How do you keep UX research data consistent with multiple sources?

1 Define your research goals and questions

Before you start collecting data from multiple sources, you need to have a clear and specific research goal and a set of research questions that guide your data collection and analysis. This will help you determine what kind of data you need, how much data you need, and how to measure and compare the data. For example, if your goal is to understand how users interact with a new feature on your website, you might want to collect data from user interviews, usability tests, analytics, and surveys. Your research questions might include: How do users discover the feature? How often do they use it? What are the benefits and pain points of using it? How does it affect their overall satisfaction with the website?

2 Choose appropriate data sources and tools

Once you have your research goal and questions, you need to choose the best data sources and tools that can answer them. Data sources can be qualitative or quantitative, primary or secondary, direct or indirect, depending on the type, origin, and nature of the data. For example, qualitative data sources include user interviews, observations, and diary studies, while quantitative data sources include analytics, surveys, and A/B tests. Primary data sources are collected directly from your users, while secondary data sources are obtained from existing sources, such as reports, articles, or competitors. Direct data sources reflect the actual behavior or feedback of your users, while indirect data sources infer the behavior or feedback from other indicators, such as clicks, ratings, or reviews. You should choose the data sources that are most relevant, reliable, and valid for your research goal and questions. You should also choose the tools that can help you collect, store, analyze, and visualize the data in a consistent and efficient way. For example, you might use tools like Google Forms, SurveyMonkey, or Typeform for surveys, Zoom, Skype, or Google Meet for interviews, Google Analytics, Hotjar, or Mixpanel for analytics, and Miro, Airtable, or Dovetail for data synthesis and visualization.

3 Establish a common data format and structure

When you collect data from multiple sources and tools, you might end up with different data formats and structures that are not compatible or comparable with each other. For example, you might have data in CSV, PDF, TXT, or JSON files, with different column names, values, or units. To avoid this, you should establish a common data format and structure that can integrate and standardize the data from different sources and tools. This will help you avoid errors, inconsistencies, and redundancies in your data and make it easier to analyze and compare the data. For example, you might use a common data format like CSV or JSON for all your data files, and use a common data structure like a data dictionary or a data model that defines the variables, attributes, and relationships of your data. You can also use tools like Google Sheets, Excel, or SQL to import, export, and manipulate the data in a common format and structure.

4 Apply consistent data cleaning and validation techniques

Even if you have a common data format and structure, you might still encounter some issues with the quality and accuracy of your data from multiple sources and tools. For example, you might have missing, incomplete, or incorrect data, or data that is not relevant, representative, or reliable for your research goal and questions. To avoid this, you should apply consistent data cleaning and validation techniques that can identify, correct, or remove the issues with your data and ensure that your data is consistent, complete, and accurate. For example, you might use techniques like data filtering, sorting, deduplication, normalization, or transformation to clean your data, and techniques like data verification, validation, or triangulation to validate your data. You can also use tools like OpenRefine, Trifacta, or Datawrapper to help you with data cleaning and validation.

5 Document and communicate your data sources and methods

Finally, when you have consistent and reliable data from multiple sources and tools, you need to document and communicate your data sources and methods to your stakeholders and collaborators. This will help you increase the transparency, credibility, and usability of your data and your insights. For example, you should document and communicate the following information about your data sources and methods: What data sources and tools did you use and why? How did you collect, store, analyze, and visualize the data? What data format and structure did you use and how did you integrate and standardize the data? What data cleaning and validation techniques did you apply and how did you ensure the quality and accuracy of the data? How did you interpret and synthesize the data and what insights did you generate? You can use tools like Google Docs, Slides, or Sheets to document and communicate your data sources and methods in a clear and concise way.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?