For the UX practitioner life is about to change. In my opinion we are about 5 years away from having data/AI driven UI. Are you ready?

The future of UX will be highly affected by Big Data and AI (Artificial Intelligence). The way we design for our users will change and that change will be significant. User-centered design practices will be instrumental in the birth of these new technologies. Where traditionally we have used Information Architecture (IA) to create content hierarchies and data flows, with the introduction of Big Data and AI, we will need to better understand how we will present the significant amounts of user-centric data in a way that best meets the individual users needs. That seems not to be anything new but the process for designing these flows will become more and more important. The complexities of displaying large amounts of democratized data targeted for individual users of an enterprise system could be staggering. Governance and flexible design systems will take a front row seat in our effort to present a wide range of individually targeted content.

Will we be relegated to creating flexible templates that lean more towards logical content holders? Will we be able to present that content with rich designs and captivating layouts? My belief is yes, but it will rely heavily on thoughtful, flexible design patterns with strong foundations in design standards. With 20 years experience in enterprise software UX I still remember the awe of my first reading of Jeffery Zeldman’s “Designing with Web Standards“. His book and the Happy Cog Website changed the way I viewed CSS, HTML and JavaScript and changed the way I designed software. I remember buying copies and gifting them to our solution architects in an effort to initiate fruitful collaboration and engaging technology conversations. Likewise, using the examples from “CSS Zen Garden”  I was able to illustrate the true power of Web standards.

Web Standards is the first step in the design and development of future AI/Big Data driven applications. The second is the democratization of data–freeing data from its structured beginnings and transforming it into flexible and consumable unstructured data. Thus the future state will be thoughtfully designing new ways to logically present user-centric data/content to individual users of the same enterprise applications, in flexible, well thought out design patterns.

Recently a colleague shared with me a link to a new product called Kumu. Basically, Kumu “reduces complex relationships into compelling visualizations, fast.” Combining visually stunning mind maps and visualizations that rely on facetted data to build complex and logical relationships. Thus, content that is quickly segmented/organized by the individual user. In my opinion, this process will be very similar to what we as UX architects and designers will need to do when creating the data/user-centric Web applications of the future. Applications that rely on the individual’s needs and present them in collective enterprise solutions.

The common thread between successful UX, AI and Big Data will be keeping it aligned to user-centered design principles. Providing thoughtful flexible frameworks and design patterns that allow for user-centric collaboration in enterprise solutions. How will we do this? My belief is that we will first need to deconstruct the presentation of content and align it to a targeted set of personas/users. We will then need to discover content that is general and content that is role-based and thoughtfully design frameworks that are consistent and moreover conversational in presentation. Discovering new ways to present content/data in a more narrative style, more addressed at informing or leading the user through specific processes. Using Big Data and AI to Know me, Anticipate me, Nurture me, Listen to me, and Guide me. Transforming our designs from interfaces to  “colleagues”.

Over the last year I have been tasked to partner with our Design Thinking team in an effort to better understand their processes and how to best align them to our UX delivery. In that effort I have collaborated with both teams and what I have come up with a repeatable process that uses what I have called “The Tryptic of UX Discovery”.

The tryptic consists of personas, user journeys, and a two-by-two matrix. All of these are common discovery techniques shared by UX and Design Thinking. Together they thoughtfully combine to provide our clients with a vision of their current issues and a plan on how we will address the solution.

We start by thoroughly researching our clients current systems, user interactions, and financial feasibility. Basically the Design Thinking tenants of Desirability, Viability, and Feasibility–in other words we research if the users are looking for changes, does it meet the Business’s expectations, and are they technologically mature enough to advance the efforts.

The process involves the research and creation of personas aimed at representing the specific users of the solution, developing user journeys to define user-centric views of the existing/proposed process, and road-mapping the proposed innovations based on affordability and effort.

Personas. We begin our research to expose those users who will be interacting with the system and who will be most affected by our proposed improvements. We then create personas of the selected representational users. Instead of coming to our first meeting with a blank persona template, we involve our research teams from our Far East Studio to develop “straw-man” personas. This provides us with the ability to create highly visual yet editable personas which act as accelerators for client participation. It is much easier and time efficient to edit rather than create.

User Journeys. Collaborating with our clients we then choose personas and processes that we feel will provide the greatest opportunity for innovation. Mapping out a “day-in-the-life” of a user interacting with the system provides us with all the touch points and data needed to discover opportunities for improvement. Each touchpoint aligns to the user, their environment, and exposes their empathy in performing the selected task. In turn, this information aligns to selected technology and creates opportunities for innovation.

