Defining the scope of the trend prediction
Little has been written about the evolution of business intelligence (BI) reporting specific to macro trends in user experience (UX) in the last twenty-five years. If we take a decades-long view, an interesting picture emerges. If this view is combined with trends in social media, gamification, and leading web apps, one may be able to predict what BI UX will look like twenty-five years from now.
Please note this view is limited to
- Structured data. Unstructured data and search have different paradigms of how a BI user engages with the analytics.
- Focus on user engagement. It aggregates all types of users and most types of structured analytics.
More scope: Shorter term UI vs. Longer term UX
Let’s imagine user interface (UI) to refer to how a BI end user leverages features and functions on their mobile or PC device to view, modify and create structured analytics. Let’s imagine user experience (UX) to be an aggregated concept of how BI end users think, talk, share, sense, respond, and use the UI, the application as a whole, and other related applications.
User interface (UI) changes are important. They may be more important than total UX to the day-to-day experience of the end user. Especially the feature/function parts of UI. The give users capabilities to do things. However, the quarter to quarter and year to year feature/function battle between BI vendors* tends to co-opt UI progression. Topics like drill down, drill across, maps, inclusion of statistical functions in a primarily SQL environment are of low significance in a long-horizon UX look at trend(s). (In fact, it’s been surprising how longstanding many feature challenges have remained in BI tools. One could argue that in 1995 and in 2012 there is still no low-impact and easy way to see maps, text, images and other ‘variety-centric’ types of Big Data in traditional and even new BI tools. Another way of saying this is that the UX of reporting and analyzing these so-called ‘supported features’ is roughly equivalent now as it was ten years ago. In two words, not optimal.) This look into a macro view of BI UX trending for the most part ignores feature/function.
Even more scope: Why ignoring Big Data in BI UX trending is right
To predict how BI UX will change over the next twenty-five years, one must ignore feature/function and the energy around Big Data. Not because Big Data is hype or trend–it’s not–but because extending BI tools (at least the BI tools that encompass most of the market share in BI usage) to Big Data will fairly quickly or already devolve into existing struggles to accommodate this data size and speed. For this trend, we’re looking at structured data only so the Big Data axis of ‘variety’ can be ignored. End users have tried to use on-premise IT-approved BI tools to accommodate Big Data for years before Big Data reached the tipping point. Thus, accommodating it as a high-priority feature due to the Big Data market hype vs. accommodating it years ago as a nice-to-have is really the same discussion, just with more gusto. Today’s tools are more capable in meeting users’ needs where the data queried is hundreds of terabytes. Thus
- We ignore the axis of variety as we’re interested in structured. Observations show that 80% or more of analytics in today’s large and medium businesses occur with structured data.
- We ignore the axis of volume. Most of the BI UIs can stay the same and handle larger data behind the scenes without needing to change the UI.
- Velocity has been the most interesting driver of UI changes—aside from the creativity in self-service BI—in the most structured BI has required data loads of no more than once a day. With data ‘in motion’ or ‘on the wire’, previous UI don’t work. However, the macro BI UX trend results seen here seem to accommodate structured in motion analytics nicely.
If one can extract ourselves to a truly high-level view, we can assume we’ll absorb evolution and perhaps revolution in terms of velocity, variety, volume and other axes that we can’t imagine today.
The 200,000 foot view: From Report to User, Individual to Crowd
Early BI was focused on the concept of ‘the report’. We’ve seen an evolution already in the collaboration axis of line-of-business/enterprise analytics. In the 1990’s, the bulk of analytic work was between a single end user and a report. It was only in the late 1990’s that tools began to have the ability for other team members to see reports of others. BI vendors were experimenting with ‘groups’, subscriptions, and not close to enabling multiple analysts working on the same report. The center of the universe was the concept of the ‘report’ that would be built by a single user. In those days, the way to share a report with others was to print it out and hand it to them. Really!
