We are coming into the golden age of analytics. This is the vision that the speaker – CEO of a company that develops data visualization software – illuminated for the audience at a recent customer conference. “We are putting the power of data into the hands of creative people to explore the worlds of possibility.”
The idea that we can use our talents to solve client data puzzles is like an adventure that makes it fun to come to work every day. We’re explorers in an unknown land, moving from all the standard questions (and the standard answers) to a place where the questions themselves haven’t been formulated yet. New thinking, contemporary sensibilities, and breakthrough technologies are disruptive factors in this age.
One area I pay attention to is the continued evolution of business intelligence (BI) in a mobile environment. Mobile BI received a lot of fanfare in the past year, and all the major platforms promote a mobile solution.
Now that some of the hype is starting to settle down, what is the real story? Here are a few thoughts based on my own observations and experience.
1. Mobile BI deployments will favor use on tablets, rather than smartphones, given current screen sizes.
It’s easy to refer to “mobile” like we refer to “Europe” as a single form factor or entity. The reality is more complex, as there is a range of devices from the smart phone, with relatively small screen sizes, to tablets, with screens just a bit smaller than you’d see on a typical ultrabook laptop. More screen space gives us more room to place data and provide interactivity. I see organizations prioritizing tablet deployment over smartphone deployment in most cases.
2. Business gains so far have been incremental, providing efficiencies rather than game-changing breakthroughs.
Many mobile BI efforts seem to focus on converting the oodles of reports hanging around every corporate office into a mobile format. That makes data more portable, which is an improvement. But what we should seek to do is to make the analysis and decision-making that goes along with running a business happen in a portable way too – where you are, right now, as soon as information becomes available and action is needed. This concept of “right-time mobile analytics” (not just mobile BI) is where I believe transformational gains will be found.
3. Design principles need to evolve to better anticipate the needs of mobile tablet users.
Most mobile BI efforts seem to be focused on cramming the dashboard or report that was designed for a desktop user into a mobile screen. There are several problems with this approach. First, a dizzying array of formats, resolutions, and screen sizes is present in the market. It’s reminiscent of a challenge with website design, where you don’t know what kind of screen the user will have for their desktop.
Beyond screen size, you quickly discover that dashboards and interactive visualizations that are crammed onto a mobile device are balky to navigate when you substitute fine-point control of a mouse cursor with the more generalized notions of a tap or swipe. Analysts and designers should invest the time to redesign existing visualizations and reports so that they can be easily and efficiently used with these new human interfaces.
4. Mobile BI tools do best at providing answers to known questions, rather than providing a platform for rich and interactive data exploration and discovery.
Largely due to the factors and challenges related to the interface, we have not seen a good mobile implementation of the interface needed to explore the data and design new visualizations. Sleek interfaces that enable and facilitate data discovery, such as the forthcoming update to Tableau’s Desktop Professional software (v8), are getting there on the desktop, but are very limited on their mobile implementations.
I’m not sure we should even care, because this may be a square-peg-in-a-round-hole problem. I don’t see the compelling need right now to port that capability to mobile, when most of the value in mobile will come from deploying effective, efficient visualizations to those who need better information to make right-time decisions, rather than enabling analysts to design new analyses on-the-go (and burning through their data plan in the process).
5. Standardization on a single mobile platform can significantly reduce development timelines.
This will help reduce complexity and allow you to dip your toe into the water with less up-front investment. Fewer permutations of screen size, operating systems, and wireless carriers will reduce the time needed to deploy your solutions (little secret: this is one of the reasons that vendors originally delivered on Apple devices – you could predict how your software would look to the user!).
Apple, with its line of iOS devices, has the best track record in this area. They have smartly positioned with a very small number of screen size variants across the iOS hardware platform. And several studies have also shown that historically, users of iOS devices generated a significant majority of all mobile device traffic over any other platform. Therefore, I believe that an engaged user base and a streamlined development lifecycle will favor adoption of mobile analytics solutions that operate on iOS devices.
6. Support the rollout of cellular-enabled mobile devices throughout the enterprise for knowledge workers that can best leverage mobile analytics applications.
Yes, this makes each tablet more expensive, and yes, cellular data plans are not free. But neither is the lost time fumbling for the client guest intranet login, or roaming the highway off ramps looking for a coffee shop with free WiFi. The investment from all of this valuable data and analytics applications will be reduced if your knowledge-based workforce cannot connect where they work, live, and travel.
Looking to the future, I believe we are now well positioned to generate competitive differentiation through mobile, BI-integrated, right-time analytical applications. The growing maturity of mobile BI platforms, and their support for the little known-capability known as write back, has the potential to be a turning point for the field. Write back in the context of BI gives the user both the ability to consume data through their visualization or BI application and to generate data that is put back into the database. This is the next secret sauce.
The actual capability to do write backs has been around for a long time. It’s even built into Excel and can be used with Microsoft Analysis Services OLAP cubes, if they are configured for this purpose. It’s also a part of enterprise BI tools like MicroStrategy as well
This is a big deal, because now we can combine analytical data (and the processing power of real-time analytics engines) with information that is entered by a user, in context, on site, in the moment while they are working on a particular problem.
Let’s say you’re a medical supply sales representative, and you go on site to visit a hospital client. Your mobile BI solution provides you with historical purchase patterns. Then, you conduct an inventory check, inputting the data while you are standing in the supply room. That information goes back to the database, and the purchasing models apply past history, seasonality, and metadata about trends at your other healthcare clients in that area (think: regional flu outbreak), generating a purchase forecast and preliminary order. The solution also recommends a product change, from buying individual packages to large count bulk packs, which would save the customer $10,000 this year. You review with the administrator, make a few adjustments, and you’re done.
This simple vignette may seem far off, but it isn’t a dream. Right-time analytics applications can become critical competitive differentiators for current and future market leaders. The complexity here is in gathering the data, understanding behavior, and building the analytical models that will help us optimize processes in our daily work. It’s complex, but definitely within reach for those that are willing to invest the time and effort to see it through.