Two-by-two Matrix. Once the opportunities for innovation are exposed it’s time to deep-dive into the technological feasibility of bringing these innovations to life. Sitting down with our architects, we collaborate to map out the proposed innovations and the feasibility (based on technology maturity and costs) and provide our clients with a roadmap for completion. Working together with our clients we assess those innovations that are lowest hanging fruit, those that can be combined, and those that are more complex and expensive (e.g., hardware solutions, data conversions, enterprise realignments, etc.). The goal is to quickly provide a win-win delivery in the beginning that builds confidence and a roadmap to further innovation successes in the future.

Our teams have found great success using this process and it has been beneficial in both large and small engagements. The key success factors center on the alignments to a user-focused, business-aligned, and technology feasible solutions developed in collaboration between all the disciplines (e.g., users, business and technology).

For many years we have been bombarded with the importance of Big Data. It hasn’t been but for about 5 years that we have really been able to act upon it. Many of my clients have been collecting data but when asked what they are doing with their data it quickly becomes evident that they are just storing it. While the Google and the Facebooks of the world have been actively investing in data science, most of my client’s enterprise applications are still storing their data in structured databases.

It is my opinion that until we transform structured data to unstructured data we won’t be able to inherit the true innovation that our future technology requires. The future of enterprise application development requires the democratization of data-the increased access to consumable data.

To understand the power of how big data can positively affect our future technology, I’d like to introduce you to the historical work of Toshifumi Suzuki, Chairman & CEO of Seven & I Holding Co. Suzuki is credited as the mastermind of Seven-Eleven Japan’s rise to power in the retail vertical. And it is my opinion that Suzuki is the visionary of modern data science through his creation of Tanpin Kanri.

Tanpin Kanri
“A retail management practice focused on satisfying customer demand through a store-by-store approach to shelf management that employs store-level human knowledge and information sharing about products, for the purpose of better understanding how certain conditions affect demand on a product-by-product basis, and then pursuing a cycle of product procurement, production, development and delivery that suits the demand.“(1)

Suzuki created a user-centric approach to inventory management by empowering his individual store owners to observe consumers and collect local data to influence demand and local inventory selections. He achieved this through emphasizing the understanding of customer psychology, observed buying patterns/influences, and focusing on three key factors that motivate consumers–time, context, and weather.

Retail is innately affected by time. Consumer purchase based on a schedule–breakfast, lunch, dinner; before and after work; payday; weekday and weekends; participation in exercise and local social events. In Suzuki’s thought process these segments of time create demand.

Context is everything. To further influence demand retailers should actively consider competition, consumer interest, regional palettes and most importantly, from a geo-specific/local perspective. Every neighborhood has its specific influences and cultures.

Our daily activities are governed by the weather. Every season has influences on consumer’s palettes–Spring influences health, Summer has ice cream, Fall insights family meals, and Winter encourages the hot meal. When it rains we all yearn for cover. When it snows we seek warmth, all of these climactic changes spurn retail demands.

Tanpin Kanri was created to provide on-demand inventories to eliminate merchandise that was stale or what he penned were “shelf warmers”. To do that, Suzuki empowered his local store employees to become retail scientists who were focused on researching and understanding their customers from a user-centric perspective. He also provided them with an open line of communication back to their suppliers upstream so that they could provide products based on the shifting customer demand. A big improvement to the suffering, traditional seller’s markets.

Here is where I would like to introduce why I believe Suzuki was the father of modern data science. Through Tanpin Kanri, Suzuki initiated the first step by his emphasis on data collection and its focused attention to local consumer activities. By empowering the collection of consumer data from a user-centric/local perspective, he was able to provide fresh, context specific, and actionable merchandise, on-time and aligned to targeted consumer demand. This concept is equally important to our current economy and technology need. Our users require timely, context specific, locally affecting data to meet the demands of today and future technology requirements. To do this we need to provide user-centric, fresh data in real time and context specific.

This brings us back to the availability of client specific, democratized data. Just as Suzuki empowered his employees to collect and provide the actionable data required to affect their specific POS requirements, we need to free the structured data that is currently the norm for most of our enterprise clients. Without an easy access to the many alternate forms of data that exist in modern corporations we will not be able to create the innovations required to bring us to the perceived future states of technology we are currently bringing to market.

(1)- Harvard Business School, Tanpin Kanri: Retail Practice at Seven-Eleven Japan, by Rajiv Lal and Arar Han, Rev: February 23, 2011. 9-506-002