Between 2012 and 2014, mobile BI adoption shot up: Forrester survey data shows that the percentage of technology decision-makers who make some BI applications available on mobile devices has nearly quadrupled, and the percentage who state that BI is delivered exclusively via mobile devices has risen from 1% in 2012 to 7% in 2014. While this clearly demonstrates that mobile BI is gaining traction, the actual mobile BI adoption picture is rather more nuanced. Our ongoing research and client interactions show that mobile BI adopters fall into three overall groups; some organizations
Really ‘get’ the transformational potential of mobile BI. They are the ones who understand that mobile BI is about much more than liberating reports and dashboards from the desktop. They focus on how data can be leveraged to best effect when in the hands of the right person at the right time. If necessary, they’re prepared to change their business processes accordingly. For those companies, mobile BI is an enabler of strategic goals, and deployment is a journey, not an end in itself.
Make mobile BI available because it’s the right thing to do, or they’ve been asked to. Many of these organizations are reaping considerable benefits from their mobile BI implementations, and the more far-sighted of them are working on how to move from the tactical to the strategic. Equally, many are trying to figure out where to go from here, in particular if the initial deployment doesn't show a clear benefit, let alone return on investment.
To compete in the age of the customer, it’s essential to make the most of the data you have access to, whether it’s from internal or external sources. For most organizations, this implies a need to review and challenge existing approaches to how they capture, process, and use data to support decision-making. But it’s important first of all to move beyond a technology-centric view of big data. This is why at Forrester, we define big data as:
The practices and technologies that close the gap between the data available and the ability to turn that data into business insight.
Moving beyond a technology-centric view doesn’t mean, however, that a bottom-up, technology-led approach to big data strategy won’t work. After all, it’s often the case that business executives can’t see the potential of a technology until they’ve seen it in action. A bottom-up approach also provides the opportunity to acquire technical skills, and gain an understanding of what needs to be done to integrate new technologies with existing systems (even if it’s just at the level of getting the data out – often easier said than done). But a pilot project or proof-of-concept demonstrating the “art of the possible” in a business context is different from implementing a Hadoop cluster and expecting the business side to start asking for projects.
“Business Intelligence in the cloud? You’ve got to be joking!” That’s the response I got when I recently asked a client whether they’d considered availing themselves of a software-as-a-service (SaaS) solution to meet a particular BI need. Well, I wasn’t joking. There are many scenarios when it makes sense to turn to the cloud for a BI solution, and increasing numbers of organizations are indeed doing so. Indications are also that companies are taking a pragmatic approach to cloud BI, headlines to the contrary notwithstanding. Forrester has found that:
· Less than one third of organizations have no plans for cloud BI. When we asked respondents in our Forrsights Software Survey Q4 2013 whether they were using SaaS BI in the cloud, or were intending to do so, not even one third declared that they had no plans. Of the rest, 34% were already using cloud BI, and 31% had cloud in their BI plans for the next two years. But it’s not a case of either/or: the majority of those who’ve either already adopted cloud BI or are intending to do so are using the SaaS system to complement their existing BI and analytics capabilities. Still, it’s worth noting that 12% of survey respondents had already replaced most or all or their existing BI systems with SaaS, and a further 16% were intending to do so.
Since Tibco acquired Jaspersoft on April 28th, 2014, I keep being asked the question: “Will this deal change the BI and analytics landscape?” (If you missed the announcement, here’s the press release.)
The short answer is: it could. The longer answer goes something like this: Jaspersoft and Tibco Spotfire complement each other nicely; Jaspersoft brings ETL and embedded BI to the table, whereas Spotfire has superior data analysis, discovery, and visualization capabilities. Jaspersoft’s open source business model provides Tibco with a different path to market, and Jaspersoft can benefit from Tibco’s corporate relationships and sales infrastructure. And with its utility-based cloud service, Jaspersoft also adds another option to Spotfire’s SaaS BI offering.
But that’s only the narrow view: once you take into consideration Tibco’s history (the hint’s in the name - “The Information Bus Company”) and the more recent string of acquisitions, a much larger potential story emerges. Starting with Spotfire in 2007, Tibco has assembled a powerful set of capabilities, including (but not limited to) analytics, data management, event processing, and related technologies such as customer loyalty management and mapping. If Tibco manages to leverage all of its assets in a way that provides enterprises with a flexible and agile integrated platform that helps them turn their data into actionable information, it will be a powerful new force that has the potential of changing enterprise BI platforms market.
To get there, Tibco has a number of challenges to address. On a tactical basis, it’s all about making the Jaspersoft acquisition work:
Retaining the talent
Making it easy for clients and prospects to engage with both companies
The findings presented in an article by German magazine Computerwoche published on Feb 11, 2014, are a forceful reminder that messages about excessive data capture via mobile apps seem to have gone unheeded so far. As reported, tests by TÜV Trust IT established that “almost one in two mobile apps suck up data unnecessarily”.
