Data as a Feature: Building Applications that Help Users Achieve Goals with Data

TIBCO and projekt202 on the Value of Data as a Feature

In this video presentation, TIBCO Product Marketing Manager Shane Swiderek and projekt202 CTO Rob Pierry discuss data as a feature and the benefits of building software that helps users achieve goals with data.

Thanks to TIBCO Software for the production and use of this video.

Highlights from Shane’s and Rob’s presentation follow.

Shane:

What we're talking about today is building applications that help users achieve goals with data.

… So what the heck is data as a feature? Why should you care about it? Secondly, we'll talk about how to accomplish data as a feature at a very high level … we're going to talk about how a data as a feature application was actually developed and what that sort of looks like.

… Data as a feature is the act and process of treating data as a core feature of a software product or service in a way that delivers benefit or benefits to that end user … So why should you care about data as a feature? Data-driven businesses in the next couple of years are going to beat out, or out-compete, outperform, however you want to phrase it, businesses that aren't data driven.

There's a quote here from Forrester Research, and they actually take it a step further and say that insights-driven public companies will continue to grow at an average of 27% annually, and the startups will grow 40% annually, much faster than the projected global 3.5% GDP growth. Whether we're talking about insights-driven or data-driven, all of that is based on data. That's what we're going to be talking about today. How to make data a valuable asset as a part of your product or service.

While we know that data has incredible value, we also know that it can be very difficult to extract that value from it. I'm going to give you one more quote from Forrester Research, and again, you probably heard some version of this before, but between 60% and 73% of all data within an enterprise goes unused for analytics. So why is data so hard? The answer is because we're not robots. Data is inherently not built for human consumption. So we need to package it, prepare it, visualize it in a way that's very intuitive and easy for users to understand.

We need to make data about the user. When we talk about making data about the user, it needs to be intuitive or simple to understand. Understand what the types of goals your users are trying to accomplish and then work your way back to the visualization that's going to best help them reach that goal. It needs to be convenient and available in the right context.

… Also customizable. We can't expect to predict every question that users are going to be able to have. They need to have the ability to filter that data customized views to answer the questions that they're trying to answer … All of this kind of leads up to intuitive, convenient and customizable giving people the ability to view, consume data in a way that's understandable for them, and then they can take action, again, based on either the operational or transactional application that they're engaging with on a regular basis.

If we take just product and data, that's really we're putting data inside a product. If we take data and design, that's really creating beautiful or intuitive data visualizations. If we take product and design, we're talking about really great application UX. When you combine all these three things together, that's when data becomes extremely valuable and usable for your users. You need to be thinking about each of these three if you do want to make your data, create your data as feature application.

Delivering the data experience that users want is a complex and never-ending journey.
— Shane Swiderek

Rob:                     

What's that about? It's really about making software fundamentally. Making software that delivers business results, that has an effect on your users, whether those are end customers, whether those are internal users of your system. Now the notion of what it takes to make software has evolved over time. Recently, and depending on how far back you want to go, you can talk about incorporating the idea of UX. Software needs to be designed so it's intuitive and it's easy to use.

But that idea is fundamentally derived from this recognition that it's actually people who use software. Software is for people. People it turns out don't behave like the ideal abstractions that a lot of developers want them to do. They don't take inputs and produce outputs that are exactly the same every time, and do stuff at the same rate every day. They've got different stuff going on. They've got good days and bad days. They've got a bunch of distractions. They're probably trying to do 25 different things and everybody is different.

Fundamentally, that informs how we approach this idea of building software. It's for people. We better figure out who those people are and what their goals are. What their aspirations are. I think in keeping with this idea of data as a feature, the answer to that is kind of more data. But it's not quantitative. We want to understand qualitatively what people need, what their goals are, how they operate, how they think.

So what this looks like is kind of a bit taboo if you've been following Agile for a very long time, because upfront, what we like to do is some thinking. We like to go out and figure out who these people are, and we want to go out into the field … really dig into what people actually need and how they work. Then that ultimately feeds through this multidisciplinary, sort of collaborative, let's go out and build stuff.

We figured out what people need, now we can find sources of data, we can look at existing technologies, we can put them all together into the application that's going to help them do what they need to do.

… We want to actually go out into the field to gather, we want to study people. We do that by using behavioral scientists, people who have anthropology backgrounds, or psychology backgrounds, or sociology backgrounds. People who understand people. We go out and we watch them … so that affects the way that you need to build a system to be able to reach them and do what you need to do.

The fundamental idea for all of this though is know who you're making stuff for.

We ultimately build up this sort of objective understanding, again, we're taking the human data to understand all the people who are going to be using our systems.

What is this person thinking of at this point in the journey and how do I use that to make my decisions? We are consuming this data in the same way that the users of your data-driven, your data as a feature applications are going to be consuming that data. We want to make it actionable, we want to make it intuitive, we want to get things communicated. That ultimately leads to making software, designing it and building it.

… projekt202 is actually technology agnostic. We build stuff in all of the major platforms … Our customers understand the importance of design. I think just like you've got the Forrester information that says data-driven companies are going to outperform, insight-driven companies are going to outperform. Forrester also has a lot of great stuff to say about every dollar spent in design is worth 100. And design-driven companies are going to outperform.

What that means is, if you make this investment in figuring out who your users are, and then you make the invest in actually designing their interface, instead of letting the developers just pushed up together, you don't want to get all the way to the implementation phase, and have your platform vendor tell you, "Nah, it's not supported. Hey, that's great and all, I know you know exactly what your customers want, but all I can give you is something that looks like that old screenshot, because that's what's in the box."

So that's what got us here is we don't have to compromise in the design. That's fundamental. We're going to put that time and effort into understanding people.

… Referencing Shane's earlier points, data really is everywhere. Companies who know how to use it are going to outperform those that don't … what data do we have and how might it be useful? We don't have to start with perfect. We don't have to ingest everything and have it fully cleaned, and start doing this machine learning or AI stuff over it, that's nice, but even just having some basic reliable data that you do, run your regression over is great. Like even be able to have accurate data in the dashboard is great. Start small, because this stuff builds on itself. But actually think about it, what data do we have and how could it be useful?

Published Oct. 26, 2018


projekt202 is the leader in experience-driven software strategy, design and development. We have a unique and established methodology for understanding people in context — we reveal unmet needs — which drives everything we do. This leads to a crisp, clear understanding of the customer, which shapes the design and development of new solutions and experiences. We have the expertise, teams, skills and scale to deliver sophisticated software solutions that improve any and all touchpoints across the user journey.

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