5 Principles to Enhance UX of Your Data Analytics Applications
Data is one of the most valuable assets of any organisation. In order to leverage value from their data assets, organisations are continuing to invest more in analytics.
However, organisations are still having challenges in adopting analytics solutions to become truly data-driven and a significant percentage of analytics projects fail to move beyond proof of concept/value phases and become fully operationalised solutions.
“Business adoption of Big Data continues to be a struggle, with 73.4% of firms citing this as an ongoing challenge.” (Source: NewVantage Partners 2020 Big Data and AI Executive Survey)
“87% of data science projects never make it into production” (Source: VentureBeat)
While there are several factors across people, process, technology and data dimensions contributing to such stats, one important factor is a lack of impetus on the end user experience while designing and building analytics applications.
This blog covers five principles to consider while working on analytics applications to enhance user experience and adoption.
But first, let’s look at why this is important.
Why Focus on User Experience When Designing Analytics Applications
As in the case of any application, a positive user experience can go a long way in improving the adoption of analytics applications by the end user community. Yet, user experience considerations often take a back seat as data teams focus more on the engineering and analytics aspects. This can negatively impact operationalisation and adoption of the final solution.
When building analytics applications, it is important to give adequate consideration to user experience from the beginning itself. Practising “UX led analytics” (see our recent post on user-centred design) where the user experience and interfaces are conceptualised upfront, before data engineering and modelling, can be a powerful tool to foster end user adoption.
5 Principles to Enhance User Experience and Adoption of Data Analytics Applications
1. Get the “right data to the right person, at the right time, in the right format”
The holy grail as far as data & analytics are concerned, “right data (and insights) to the right person, at the right time, in the right format” is often quoted as the vision of data & analytics teams or practices.
Aligning to this vision and using it as a ‘compass’ to guide the design and development of analytics applications can help to ensure positive user experience and adoption by providing clarity on the following critical aspects:
- who are the users?
- what data & insights they want?
- how frequent and timely should each set of data & insights be?
- which format(s) and interface(s) satisfy the requirements?
- what (subtle) differences exist in the requirements amongst different user groups?
- which are the must have vs. good to have requirements?
- what are the minimum requirements around data quality, model accuracy etc.?
- what tools/platforms are the end users familiar with, and how much upskilling, if any, will be required?
2. Adopt user-centred design to enhance user experience and adoption
In user centred design, the emphasis is always on the end user. It is a design approach that focuses on understanding and empathising with the users, their requirements and challenges, and the context in which they (will) use a specific product or solution.
Having a clear understanding of the various personas and their user journeys will enable design and develop solutions that truly resonate with the end users and hence, have a better chance of successfully solving their challenges and positively augmenting their experience.
Adopting a user centred approach to designing analytics applications can enhance user experience and user adoption due to the following factors:
- end users are engaged very early in the process and are contributors to the overall design and vision of the application
- the design is iterative with incremental feedback from users and relevant parties incorporated in each stage
- the design team has good understanding of the end user personas, their needs, motivations, challenges and the context in which they require the application
3. Ensure users have visibility of relevant context to enhance trust and adoption
A key factor that impacts the user experience and adoption of analytics applications is the trust that end users have in the data and insights presented. Users need to be confident that they are referring to the right numbers in order to effectively use them in decision making and business activities. The better the trust in the data and insights presented, the better the user experience and adoption are.
An approach to cultivate this trust and confidence in the end users is to ensure they have visibility or easy access to the context of information being provided. Having the context around the data and insights will help users to understand and trust the application more and use it with confidence.
Some examples may be:
- where an application is showing filtered data, explicitly displaying the current filters applied
- indicating the time of last data refresh for datasets
- showing the main factors/features that have led to a specific forecast/prediction
- incorporating tool-tips to explain the definition of metrics
4. Provide consistent interfaces for all users for seamless collaboration
Customisable interfaces and self service are awesome! However, sometimes the requirements to configure own views, reports etc. and to ‘discover’ subsets of relevant data themselves may overwhelm less tech-savvy users. There needs to be a fine balance between simple, consistent interfaces vs. customisable, free form ones; and this will largely depend on the spectrum of users serviced by the analytics application.
When the users are looking to get specific ‘ready-made’ data or insights quickly it makes sense to tailor the interfaces to provide exactly that. This will mean a strong discovery phase engaging the users as early as possible to finalise the specific requirements. In general, the fewer the clicks that the user needs to get to the information they are looking for, the better the user experience.
Of course, self service data & insights discovery can be powerful, especially to user groups like analysts. In such cases, a consistent starting point enabling users to quickly reach views, reports, metrics etc. that are most often needed by most users with options to dig deeper, if required, can be a good way to go.
Providing the same, consistent interfaces to different user groups can enable smoother collaboration as everyone is looking at the same views, reports, metrics etc. and having communications in reference to them becomes easier. Further, this can enhance the overall user experience and increase the chances of users embracing the application.
5. Consolidate data and insights in one place to improve efficiency and user experience
It is hugely beneficial for end users to have access to all the data and insights they require at one place. This will help them save a lot of time by avoiding the need to switch amongst multiple applications/interfaces, and enable them to operate more efficiently.
Unfortunately, more often than not, the information required is scattered across multiple systems and platforms resulting in a sub par user experience.
When designing and building analytics applications, consideration should be given to maximise such consolidation and reduce the number of times users have to refer to other sources for related information.
Given the proliferation of technology platforms that many organisations experience this may be extremely challenging. A simple starting point in such cases may be embedding screens from or links to other relevant reports, data sets etc. within the application being built and enabling single sign on authentications, so that the users can at least navigate seamlessly, if/when required. Such provisions can contribute to a better user experience compared to the overhead of logging in and out of multiple platforms.
Intuitive interfaces can reduce, or even eliminate completely, the time users need to spend on training to use an application. Maximising consolidation can also contribute to reducing overall training efforts by reducing the number of tools/platforms/applications that users need to interact with, which further contributes to positive user experience.
Designing applications that end users love and embrace is as much an art as it is a science. Well designed analytics applications focussed on the end users and their needs, enabling them to easily get to the data and insights they need, when they need it, can make a profound impact on productivity and efficiency. Applications that provide positive user experience have a better chance of being adopted by the end users in turn providing good return on data & analytics investments.