In the oceans of the planet earth are approximately 332,519,000 cubic meters of water, a quantification so hard to interpret that even its association with about 352 quintillion plastic bottles contributes to an improved perception of its volume. The same happens when we quantify the digital information produced until today at a global level, with the added difficulty of the interpretation given by its intangibility.
In the last decade, we discovered a new information unit, reflecting the increase in IP traffic and also the amount of information produced globally. Eight years ago, we were discovering zettabyte at a time when the amount of digital data produced was already exceeding 1021 bytes worldwide. In 2018 we reached 33 zettabytes of information, whose download would take more than 1 billion years if we considered the execution of this task by a single human being at the current speed of the internet. Officially at Zettabyte Era, the amount of data produced continues to grow at an increasingly fast pace, allowing IDC to predict the existence of 142 zettabytes within five years, that is, an increase of 81.15% in seven years.
In the creation of digital products and services context, this increase in available information represented benefits but also increased levels of demand for its conversion into guiding insights in the decision-making process. The truth is, through our biological cognitive capacities we continue to have the same natural information processing skills available for a much higher number of external triggers, which now requires us to create methodologies, strategies, and tools to accelerate this analysis and interpretation process.
As designers, the strategies defined to serve the needs of stakeholders are now more agile, with a high degree of adaptability, responsiveness to change, and preparation to react in environments of high uncertainty. Among the strategic business objectives of the clients and their adaptation to the needs and end-users’ mental models, the role of the designer has become imperative to guarantee the acceptance of digital products and services by the market.
In this vast ocean of data with prominent depth, being a diver-designer requires going deep to be able to transform an idea into a marketable product. Variable according to the complexity and dimension of the project, there are several phases in its development.
Sight the sea
Initiated by the identification of a need, the creation of a new digital product benefits early from the intervention of a human-centred designer. Human-centred designer, in contrast to the user-centred approach still very much defended today, because the added value only can be maximised if the focus of the product designer is beyond the needs of the user, whose response includes validating the needs of all stakeholders involved: customers, users and development teams.
In this role, originally defined here as diver-designer, the contribution is initiated by the perception and business requirements survey, usability and technical requirements, a knowledge that will directly impact the design effort and future changes to the product.
In general, it should be able to identify the fundamental preference transversal to the client, development team and end-users, transforming it into a set of complementary instrumental preferences. In other words, at this stage, the diver-designer is preparing to define goals, to transform them into a set of interactions with recurrence to a determined technology. As the first step in the UX research phase, observation is a moment of analysis to raise awareness about expectations, needs and existing possibilities.
Realise the Depth
After the observation phase, what kind of actions should we take to effectively execute a UX research strategy? In general, we must consider two levels associated with two types of actions: the perception phase, for data collection, and the analysis phase, characterised by its synthesis and transformation into conclusions, in a convergent performance of relevance to the architecture of the solution.
We know that UX research differs from market research, but it also uses a part of it to assess more specific conclusions. In this context, we start by understanding what the market tells us to analyse its behaviour, in the same way, that we start by understanding the tools used to guarantee the maximisation of its potential use.
The perception of past usage experiences, with a view to higher assertiveness and suitability for end-users, is defined as mental-model and represents, in the designer's activity, the ability to convert knowledge into improved and superior quality user experiences. A mental-model provides us with information about the decision-making process of a particular group and allows the selection of the most effective user research methods followed by a language with which the user group identifies.
In this field, the range of research methodologies and tools available is vast. Between qualitative and quantitative methods, the designer must always use his new knowledge about the group under study to direct the research and avoid compromising the progress in the process.
This translates, in particular, into a perspective of divergent analytics of the available data, and ends with the convergence in conclusions directed to the definition of the product or service functionalities. Given these conclusions, we guarantee our customers a real perception of the needs of their end-users, using their experience to ensure a future solution with higher added value.
Put Your Oxygen
Analytics gives rise to ideation. The information categorisation that relates the content, its context and the interaction on the part of the users is carried out, translated by the Venn diagram of Rosenfeld and Morville. In this process, the insights are transformed into the strategic plan of contents and functionalities, in a perspective of prioritisation and implementation decision.
In this case, the diver-designer uses tools that provide gauging feasibility and the generation of development, such as the Minimum Viable Product (MVP) Prioritization Matrix. For each of the functionalities, it will be necessary to measure the effort and their impact on the service. To predict the impact of a given action, we consider its strategic value, its relevance for increasing the use of the product or service and the level of user satisfaction. On the other hand, concerning the effort, it is essential to assess the development needs, the operational cost of its implementation and the risk.
After this analysis, we gather all the information necessary to perform a set of tasks in a logical, contextualised and organised way. From the design point of view, we have now defined the information architecture represented, in many cases, by a flowchart, the blueprint of the user experience. From the management point of view, the operational roadmap aimed at maximising efficiency in achieving the client's objectives. From the development point of view, the logic behind the automation process that will guide the needs of creating algorithms, even leveraging the use of structured data for the creation of provisional models, in products in which artificial intelligence is considered.