I recall our analytics business in those days had a huge amount of intellectual property in data collection, cleansing, preparation, visualization and more. And we happened to sell software that included pre-canned reports. It became frustrating that the most often question asked by users was ‘Show me your reports’, ‘what reports do you have’, report, report, report. We could have eschewed work on infrastructure, support, reliability and so much more; built the largest number of canned reports; and sold more software. It was as if we’d built and offered the best kitchen, utensils, food inventory, food delivery, and the prospect wanted to see the menu of left-overs in the fridge ready for reheating. As a teaser for where we are going, they never asked about who else was using the kitchen, who might be making the left-overs, or certainly not about pizza delivery.
From 2000 to 2010 the industry has seen a very positive change to this approach. The central ‘object’ and focus is still the report. However the migration has been along this path
- Report viewable by the creator
- Report viewable by the company (this step actually allowed for IT to be used more often than before as ‘report creators’)
- Report viewable to team members
Report as ‘golden calf’
Then a disjunctive transition to report object as central viewable site or target. Almost as if the report itself was a location, a phenomenon. This went along with the advent of Tableau Public–and there may not be too many analogs to Tableau Public–and led to our current phenomenon of infographics. Reports as a water cooler. Reports as central, statement-making attractions that are easily locatable, sharable, viewable, comment-able, and that may push the ‘crowd’ toward a singular or common idea or action. Our common technology advances in Web 2.0, social media, and general web UX have led to transparency, communication, and the ability to achieve something closer to ‘worship’ for the best, single report. This is ‘report as golden calf’**. While there is a huge focus on infographics, the irony is that they are fairly non-useful for the bulk of analytics work. They cannot be pasted into a Powerpoint slide (too big). They have evolved to require a graphic designer (too expensive). They require research (too time-consuming). They are based on external research, web data or stats (not BI analytics). So perhaps infographics are out of scope. However, they could also be viewed as the unstable pinnacle—and final stage—of our obsession with ‘the report’. The report will never go away. It is amazingly valuable. In some sense, at least with today’s technology, perhaps the only way to derive most insight. However, just as nothing looks the same as twenty-five years ago, as we go into the future, some day we will look back and say “That was analytics in the way-back past. Archaic! How did anyone derive value from that?”
Symbiotics of Report as Golden Calf
At the risk of driving the quants, data scientists, Cartesian, logical readers crazy, there are a host of right-brain adjectives and phenomena that signify and represent the evolution to report as golden calf. These have been consistently gaining traction and attention! Perhaps best described as milestones, flags, or tailwinds, these are symbols of the pedestal we’ve been able to achieve in bringing data into single viewable insight-transmission devices. These include (and ideally presented in a tag cloud!)
- color graphics, infographics
- multiple reports, widgets in one report
- data marketplaces
- merging publicly available data with private data
- analytics on publicly available data
- cloud security being more conducive to group analytics on public data than private data
- Netflix Prize
- reports as Flash objects
- reports as videos showing time series progression and data layering onto the chart–the report is a ‘movie’ and movies in our culture are watched by many, a social event as much as information transfer
What happens next?
The next phase of the process is placing the ‘user’ at the center of the process. No longer will the question be: what reports do you have? The question will be: what users do you have? Who is changing, adding, commenting, analyzing and sharing? Who is speaking? What voices add to the report? Who is improving the report?
An interesting place to look outside of BI is the process of ‘gamifying’ applications that are not games. In the gamification of applications the end user is often ‘placed’ at the center of play. Further, in our culture in 2012, the center of our digital engagement is ‘me’. The profile on Facebook, the profile on LinkedIn, always the profile. And a network of engagement with friends, brands, colleagues, and content. This next wave will focus on the power of engagement, drawing many more users into BI–just as Facebook’s simplicity and ‘Me’ model has drawn many more citizens than we’d ever thought possible into digital communication. The stream of data and analytics will be network-centric. Imagine a Facebook where instead of friends we have business analysts, data scientists or insightful people, and the stream of things they say are ‘reports’. Thus more communications about the data before the report, the context, the insight, the drill-downs, the next logical report…all the things that actually make a structured BI report valuable.
