The existence of a robust evidence-base to support decision making and performance monitoring is a fundamental element of a Data Governance Initiative. However, there are a number of challenges in obtaining and maintaining a robust evidence-base, including:
There is often no clear and unambiguous picture of the data held by the organisation.
The absence of a common overview of the data makes it difficult to get value from the data i.e. there is no common data model.
Data is held in a variety of systems and formats and it can be difficult to access.
There are sometimes multiple and/or partial ‘versions of the truth’ which means that data is not always trusted.
Duplication of data is common, where multiple business areas are collecting and storing the same or overlapping datasets.
The Data Audit exercise enables organisations to identify and understand:
The extent and range of datasets that exist, their format and how their lifecycle is managed.
The ranking of datasets in terms of their importance to service delivery and the perceived gaps in useful data that might help the delivery of these objectives.
The potential for sharing datasets privately within the organisation and openly with the wider public.
Derilinx’s Data Audit Framework provides a proven method for:
Identification of datasets
The compilation of a comprehensive inventory
The definition of clear recommendations for managing this information and sharing it in a controlled environment
This Data Audit Framework, consisting of methodologies, processes and templates for cataloguing, enables organisations to find out what data they hold, where it is located and who is responsible for it.
Derilinx data audits follow the best practice outlined in the Digital Curation Centre’s Data Asset Framework, and aligns with best practice from DAMA.
The Data Asset Framework (DAF) is a set of methods to:
1. Find out what data assets are being created and held within organisations
2. Explore how those data are stored, managed, shared and reused
3. Identify any risks e.g. misuse, data loss or irretrievability
4. Learn about stakeholders’ attitudes towards data creation and sharing
5. Suggest ways to improve ongoing data management
Derilinx’s Data Audit Framework follows 4 stages:
Data Catalogue
A Data Catalogue can be directly used to record and describe the data assets identified during a data audit. Using strict data governance processes, each data asset can be reviewed for suitability for data sharing. The data asset can remain as private data, or approved for sharing with certain stakeholders, or approved for publication as Open Data.
Check out our datAdore platform for more information.
Pilot Data Audit
We recommend approaching Data Audits as focused exercises centred on a theme (e.g. environmental data) or on a subset of an organisation first – very much as a pilot. This approach enables us to deliver concrete results quickly, limit the initial effort and build organisational knowledge. This knowledge can then be extended to other parts of the organisation with a view to make data auditing an in-house capability delivered on a regular basis as part of data management responsibilities.
