UX, Data and Product Terms You Should Know, Pt. 1

3 Minutes Read

As consultants working with clients across a variety of industries, it is our job to make sure that our combined project teams all have a shared understanding of the work we are going to do.  To do this effectively, we create a glossary of terms and present primers throughout the project lifecycle.  These end up being a great resource for a shared understanding as well as for onboarding new team members. 

As I was preparing to start a new project today, I realized this might be a useful tool for others, so I decided to start a series titled, UX, Data and Product Terms You Should Know. These terms are in no particular order with regard to importance, just what pops in my head day-to-day as I work on building software.  With how quickly technology changes, I feel this could be an ongoing series and I hope it provides value to you.

Feel free to copy and use this for your own projects!

Toady's terms: 


User experience (UX) is the experience a person (the user) has with a product.  It encompasses how people use a product (browse, touch, speak, search, view), how they feel when they use a product (happy, inspired, sad, frustrated), their intent and what decisions they make when using a product (buy, learn, connect, subscribe, download).  Understanding user experiences requires research and testing.  One of the big mistakes people often make when building a product is to assume they know what users want and how they will behave which often results in a product that fails to meet expectations and often ends up costing companies thousands to millions of wasted dollars.


Data.world puts it this way:

"Knowledge graphs power some of the most ubiquitous applications in the world today including Amazon Alexa, Netflix, Facebook, and Google Search. They are extraordinarily flexible, agile, and resilient, with a different structure than traditional relational databases. Rather than representing data as a table with rows and columns, a knowledge graph captures and organizes relationships between real-world concepts in the form of a graph.

Knowledge graphs are modern data infrastructure where data and metadata connect to all users in an organization. They describe the most important and crucial things in your company that form the basis of next-generation search, recommendations, graph machine learning and AI applications such as chatbots, natural language question answering and personal assistants.

Knowledge graphs bridge the gap between how data consumers understand their business world and how companies store the data. They are unique in that business terminology is represented as concepts and relationships that are both understandable by people and machines in the exact same way."

Learn more about the intersection of UX and knowledge graphs on our blog.


Agile is an iterative approach to building a product.  Its core tenets are frequent, transparent communication among team members while they iteratively define, design, and develop a product.  A key attraction of agile for clients are the demos of working prototypes every 2-3 weeks and the idea of fail fast and pivot so money isn't wasted on the wrong activities. 


A physical or digital thing that provides value to its intended user.  A car is a product, so is software, a blog, an app, a book or a website.  Predictive UX focuses on the design, testing, and development of digital products.


A Request for Proposal, or RFP, is a document issued by a customer seeking bids or proposals from potential vendors.  It describes the details of a project, past performance requirements, the stakeholders, what type of company the hiring organization wants to work with and the timeline for submitting questions and final proposals.

If you work for an organization that is issuing an RFP for a data or UX project, make sure to include a requirement for user experience to be part of your data project and ask vendors to illustrate where they have been successful in realizing ROI for data projects in particular by using design thinking to inform data design.  Many data projects end up as costly endeavors that don't meet expectations because human-centered design is not considered up-front.


A roadmap is a high-level swimlane view of the releases planned for a product.  The goal of the roadmap is to illustrate when you will implement specific features over time.  A lot of upfront planning goes into making a roadmap to ensure it is achievable and aligned to business objectives.  An outcome of creating a roadmap is insight into resource needs, costs, and release cycles that can be shared with stakeholders and users of your system.  However, a roadmap is not a static document, it should be updated regularly to reflect what you are learning about what your users want and to reflect the reality of what you can develop based on cashflow and the success of connecting assets at-scale within your organization.


Once a project is awarded to a vendor, a Statement of Work (SOW) is issued detailing the specific timeline, costs, resources and deliverables for a specific phase of the awarded project.  The SOW may also detail important assumptions and constraints. Be sure to ask vendors to include measures of success so that you can both align on what it means to deliver a graph database, knowledge graph, machine learning or AI foundation.  That statement alone isn't enough for your vendor to know they have done their job and it's not enough for you to know they have either.  Get detailed up front so that you can save yourself from costly and frustrating outcomes later.


Picture of Karen Passmore

Karen Passmore

Karen Passmore is the CEO of Predictive UX, an agency focused on product strategies and user experience design for data-rich applications. Karen talks about UX, DEI, Content and Data Strategies, Search, Knowledge Graphs, and Enterprise Software. Her career is marked by UX design leadership at Fortune 500 companies, startups and government agencies.