Jump to Water
In an era when innovation, machine learning and artificial intelligence are the starting point for the creation of product design, the level of demand in the component of user experience towards the user interface continues to increase progressively.
During the creative process, the diver-designer reduces the number of interactions as much as possible, contrary to the current state of decisional fatigue caused by the high number of notifications and stimuli. For this purpose, we consider four decision axes:
Information consumption support:
Artificial intelligence brought the possibility of consuming information in new ways and with higher speed. Whether through graphical interfaces or voice commands, the truth is that the swiftness response determines the selection of the support, and both are undergoing systematic changes that impact the design process. Even in the case of graphical interfaces development, we are now witnessing a standardisation of different technologies and frameworks components, as a consequence of the existence of increasingly standardised mental-models among end-users. In a way, designers may feel some creative limitation, however, the truth is that the customer benefits from a much faster development speed and users from greater ease of understanding about the expected features and interactions.
Sub-groups of end-users:
The consideration of sub-groups of end-users is directly related to inclusive design. Understanding human diversity and adapting the experiences of the sub-groups to their perspectives with a view to creating synergies, can determine the effectiveness of a company's response to a given objective. When Scott Page (1) explored the importance of human diversity, concerning different perspectives and their benefit in problem-solving, he was far from promoting a direct association with the creation of better usability experiences. However, we were able to identify a strong competitive advantage in creating complementary customised experiences.
As an example, consider the B2B market and business information needs. The creation of a user experience directed to the needs of the sub-groups of end-users will guarantee an operational fluidity between the different business departments.
Consumption frequency and response time:
Within an organization, awareness of the existence of different job roles leads us to design solutions according to the frequency of information consumption and the necessary response time. If we consider an operating team, the update in real-time and the presentation of previous conclusions to make decisions at the moment will determine the capacity for continuous progression. Reducing noise and the need for interpretation are, in fact, the determining factors for success. If we consider top management, by contrast, a higher number of indicators will be needed over a longer time, as determining factors for the medium-term decision-making process. This means that if each group has access to the relevant information that best supports its functions, all departments will benefit from higher operational and strategic effectiveness.
Directly related to the previous point, the processing and analysis needs should be considered when creating the user experience. Nowadays, the excess of information requires that the analytical need is increasingly less. We are creating products designed to remove unnecessary decisions from the user's daily routine, reducing the number of options associated, however, with total transparency about this process. Within this model, achieved by the increasing use of artificial intelligence, we still maintain the analytical needs-oriented to the type of decision and sort of profile. A product designed to influence, for example, the strategy of a semester will differ from a product designed to determine decision-making every five minutes. In the first case, the development team must guarantee a higher number of options for crossing and filtering information concerning the second. Nevertheless, in both scenarios, with the ability to learn about user interactions, gradually reducing this need for interpretation.
After making the initial decisions about the creative process, allied to the previously defined information architecture, the user interface design emerges. Flowcharts give rise to wireframes, layouts and/or prototypes according to the type of information support selected, with the complexity of the project and the level of demand for acceptance tests.
Each feature unfolds into a set of user stories that will be represented by the aforementioned visual elements. The initial result of this process has long ceased to be characterised by invariability until the completion of development. Its growing relationship with innovation brought the need for reaction in environments of continuous change.
Following the principle advocated by Golden Khrisna in 2015, the best interface is the non-existence of an interface, insofar as simplifying access to information should be a concern and the neuroplasticity stimulus focused on the potential added value of the rapid interpretation of relevant information, and not a need arising from its complex analysis.
In other words, this is where we define how information is presented. Additionally, this information may be the result of an automation process, by using structured data systematically and repetitively or an artificial intelligence solution, using structured data as an input to find patterns, interpret it and present future predictions. In the last case, we do not design the solution, but rather the starting point for the search for it and, therefore, we must evolve together with the conclusions reached.
Through all the scenarios presented, the challenge of the diver-designer is to identify the forms of design that best suit the type of solution and the different profiles involved in it. The way we present the influential role of the designer in this process takes us to management functions, however, the truth is that the total involvement and knowledge on the part of the designer are a solid contribution to the local optima of each team involved.
As the information dissemination takes place in the ideation journey and takes place in a uniform perspective and full knowledge of requirements and needs, the responsiveness of stakeholders increases proportionally, and the various teams merge into a cohesive system prone to the approximation of the global optima.
In short, the consumer psychology and the loop feedback model are based on each user experience, guaranteeing an increasingly assertive and improved response to the user's needs. Presently, this type of response guides design and development and is increasingly required to keep up with market developments and ensure high competitiveness.
Full Stack Designer