Understanding the user journey

This section provides guidance about how to consider user needs in a more systematic way

Frustrations for data users

Our research with data users highlighted that many felt open data was still sometimes challenging to access and make use of. Although data may be freely available online, some data users described difficulties actually finding and extracting the specific data they required. This was due to issues with the templates or formats, time periods and geographic regions that didn’t quite fit their requirements or other (perhaps unintended) limitations.

Problem examples

• Data is not explained

  • No description of metric

  • Unclear unit of measurement or time frame

  • Unclear distinction between whether figures given represent actual counts, or percentages or proportions

  • Minimal supporting text or context for data

  • Charts with unclear labels, colours or keys

• Data is not accessible

  • Raw data in large spreadsheets not easily downloaded or able to view on screen

  • Static PDF documents that can’t be edited or text can’t be selected

• There is no feedback loop

  • Users cannot review or comment at point of access/no identifiable contact information

  • No outlet for group feedback or peer support

Quick Fixes: Common Publishing Errors

Be careful to address the following common publishing errors as these are often aspects of data that can easily be cleaned up, and help to create a simpler and more accessible experience for data users.

• Dates. Mixed date formats or British versus American dates used simultaneously. ACTION: be consistent with date formats within documents and across different publications if possible. Where necessary add a label that indicates which date format is being used.

• Multiple representations. Abbreviations and expanded forms i.e. Vice-President or VP or vice-pres. ACTION: Try not to use abbreviations; clarity is better. If they are unavoidable, create a key or glossary to explain them in full and be consistent in how they appear.

• Duplicate record detection. When searching for a term, items are duplicated to speed up searches across multiple domains. ACTION: if there is no technical solution for this within the platform you are using to publish, then be clear with a warning that tells data users that there may be duplicate records.

• Summation records. Data containing notes, sums or formula instead of expected numerical data. ACTION: this is 'data noise' that you may want to remove from your data. However, where necessary you can colour code summation data to indicate that is should not be included in analysis.

• Redundant data. Unrelated data in data sets, such as administration codes, thats data users do not require. ACTION: this is 'data noise' that you may want to remove from your data. However, where necessary you can colour code summation data to indicate that is should not be included in analysis.

• Numeric ranges. Often used to anonymise data by grouping individual data points together, but can make searching difficult. ACTION: Where possible, allow data users the functionality to create their own more meaningful numeric ranges.

• Spelling errors. Can lead to limitations when querying and search data; also impacts automated visuals that rely on text. ACTION: Thorough proofreading is required and should be past of a Quality Assurance Process before data is published.

Data users may get frustrated with common publishing errors, but these can often be easily fixed.

Mapping the user journey

A user journey map is a visualisation of the process that a person goes through in order to accomplish a goal. User journeys are used to:

• Understand user needs and motivations

• Create useful customer insights and validate what we know

• Develop consistency and clarity of thought across an organisation or project

• Invite collaboration and constructive dialogue to determine how best to meet user needs

• Create actionable intelligence to create and develop a user-centric service and product portfolio

Mapping user motivations and touchpoints at different stages of their journey is another way to ensure the data offer is user-centric. There are various user needs at different stages of engagement. The mapping tool below could be used to help break the user journey into simple stages and document these needs. Or it could be generated in a data user workshop, for example.

• Look at initial contact: What is the data user's initial aim or motivation and how do they act on this? How do they first make contact with your data? Does anything require improvement at this stage of the journey?

• Look at the user's subsequent actions: What is the data user thinking about and doing as they try to access the data they need? Where are the information exchanges between the users and publishers happening and how could these touchpoints be improved?

• Look at the finalisation or conclusion: How is the interaction completed or concluded? What are the opportunities for feedback and follow-up? Would they share and promote your resource with others?

Are there aspects of the user journey you are concerned about? Can the tool below help you to pinpoint specific issues?

TOOL: Reviewing touchpoints along the user journey

Other resources for mapping the customer journey:

• Miro customer journey mapping template
• A guide to creating user journey maps
• Nielson Journey Mapping 101