A Design-Driven Future for Enterprise Software — Part 1

Improving the Discoverability of Complex Datasets

Enterprise software for specialised and technical domains — asset integrity in our case — is very much unlike consumer applications. Users are segmented into hierarchies of stakeholders with widely different behaviours and intertwined goals. They work with dense data in nonlinear workflows and make mission-critical decisions.

At Aize we have a vision to ultimately replace Coabis — the industry standard in asset integrity management software — with a cloud-based and future-ready offering. Though Coabis is a powerful enterprise application that has been built over 30 years and is loved by many, it lacks the ease of use of a modern, web-based platform.

Starting afresh allowed us to bring the value of a consumer-grade user experience to our domain and make it one of our unique selling points. To make this happen, we followed closely NNG’s 10 Usability Heuristics Applied to Complex Applications. We started with a key productivity driver: enabling users to get the data they need as quickly as possible.

Data Recognition Over Data Recall

In the world of asset integrity, datasets can be very extensive, with thousands of entries — each of these with dozens of properties. In our legacy software, users typically had to filter on data before they were able to see any results. This required a high degree of knowledge of the dataset, often having to recall complex details. Our new approach is to allow users to see the data outright, and to then filter on this general view to find specific results — leading to data recognition and familiarity.

We realised that this approach would affect page performance because of the sheer scale of the data to display. To optimise loading times, we implemented a configurable pagination. By default, users navigate a set number of entries per page that varies based on data size and the space required to display it. More extensive data equals less entries displayed, which also helps prevent cognitive overload. Users can easily switch to display as many results per page as they need, and jump to any page — saving countless clicks.

The Freedom to Decide How to Search

Software proficiency, user goals, and familiarity with other applications are all factors that can affect how our users navigate data, and we agreed to cater for all their different search behaviours. We implemented a faceted search experience on each page, enabling users to combine search field queries, filters, and sorting order. These fluid search criteria help narrow down to the entries that the users are looking for and can be changed dynamically.

There are use cases where our users may not want to just filter results but see them all in a certain order — for example, by status or criticality. The sorting order can be adjusted to display properties from first to last and vice versa. This is achieved differently based on the property types: strings can be sorted alphabetically, dates by chronology, statuses by workflow order or importance — enabling a mental model of data importance / relevance.

Filter complexity goes hand in hand with data complexity: dozens of different properties, each with dozens of discrete or range attributes, can be filtered on dynamically. We established a single, scalable filtering pattern that users can familiarise with. This can also be configured to include additional filters not shown by default, meeting various user needs.

We created context for search queries by adding an indication of the results returned. This changes instantly as the filtering attributes are selected. The indicator’s key benefit is enabling learning by doing, quickly showing the impact of the users’ choices. At the same time, it also allows users to start again in one click, fostering user control and freedom.

One Size Doesn’t Fit All

User behaviours aren’t limited to how they search data in a textual form. While some are used to a file explorer-like environment and enjoy the flexibility given by lists and tables, others think more visually. We have enabled users to make informed decisions by navigating and analysing some key datasets in an interactive chart format. Users are free to switch between textual and visual display modes based on their preference.

Discoverability is just one of the many things we are working on at Aize to make our user experience best-in-class when it comes to enterprise applications. From simplified workflows to in-context help to smart error prevention, we are implementing many other patterns to save time, drive user familiarity, and increase efficiency.