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Designing the Next Generation of Decision-Support Systems for Business Executives

January 5, 2015

The evolution of digital ecosystems—such as wearable devices—is quickly making the channels by which users interact with devices more efficient and effective. We can only imagine what the Internet of Things will bring tomorrow, but today, we’re already reshaping mobile user experiences to provide a more accurate, granular, context-sensitive experience for all domains. This new reality turns out to be particularly important for business executives.

Decision-Support Systems: The Context of Use

Before we can properly analyze a decision-support system and its related ecosystem, we need to understand its users. When talking with executives in large, multinational corporations about their needs, you’ll hear a lot about performance management—including such issues as tracking KPIs (Key Performance Indicators) and metrics—and effective communication.

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Business executives have plenty on their plates and need to prioritize things carefully—whether it’s the information they absorb or the time they spend communicating. But their preferences regarding new technology and the channels they use when working vary depending on the domain in which they work. Of course, more and more senior-level staff across all domains now tend to use state-of-the-art technology. The hopes and dreams of executives correlate with the goals they set and the progress that they make. Executives want access to information at any time and in any place, want data to update in real time, and need to react immediately to critical events. All of this seems reasonable for a person sitting at his or her desk the whole day, using a computer with a strong Internet connection.

However, today, we cannot design a good decision-support system without considering additional contexts and other parts of the ecosystem in which executives live and work. The majority of senior staff travels frequently and typically conducts their work by making brief contacts via smartphone, tablet, or a notebook computer. They participate in online meetings and conference calls; and communicate via instant messenger or email. Their schedules are tight and their time and attention cost a lot.

Decision-Support System Components

Now, let’s take a closer look at the components of a decision-support system. Executive dashboards include more or less standard data points. Features that are usually present include data that has been mined from various sources—for different time zones, standards, and goals; multi-channel content delivery; and predictive, prescriptive analytics that make a decision-support system a really useful assistant.

However, executive dashboards are useful only in cases when a user has some time and access to a computer or, at least, a tablet. Rarely are executive dashboards designed specifically for a smartphone and almost never for a smart watch. Plus, these systems usually do not sense the contexts in which a user lives and works. Notifications don’t take calendar events into account. Action items are not tied to the data that devices gather about the user by parsing email messages or analyzing search histories or any other statistics.

Decision-support systems must learn a lot about users to become really useful and increase executives’ productivity. They must also be secure because they store, process, and display both personal data and strategic corporate information. Decision-support systems should have all of the best qualities of a real-life assistant. They must be

  • professional—They must have all of the necessary features to serve as a proper assistant.
  • useful—They must possess sufficient knowledge about an executive and the company for which he or she works.
  • helpful—They must appear whenever they are needed, but should not be distracting.

Decision-Support Systems for Short-Term and Long-Term Business Goals

As additional sources of data such as physical sensors and software trackers supplement decision-support system, they are becoming more and more responsive. This responsiveness brings many benefits—from an improved, overall user experience to the possibility of actually interacting with the system instead of merely consuming its content—making processes more efficient across an entire company. This makes it possible to hit most short-term, tactical goals.

To assist with strategic, long-term goals, decision-support systems should be able to work with Big Data and perform not only predictive analytics—which tell a user what may happen in the future and the odds of events actually happening—but also prescriptive analytics—which define what result is most likely, as well as future risks and how to avoid them by being proactive. The next generation of decision-support systems will see further ahead.

A UX Designer’s Takeaways

A designer who is about to start working on a new decision-support system must do the following:

  • Do thorough user research, focusing on the context of use.
  • Do service-experience mapping, outlining all channels that an executive might use—or those that you would advise their using.
  • If you’re not a data analyst or a data-visualization expert, make sure that you have one on your team. If you are, be sure to work with a UX designer with a solid user-centered design background.
  • Remember, decision-support systems are usually very large and complex, so start with a minimum viable product when creating your first prototype.
  • Despite the fact that executives’ time is very valuable, you must test your prototype with your actual audience. Finding more than one executive to be your advocate and devote significant time to your project is nearly impossible. However, it is essential that you identify any major gaps in your working product as quickly as possible. Otherwise, your decision-support system will turn out to be much more expensive—for two reasons:
  1. The more complex the system becomes, the more resources redesigning any poorly conceived, badly designed features will require.
  2. Any decision-support system that causes executives to make bad decisions is worse than having no decision-support system at all.

Conclusion

Every business executive works under pressure and on a tight schedule. Thus, they must be able to collect the background data that they rely on for decision making in business-specific, decision-support systems such as executive dashboards and the ecosystems that surround them. Context is king and determines not only dynamic information architectures and system logic, but the ways in which users interact with various channels within an ecosystem. The representation, order, amount, and density of data must continuously adapt to a user’s current context of use, conveying just the right amount of information on the appropriate device. 

UX Designer at SoftServe, Inc.

Austin, Texas, USA

Andrii GlushkoAndrii has created high-quality user interfaces that ensure optimal user experiences for numerous desktop, tablet, and mobile applications. He has defined effective information architectures, conducted usability assessments and heuristic evaluations, and applied best practices and guidelines for various platforms. His experience includes many healthcare projects. Andrii blogs regularly on the SoftServe United blog.  Read More

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