Let’s get macro and talk about data strategy. In the article “What’s your Data Strategy?”, published in the most recent issue of the Harvard Business Review, the authors describe a data management framework that “requires flexible data and information architectures that permit both single and multiple versions of the truth to support a defensive-offensive approach to data strategy.”
The original article covers broad principles aimed at management decision makers. In response, here’s a subtext geared toward UX designers, content strategists, product owners, and service designers to connect the dots of how user experience can support a few of the data strategy elements outlined in the original article. I hope you walk away with actionable insights of how to make good decisions on behalf of people that use your products and that are, in turn, good for your company’s execution of a comprehensive data strategy. After all, data is only as good as people’s willingness and effectiveness in engaging with the tools that a business provides.
2 Overarching Bits of Knowledge
Firstly, if the word “data” sends you running for the hills, think about it like this: Every interaction that a user has with a product or service is a data point. That didn’t hurt too much now, did it? Data in this sense is a byproduct of human interaction. Data alone is not enough, however, when paired with the humanist lens of empathy, the understanding of context is just that much more vivid.
Secondly, companies are swimming in data and playing a game of catchup in managing and making meaning of it all. The need is real; in a cross-industry study, less than 50% of structured data is actively used in decision making. The target exists out in the wild; much of Netflix’s success is owed to it’s use of insight from user generated data. With this in mind, companies know they need to shift. The original article provides a strategy for prioritization of the complexity of data management.
What is important to note is that the article recommends two types of data management strategies: defensive and offensive. To paraphrase, defensive data management is characterized by downsizing risk, and focuses on controlling the flow of information through a single source of truth. It’s a priority for highly regulated industries like healthcare. Offensive data management, on the other hand, is characterized by the integration of disparate data sources into meaningful insights, analysis, and modeling. It relates to customer satisfaction, profitability, revenue, and flexible data. And it is a top priority for flexible industries like retail. Essentially data management strategy must balance both defensive and offensive strategies for a comprehensive approach.
As a side note, the ethical appropriation of data use that protect the rights of individuals is important and if you’re into that sort of thing, check out the Electronic Frontier Foundation.
UX Best Practices
Regardless of whether your organization has a data strategy, every UX designer should be asking these questions right now:
Defensive Data Strategy Guiding Questions
- Does the interface ask for information in a way that will introduce risk to personal information?
- If similar information is displayed inconsistently at various touch points throughout an application, ask a backend developer if there are different data sources for that seemingly congruous information. If there are different data sources, ask why. Begin tracking which information is stored where and keep track of this knowledge.
- Are users required to navigate to disparate parts of a system to complete simple tasks.
- How might this workflow be more consolidated?
- Identify how many access points to Personal Identifiable Information there are. Ask why users are required to enter in the same information about themselves multiple times. Does this mean that their info is stored in many places? Is this in the best interest of their personal security? Propose a single source of truth: user profile.
Defensive Data Strategy UX Activities: Mitigating Risk & Simplifying
- Information Architecture Audit Goal 1 – Identify duplicate input information and make recommendations on how to consolidate it.
- Information Architecture Audit Goal 2 – Also, identify similar information in disparate parts of the system and illuminate the nuance between the two. Use this as an artifact to start a discussion with your data team about why there’s a discrepancy of information in different places. How can the data quality be improved? Can it be consolidated?
- Information Architecture Goal 3: Propose a unified user profile. Illustrate the many places that users currently input their personal information to show how this will not only benefit user efficiency but also data privacy.
Offensive Data Strategy UX Questions
- Will consolidating this taxonomy, increase the ability to more accurately measure user-generated insights?
- Would my company or client benefit in profit, revenue, or operational efficiency from analyzing the decisions users are making on my product?
- Do various branches of decision trees follow the same workflow so that each decision (data point) can be isolated and compared to it’s alternate choice?
- Essentially, are the behavioral variables measurable in a meaningful way?
Offensive Data UX Activities: Increasing Measurability & Learnability
- Catalog the current taxonomy of all drop downs menu choices throughout an application, especially if there are multiple touch points to the same decision. Propose unifying the items into commonly shared phrases and names across the application. This both reduces cognitive load on user to remember what things are referred to and also improves the accuracy of behavioral data analysis for company insight purposes. This way, when someone pulls a query on a given phrase there aren’t leftover alternate phrases referring to the same thing that are excluded from the data set.
- Standardize workflows to set consistent user expectations and give companies the ability to isolate metrics of decision making. Even if the options presented to a user are many, propose an interaction model that supports them all.
Out in The Wild
Last year I applied for a pass on the Global Online Entry System (yes, there’s a cute acronym in there). Even for a government website, it was a surprisingly difficult form to complete. It was page after page of re-entering the same information about my name, social, birthday, past residences, work history, etc… With a growing curiosity about the task completion rate I wanted to call it quits. 1 hour 15 minutes later, I was finished and that time on task was etched into my memory as a stellar example of what to avoid when designing.
Efficiency of use is a longstanding principle of interaction design when discussing the structure plane of a product. This example is a common design problem: low user efficiency due to duplicate input criteria, especially with legacy applications. What many user experience designers may not be aware of however, is that duplicate input criteria often points to a multiplying risk in data quality. Yikes! It’s a likely assumption that multiple input fields in legacy applications are required because there are multiple data storage solutions housing various parts of the application. Duplicate user input criteria of sensitive personal information like social security numbers establishes multiple sources of truth.
In the age of big data, where data breaches are increasingly common, empathy for users includes stewarding their information in a way that earns their trust. So, as the amount of data increases, the level at which we understand human computer interaction broadens. How do these concepts relate to products and services you create? Thanks for taking the time to read this, and I welcome your thoughts and conversation!