Add page on defining data standards

source/defining_standards.html.md.erb

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+---
+title: Defining the standards
+weight: 14
+---
+
+# Defining the standards
+
+One of the outputs of the the data standards project was to define a blueprint or process for defining data standards.  The intention is to use this blueprint to define future data standards across MCHLG.
+
+![A diagram showing the circular blueprint for defining and refining data standards](/images/data-standards-blueprint.png)
+A diagram showing the circular blueprint for defining and refining data standards that follows the pattern:
+
+- Assess data needs
+- Review existing standards
+- Adopt and extend
+- Assure
+- Publish
+- Implement
+- Evaluate and measure
+- Iterate
+
+## Assess data needs
+Before defining a data standard, it is important to understand the needs. It is therefore important to:
+
+- Understand what data needs to be collected.
+- Understand any business policy requirements for the data in question.
+- What is the data going to be used for.
+- What is the purpose of collecting the data.
+- What decisions or processes does the data enable.
+
+## Review existing standards
+
+Once, we understand the needs and the drivers for defining the data standard we need to review existing related standards used within MHCLG, Government and the wider funding ecosystem. We do this by:
+
+Undertaking desktop research to collate existing standards for the given data subject, including:
+
+- Standards used within MHCLG.
+- Reviewing [Open standards for government - GOV.UK](https://www.gov.uk/government/collections/open-standards-for-government-data-and-technology]).
+- Consult [ukgovernmentdigital.slack.com](http://ukgovernmentdigital.slack.com/) for any in-train standards work or standards used in other Government departments.
+- Review more generalist standards such as [schema.org](http://schema.org/) and [DCMI: Dublin Core™](https://www.dublincore.org/specifications/dublin-core/).
+- Review of any standards used by Local Authorities such as [LG Inform Plus](https://home.esd.org.uk/).
+- Document data standards and their data points.
+- Collate standards and compare differences.
+
+## Adapt and extend to suit needs
+
+At this point, we should have an understanding of our data needs and a picture of any existing standards and their data points.  
+
+To ensure consistency we should, where possible use an existing standard as this reduces diversity and the need to customise. If this is not possible a suitable data standard should be used as the starting point and then additional data points added and/or removed as necessary. If should be a point of last resort to make up a data standard from scratch.
+
+The data standard should then be documented using our data standards template to ensure that we capture all relevant information, including:
+
+- Data point name and description.
+- Data types and formats of data.
+- Validation of data details.
+- Whether the data is mandatory, optional or conditional.
+- Source of data point and rationale for inclusion.
+
+## Assure
+Once a draft version of a data standard has been documented it is essential to get feedback to assure that the data standard will meet the needs of its user and the business processes that it supports. Assuring data standards should involve:
+
+- Business stakeholders relating to the subject matter.
+- Subject matter experts within MHCLG.
+- Data specialists within MHCLG.
+- Delivery teams involved in the development of any digital products affected by the data standard.
+- System maintenance  teams responsible for the upkeep and support of any business systems potentially affected by the data standard.
+
+## Publish
+Once the data standard has been defined and assured it needs to go through a final Governance process before being published.
+
+<%= warning_text('The Governance and publishing process is still to be defined.') %>
+
+## Implement
+Implementation of a data standard may be complex and the take time will vary depending upon the system changes involved and the processes that they support.  Implementation changes should be planned and involve all interested parties.  
+
+Considerations for implementation:
+
+- Interoperability and integration with other systems.
+- Changes to user interfaces and support business processes for collecting the data.
+- Additional validation and conditionality.
+- Reporting and management information changes.
+- Any audit or capture of longitudinal changes.
+- Deadlines and important policy dates.
+
+>It is important to note that data standards are the minimum data set that should be collected and each system may want to collect additional data especially if its needed to support system function or a users journey. 
+
+## Evaluate and measure
+Once a system or process has become data standard compliant it is important to monitor those changes over a period of time to ensure they meet the needs of its users.  
+
+This includes:
+
+- Are they users needs being met?
+- What is the user experience in any implementation changes?
+- What issues are there in collecting the data?
+- Is there ay data missing, or some data that is no longer needed?
+- Are business process more efficient or have issues been introduced?
+- Do users understand why the data is being collected?
+- Are there changes to data quality?
+- Are reports accurate and contain the right information?
+
+These observations should be communicated with the data standard owner and the data community in general.
+
+## 
+Iterate
+It is important to note that data standards will change.  Whether this is off a period of monitoring and evaluation, a change in policy or a change or user needs.  Where changes are needed it is important to capture why and go through the cycle of defining the data standard from scratch.
+
+This will enable you to validate that the data standard is still relevant and is continuing to meet the needs of it users.  By assuring the change and following the governance process you will ensure that the data standard is changing for the right reason and that any impacted systems are aware of the change and the amount of effort it takes to change it. 
+
+