The Facebook-ification of BI
In Facebook, it is super easy to start and super easy to engage. For even the ‘least capable’ of computer or mobile users. As the components needed to support a central ‘Me’ rather than a central ‘Report’ are solidified, it will be super easy to start and engage. Start and engage will look like
- creating a profile
- be incentivized through rewards, fun and other draws to participate in others’ analytics and insights via comments, derivative analytics, derivative visualizations (this step requires no data of one’s own)
- select from public data for rudimentary analysis
- upload one’s own data
- do analysis
- share reports
- present insights
- comment on others’ reports
- comment on data
- clean other people’s data
- make reports on their data
- and so on…
Unlike previous BI, at each step of this engagement process, the step is its own reward. Some users will stop at an early step and find enough value to stay engaged. Just as a simple Facebook user with limited computer skills may simple be on Facebook and never post but just read family updates, a simple user may just read analytic updates. However, with gamification components such as relevant/friend/motivation-adjusted leaderboards, points, avatars, mastery, badges, and more, the ‘Me’ BI tools will slowly start to drive users to
a) understand and trust the ‘mayors’ in their own organizations and spheres, as they do now to some degree but to a much wider scale and effect
b) move up the path to BI mastery, driving quality and frequency of insights and action increasingly
c) find more relevant peers to connect with related to similar datasets, action potentials, vertical industries and more
d) see increased business performance from the increased action
e) reach states of cleaner data as there is more reward from self-cleaning and cleaning others’ data
f) enjoy analytics
The move to center focus on the host of ‘Me’’s all around the world driving analytics, doing analytics is a key step. Without this step, there is little shared knowledge in a relative sense. When suddenly we have a vast Twitter-like communication noise about insights, insights will be so prevalent that one will have a harder time steering clear of insights and cherry picking than of trying to find them like finding needles in the haystack.
Post-‘Me’ BI UX
There is certainly a chance that the ‘Insight’ phase could take the central role rather than the ‘Me’. In some ways, were the insight to take the central role it would be more value-added to the universe of users. But it is too soon for that to happen. Without a solid underpinning of the ‘who’ there is no platform for insight to be understood, validated, and put into context. Unfortunately, it is likely that our society of BI users will have to progress and learn through at least a decade of ‘Me’ before the insight and action can come to the fore.
When the ‘Insight’ phase occurs, it will actually be single sentences of truth. An insight should be able to be summarized and stated in a single sentence. If it cannot be, then it is not (yet) an insight. This will be transmitted to the user via a ‘Siri’-like voice or a single sentence of text or via some other method we’ve yet to see. A data set should show just as many interesting insights as a look at a beautiful park or a walk through a museum, or watching a business presentation. In every example, there are perhaps fifteen to forty specific points/exhibitions/sights/objects, and the normal human will walk away perhaps experiencing joy, education or wonder at all of them at the time, but will remember only one over a long period.
Post-‘Insight’ BI UX
No one can say just what analytics will look like in 2030 or 2040. There will be reports. There will be analysts. And there will be insights. All that said, it is very likely that the gravitational pull of the data will absorb the insights and actions to its very source. That data will not exist outside of action. The very systems producing the data will be enabled to absorb, analyze and act on the data. The question being asked at that time of every part of our lives may be ‘How involved do humans really have to be in this?’
How will it appear 30 years out
To the BI tool user fifty years out, the progression should look something like this
- Data (1975-1995)
- Reports (object or data focus) (1990-2015)
- Users or ‘Me’ (profile or community focus) (2015-2025)
- Insight (2025-2030)
- Action/Embedded…the end of analytics (2030- )
As Moore’s and other non-linear laws of compute progress, the first or second derivative of these should apply to the BI progression, making each phase shorter than the one before. Further, during each phase, all of the objects of the previous phases continue to grow and evolve. One can imagine that ‘Big Data’ is a transformative evolution within the previous phase object ‘Data’; likewise during the ‘Me’ phase of socializing BI, there should be a transformative evolution of what a Report is. Maybe it like a magical garment, when shared it will adjust its size, shape and color to the business question, data set, and capabilities of the user to whom it is transferred.
* BI tools include applications for large and SMB enterprises such as MicroStrategy, SAP Business Objects, IBM Cognos, Tableau, Birst, Domo, GoodData, Qlikview and similar firms.