Our journey with Data Governance started 20 years when building Data Warehouses was in its infancy. We learnt quickly that building data warehouses with trusted information that users would be prepared to adopt required more than load scripts and agile data modelling. It also needed data quality, data transparency, a common understanding of business language, and sophisticated access models. These requirements have only recently been termed ‘Data Governance’, but as a business, we have always been being applying these disciplines to our customers’ assets and solutions. Before the advent of the tools we delivered Governance in documents, spreadsheets and our database models.
So what is Data governance? It is broadly the initiative a company takes to create and apply a set of rules and policies regarding its data. These policies cover issues such as: Assigning accountability to employees responsible for data assets, maintaining data in a way that ensures accuracy and consistency.
Through our many years of experience in this field we note that often good Data Governance is about People, Processes and Technology with all playing a vital part. We also would strongly recommend starting small and expanding as user acceptance/ buy-in grows.
However, In short, data governance is a set of policies, procedures, protocols, and metrics that control how data is used, managed, and stored. Any data governance tool must be able to quickly and effectively manage data from many different access or storage points as well as meet the needs of different end-users.
There can be enormous Data Governance Benefits a business can derive and some of those include;
- Better Decision-Making – One of the key benefits of data governance is better decision-making. This applies to both the decision-making process, as well as the decisions themselves. Well-governed data is more discoverable, making it easier for the relevant parties to find useful insights. It also means decisions will be based on the right data, ensuring greater accuracy and trust.
- Operational Efficiency – Data is incredibly valuable in the age of data-driven business. Therefore, it should be treated as the asset it is. Consider a manufacturing business’ physical assets, for example a well-run manufacturing business ensure their production-line machinery undergoes regular inspections, maintenance and upgrades so the line operates smoothly with limited down-time. The same approach should apply to data.
- Improved Data Understanding and Lineage – Data governance is about understanding what your data is and where it is stored. When implemented well, data governance provides a comprehensive view of all data assets. It also provides greater accountability. By assigning permissions, it is far easier to determine who’s responsible for specific data.
- Greater Data Quality – As data governance aids in discoverability, businesses with effective data governance programs also benefit from improved data quality. Although technically two separate initiatives, some of their goals overlap. Data governance improves data quality, because answering the latter makes it easier to tackle the former.
- Regulatory Compliance – As mentioned in the introduction, if you haven’t yet adopted a data governance program, compliance is perhaps the best reason to do so. Hefty fines with an upper limit of €20 million or 4% or annual global turnover – whichever is greater – are nothing to baulk at.
That said, GDPR fines are only incentivising something you should already be keen to do. Data-driven businesses that aren’t enjoying the aforementioned benefits are fundamentally stifling their own performance. It could even be argued that to be truly data-driven, data governance is a must.
Success stories
Reinsurer Creates a Trusted Data Source with Data Cataloging and Data Lineage
A Smallnet client who is a leading Insurer and... read more
A Leading Property & Casualty Insurer & Re-insurer Netezza Data Warehouse Migration
For the past 7 years or more IBM have... read more
Data governance and Catalog Use case
SmallNet currently support a leading insurer operating in the Lloyd's,... read more
Data Governance Engagement approach
We are very flexible in our engagement approach from short term tactical help to help issues or get you started to a more involved end to end pilots that help you learn while delivering something that is meaningful and useful.
However, whatever you want to do, we believe in not trying to do too much at time, so we recommend breaking down your projects into smaller chunks of easily defined and scoped deliverables. This enables you to focus on the detail, have early delivery of results and capability and manage better manage time and cost. As you do this your understanding of how and what you want to do will grow and your thoughts and approach may change, the approach enables you to adapt without major rework.
Examples of SmallNet Data Governance Engagements:
- Data Lineage for financial services and Banking organisations to support regulatory compliance
- Business Glossary of terms and Policies, setup and population
- Catalog design and population and initial load from existing sources such other catalog tools, spreadsheets and word documents etc
- Process and workflow implementation
- Data Quality Platform to support the active monitoring and remediation of Data Quality failures
- Technical Architecture and solution design
- Strategy and Implementation road map, helping to build a Data
- Governance Health Checks and Readiness Assessments