What’s “unnecessary” of course depends on your viewpoint: it may seem unnecessary to me if my mobile email app captures my location; the provider of the app, on the other hand, could be capturing the information to provide me with a better service and/or to make money from selling such data to a third party. The trouble is that I don’t know, and I don’t have a choice if I want to use the app. From a consumer perspective, this is not a satisfactory situation; I’d even go as far as calling it unacceptable. Not that it matters what I feel; but privacy advocates and regulators are increasingly taking notice. Unless app providers take voluntary measures, they may see their data capture habits curtailed by regulation to a greater degree than would otherwise be the case.
Let’s step back a moment and consider why so many mobile apps capture more data than is strictly speaking necessary for the functioning of the app:
Reflecting on 2013 (as one does on the last the day of the year …), I’m struck by how much I seem to be living in two parallel universes: a promised land of appropriately targeted marketing, personalized offerings, courteous and efficient customer service, timely and accurate information – you get the picture; and the real world, in which the gap between the promise and what’s being delivered seems, if anything, to be widening.
Admittedly, my research focus on business intelligence, analytics and big data no doubt heightens my awareness, as I’m forever looking for signs that the technologies that are available have actually been deployed. Sadly, a lot of the time I find that even companies with flagship projects involving advanced analytics manage to undo much of the good work by falling down on something very basic, such as getting my name right, or knowing which products I’ve actually purchased.
In case my point needs proving, I’ll start by taking a light-hearted look at a few examples of what I’m talking about, before suggesting a few New Year’s resolutions to all those companies whose claims about customer-centricity and superior service are being contradicted by reality:
The major UK retailer which keeps addressing me as “Mr”, has repeatedly assured me that the matter has been addressed, and which resorts to offering me flowers when I point out – again – that all my mailings are still addressed to “Mr Bennett”. Almost enough to give me an identity crisis.
The global bank whose customer I’ve been since 1997, but which I’ve been unable to convince for a number of years now that there is only one Martha Bennett. Definitely enough to give me an identity crisis!
Many organizations will have been relieved to find that the implementation of the update to existing European data privacy laws, the EU Data Protection Regulation, has been postponed. Adoption of the Regulation is now scheduled for 2015, which means it’ll be 2017 (possibly end of) before it’s actually applicable.
At least, that’s what it looks like. In typical fashion, the official document released after the European Council meeting in Brussels on Oct 25th is the result of much political horse-trading, and avoids specificity on any matters where agreement is lacking. As a result, one has to rely on a variety of third party sources in order to piece the story together. In a nutshell, a number of countries felt that the process for finalizing the EU Data Protection Regulation should be slowed down. The UK and Germany in particular argued that further consideration was required, albeit not for the same reasons: on the British side, concerns were more on the potential adverse impact on business of very stringent rules, whereas Germany wants to ensure that all required safeguards are in place.
Those who are rejoicing over the postponement shouldn’t pop the champagne corks yet, though. While the extra time is no doubt welcome, headlines such as “Victory for tech giants on EU data laws” are premature: nothing is finalized, and there is still the chance that the final version is rather more restrictive than many would hope.
Having business applications available while away from the office is nothing new; neither is using mobile devices as an integral part of a business process. Until recently, however, the former has mostly consisted of standard PC applications running on a laptop, and the latter has largely been the realm of specialist, often ruggedized mobile devices used for a single purpose, such as delivery tracking or stock-taking. The advent of smartphones and tablets has changed the dynamics of what mobility means in a business context.
One driver clearly has been the desire of business professionals to stay in touch and keep workflows moving even when not at their desk: 58% of information workers use a smartphone and 30% use a tablet for work (either employer-provided or personal). Even more importantly, the executives holding the purse strings have discovered the power of mobile. Not that tablet-toting business leaders are anything new; the “cool factor” of the iPad in particular meant that it quickly became a status symbol. But there’s been a more subtle revolution behind the scenes: once early adopters had started moving towards the electronic distribution of board papers, board members themselves started spreading the message, challenging organizations that were still paper-bound to go digital.
During a recent webinar on big data, several listeners wanted to know what the biggest stumbling blocks and reasons for failure were when it comes to big data projects, and what they could do to avoid them. Given the amount of resonance, in particular the top issue I cited, I thought I’d share it in this blog post. Please let me have your views and comments.
There are clearly many reasons why projects struggle or fail, and big data projects are no exception. What can put big data initiatives in a league of their own, though, is the level of (typically unrealistic) expectations often associated with “big data” technologies. Based on many conversations with clients, consultants, and conference delegates over the past couple of years, I find three key issues are being mentioned time and again. These are:
Not starting the project with a question
Underestimating the technical skills and expertise required