How would you summarize the difference between the two texts with respect to how they present the relevance of big data for UX?
To make one thing clear right in the beginning: Answering this question just by reading the title is not really possible. After reading both articles in-depth however, the distinction becomes more clear. They do not necessarily transfer difference in views or opinion - they just show different use cases of UX in the interplay with Big Data
How to leverage Big Data of digital products for your Design Process?
- e.g. Improve UX based on Big Data insights
- Structures big data to find clusters such as different target audiences, find patterns in their user behaviour
- Tailor products to special target audiences
- React to markets in real-time
How to design Big Data platforms and make the data available for the masses?
- e.g. The UX of Big Data Analytic tools
- Make the relevant data accessible for the people who need the data
- What data needs to be accessed by whom?
Give an example of a service that you use which relates to at least one of the presented perspectives. Explain how the service makes use of big data (or how you think it does if you cannot be sure).
Example 1: User Tracking with Hotjar - Improve the product based on actionable insights
Recently I was investigating a webshop for a client. After concluding the existing user journey and overall layout of the shop already followed reliable patterns due to the fact they are using a popular Shopify template, we decided to dive deeper in the data. The data provided by Google Analytics however was not that insightful to work with, as there were no events or goals defined yet.
The tool "Hotjar" however enabled us to track the users' behavior right away on another level: It is possible to track the exact individual user journey as video playback and see patterns / differences between mobile and desktop users, different attention spans at different times of the day etc.
With this technology in place, the relevance of guided user testing just became less, because it enables us to conduct covert, "passive" user testing and helps to
Example 2: Data for the non-technical stakeholder: Custom Dashboards with Google Data Studio
Clients want to keep track of their metrics. With the sheer amount of tools in the Online Marketing world (e.g. Google Analytics, Google Ads, Sistrix, Ahrefs, Webmaster Tools, ...) a tailored representation with tailored time scales (Year-over-year) and special wishes on how to present these metrics with custom units (e.g. "Cost-Revenue-Relation") can become very complex.
For the client it would be very uncomfortable to check their numbers manually by logging into each tool one by one.
A popular way of dealing with that is often found in Excel sheets, where a Marketing manager would copy all the data with according filters set in one sheet and finally make sense of the data in the Excel sheet while making it even more descriptive, e.g. by using graphs.
This can give the client a more filtered, custom dashboard of the desired data. This approach however, while the report looks as professional as computer-generated is still connected to a very manual process.
With the introduction of Google Data Studio, this manual process became more and more obsolete, as for many use cases, the one-time setup of Data Studio is faster than manually aggregating an Excel file on weekly basis. Data Studio allows to aggregate Data from different sources and make sense of that data in real time. Easier for the managing employee in an agency and easier for the client.
My question: The change in UX roles
Are you scared?
Imagine you are planning to become a UX Researcher - how do you expect your role is going to change in the next decade due to Big Data?
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