata governance by improving data definitions, readability, and auditability.
Data Lifecycle Management
Comprehensive data quality monitoring that spans the entire data lifecycle, including real-time quality checks, continuous profiling, and automated remediation of errors, is essential. Data lineage tools can help trace data flow and changes across systems, aiding in diagnosing and resolving quality issues promptly. Policy orchestration platforms can enforce governance rules automatically throughout data processing pipelines, ensuring integrity and consistency from creation to archival or deletion.
De-duplication Techniques
Robust de-duplication processes during data profiling can identify and resolve duplicate records, maintaining referential integrity and prioritising fixes based on business impact. Automated de-duplication can be integrated into data quality scorecards and workflows, minimising manual intervention and preventing duplication propagation downstream. Treating data as a product, assigning accountability to data owners who ensure datasets are reliable, well-documented, and improved iteratively, discourages duplication.
Data Observability
Continuous monitoring systems provide real-time insights into data quality, freshness, and operational metrics, helping preemptively detect issues. Establishing feedback loops from data consumers to data owners addresses quality issues at data creation points and adjusts governance policies accordingly. Audit logs and version control track data changes with transparency, facilitating troubleshooting, compliance, and rollback capabilities when errors or duplications arise.
These integrated strategies create an environment where data is trusted, secure, accessible, and well-managed, effectively reducing overhead caused by poor quality, duplication, and fragmented data management efforts.
Automated cloud storage can exacerbate the problem by storing large amounts of underutilised and unused data, offering no return on investment. Disorganised local storage can result in version sprawl and fragmented files, making search, backup, and security efforts more difficult. Regular audits and the use of Data Observability and FinOps tools can help monitor data growth, usage, and cost, optimising data usage and controlling cloud storage expenses.
Employing consistent strategies to manage data overhead improves efficiency, reduces storage demands, enhances data quality, and increases accessibility. Data Lifecycle Management (DLM) automates policies for backup, cold storage, and purging, making it easier to manage data.
Scott Francis, Technology Evangelist at PFU America, Inc. and Ricoh Document Scanners, emphasises the importance of managing data overhead for modern organisations. Organisations can reduce data overhead through regular audits and the strategic deletion or archiving of obsolete information.
- Scott Francis, as a Technology Evangelist, underlines the significance of managing data overhead for modern organizations, asserting that they can reduce data overhead by conducting regular audits and strategically deleting or archiving obsolete information.
- To effectively reduce data overhead, organisations can employ Data Lifecycle Management (DLM) strategies that automate policies for backup, cold storage, and purging, ensuring data is handled in an efficient and cost-effective manner.
