Are silos hindering your data quality goals?

Data quality is one of the biggest daily obstacles faced by organisations. From simple considerations, such as ensuring the recording of the date for a trade, or more complex multi-column checks on the validity of customer metadata, data quality affects decision making, compliance, customer satisfaction and business success.‍

‍

How does poor data quality arise?

‍

‍One factor that can severely affect data quality, and further compound the usual challenges to successful data governance, is the siloed storage of data which is typical for large organisations.

‍

All data in an organisation undergoes a journey from source and producers to internal or external consumers, during which it is transformed from raw, captured data into conformed data assets, used for decision making and regulatory reporting.

‍

In a siloed environment where duplication across data, processes and people is common, keeping consistency and accuracy across the data journey is a complex and onerous undertaking. Changes upstream may not be effectively recognised downstream, and data quickly becomes out-of-date and unreliable and loses its value as a trusted basis for decision-making.

‍

From a data governance point of view, the siloed approach increases the risk of inconsistent implementation of policies and standards, diverging definitions for the same data and inconsistent quality rules and fixes which ultimately undermine the semantic congruency of the data. Teams within the organisation lack the overall visibility of the data, what it means and how and why it has been changed and whether it is fit for their intended use case.

‍

These factors ultimately impact the business ability to absorb change and evolve in line with the continuously expanding regulatory, environmentally, socially and governance requirements.

‍

Tooling provides a solution

‍

‍Managing and mitigating these challenges can be daunting without the use of tooling. Having well defined data catalogues and quality rules that can be applied across a variety of sources to ensure data is exact, current and consistent, and being able to capture and track issues back to source to fix them are all areas where tooling can help.

‍

To learn more about the Holley Holland approach to data quality and find out how we have helped organisations with these and other issues, please get in touch with Scott Anderson and Steven Whaley.