Data management

Our experts can help you consistently hit your target

Making strategic decisions based on data with quality control issues can be like trying to hit a target while blindfolded - it's easy to completely miss the mark and damage something else instead. It's critical to establish good data quality management and data governance processes but it doesn't happen overnight - it's a journey, and one that's very familiar to the Datasight® team. Whether you're looking to optimise your existing data management strategy, or need guidance to build one from scratch, we can help you find the right fit for your data.

Data management involves every aspect of the data lifecycle

Data management is the process of collecting, storing, protecting, and delivering high-quality data over time. Precise and consistent data management practices enable better decision-making, support compliance commitments, and reduce costs associated with errors and inefficiencies.¹ Without a good data management system in place you can't prevent poor data from filtering into clean data sets and lowering the overall data quality. In this situation, data assets quickly become liabilities. Datasight® specialises in helping clients avoid that scenario by developing effective data quality management systems and data governance frameworks.

Data quality management systems maintain high data quality over time

Data quality management refers to the processes designed to ensure your data is accurate, complete, consistent, timely and relevant.² It typically involves multiple stages designed to progressively reveal and resolve data quality issues before revealing the next layer of issues. The process usually starts with profiling the data, followed by cleaning and transforming data as needed, matching data records, removing duplications, and checking data correctness through detailed validation rules. Regular data audits are also commonly conducted to find poor quality data that slipped through the cracks, and prevent it from happening again by tightening the data validation rules.

Data governance frameworks clarify roles to make sure data assets are accessible but still protected

Data governance is like having an air traffic controller for your data that coordinates people, processes, and technology to keep everything moving smoothly without any crashes. It makes sure your data is reliable, secure, and available when you need it.³ While it shares a common goal with data quality management — to provide trustworthy data — data governance has a broader focus. It’s not just about cleaning up the data; it's about managing the data through its entire lifecycle.

A data governance framework is a useful document that defines who has the power to take action on specific data issues, what type of actions are allowed, individual and team responsibilities, and procedures for making data-related decisions. Understandably, there is no one-size-fits-all solution that works for everyone; each framework is tailored to the specific needs and goals of each organisation. However, there are some common strategies for developing and implementing a data governance framework such as setting up a data governance team, choosing data stewards, and developing data policies and procedures.⁴ Contemporary tools including data catalogues, data dictionaries, and data governance software can also help manage your data more effectively.