The Eyeo Festival took place in Minneapolis last week. I missed it. I missed it for a very good reason, which is that I just started a new job as a Principal Analyst at Forrester Research. But I still followed from afar, wishing I could hear firsthand about some of the fantastic projects and ideas that get presented there (and I’ll certainly check out the videos as they get posted).
What is the Eyeo Festival, you might be wondering? It’s a small annual conference that “brings together creative coders, data designers, and creators working at the intersection of data, art, and technology for inspiring talks, workshops, labs, and events.” I’ve been to two out of the four conferences and have come away both times incredibly inspired and impressed. This is not just big data. This is big, beautiful, informative data. The coders, designers, and creators both at Eyeo and elsewhere provide living proof that big (and small) data doesn’t have to be ugly, messy, or impossible to understand.
It can have an emotional impact and make a point like this project by Kim Rees and Periscopic, which uses mortality data from the World Health Organization to estimate the number of years lost to gun deaths in 2013 alone.
Recent news of a a computer program that passed the Turing Test is a great achievement for artificial intelligence (AI). Pulling down the barrier between human and machine has been a decades long holy grail pursuit. Right now, it is a novelty. In the near future, the implications are immense.
Which brings us to why should you care.
Earlier this week the House majority leader, Eric Cantor, suffered an enormous defeat in Virginia's Republican primary by Tea Party candidate David Brat. No one predicted this - the polls were wrong, by a long shot. Frank Luntz, a Republican pollster and communication advisor, offered up his opinion on what was missing in a New York Times Op-Ed piece - lack of face-to-face discussions and interviews with voters. He asserts that while data collection was limited to discrete survey questions, what it lacked was context. Information such as voter mood, perceptions, motives, and overall mind set were missing. Even if you collected quantitative data across a variety of sources, you don't get to these prescient indicators.
The new wave of AI (the next 2 - 5 years) makes capturing this insight possible and at scale. Marketing organizations are already using such capabilities to test advertising messages and positioning in focus group settings. But, if you took this a step further and allowed pollsters to ingest full discussions in person or through transcripts in research interviews, street polls, social media, news discussions and interviews, and other sources where citizen points of view manifest directly and indirectly to voting, that rich content translates into more accurate and insightful information.
Big data this, big data that. Hardly a day goes by when we're not bombarded with messages about the big data platforms and technologies that will solve all our marketing problems. Let's be honest though: these tools and technologies alone simply won’t solve the big data challenge. But the effect of all that media and market hype? A lot of confusion and mistrust on the part of marketing leaders about what big data really is, what it can do, and how it should be incorporated into business strategy. And that's holding a lot of firms back from maximizing the power of the data at their disposal.
By now you're asking yourself how anything I've said so far is different or unique. Here it is: "big data" isn't about exabytes or petabytes. It's not about velocity. It's not a project or Hadoop or any other single thing. Big data is a journey that every company must take to close the gap between the data that's available to them, and the business insights they're deriving from that data. This is a definition that business and technology leaders alike can understand and use to better win, serve, and retain customers.
My colleague, Brian Hopkins, and I have just published a pair of reports -- researched and written in parallel -- to help our marketing and technology management clients work together to tackle the opportunities and challenges of big data. Here are a few of the most interesting "a-ha" moments of the research:
Big data is undergoing big change, but most companies are missing it or just grasping at the edges. My colleague Fatemeh Khatibloo and I have just completed an exhaustive study of the big data phenomenon. We found a familiar pattern: business confusion in the face of stern warnings about the dangers of big data and vendor-sponsored papers extolling its benefits. Here’s what we found hidden beneath the buzz:
As data explodes, so do old ways of doing business.
Everywhere we look, we find businesses using more diverse, messier, and larger data sets to stay competitive in the age of the customer — like the consumer goods firm that allocated marketing dollars based on flu trend predictions and the oil and gas companies that used weather data to predict iceberg flows and extend their drilling season. Savvy businesses find ways to turn more data into a competitive advantage. If your firm doesn’t get this, it won’t be pretty — starting in the not too distant future.
Technology managers and architects can’t afford to sit back and think that their Hadoop project will deliver everything the business needs. Nor can you afford to think that big data isn’t for you because you don’t have that much data. Why? Because “big data” is really the practices and technologies that close the gap between the available data and the ability to turn that data into business insight — insight that your firm needs to survive and thrive in the age of the customer. Four things to understand:
BI is no longer a nice-to-have back-office application that counts widgets — it is now used as a key competitive differentiator by all leading organizations. For decades, most of the BI business cases were based on intangible benefits, but these days are over — today 41% of professionals, with knowledge of their firm's business case, base their business case on tangible benefits, like an increased margin or profitability. As a result, BI is front and center of most enterprise agendas, with North American data and analytics technology decision-makers who know their firm's technology budget telling Forrester in 2014 that 15% of their technology management budget will go toward BI-related purchases, initiatives, and projects.
But taking advantage of this trend by deploying a single centralized BI platform is easier said than done at most organizations. Legacy platforms, mergers and acquisitions (M&A), BI embedded into enterprise resource planning (ERP) applications, and organizational silos are just a few reasons why no large organization out there has a single enterprise BI platform. Anecdotal evidence shows that most enterprises have three or more enterprise BI platforms and many more shadow IT BI platforms.
But Avoid Ending Up With A Zoo Of Individual Big Data Solutions
We are beyond the point of struggling over the definition of big data. That doesn’t mean that we've resolved all of the confusion that surrounds the term, but companies today are instead struggling with the question of how to actually get started with big data.
28% of all companies are planning a big data project in 2014.
According to Forrester's Business Technographics™ Global Data And Analytics Survey, 2014, 28% of the more than 1600 responding companies globally are planning a Big Data project this year. More details and how this splits between IT and Business driven projects can be found in our new Forrester Report ‘Reset On Big Data’.
Or join our Forrester Forum For Technology Leaders in London, June 12&13, 2014 to hear and discuss with us directly what Big Data projects your peers are planning, what challenges they are facing and what goals they target to achieve.
IBM's acquisition of Cognea, a startup that creates virtual assistants of multiple personalities, further reinforces that voice is not enough for artificial intelligence. You need personality.
I for one cheer IBM's investment, because to be honest, IBM Watson's Jeopardy voice was a bit creepy. What has made Apple's Siri intriguing and personable, even if not always an effective capability, is the sultry sound of her voice and at times the hilarity of Siri's responses. However, if you were like me and changed from the female to male voice because you were curious, the personality of male Siri was disturbing (the first time I heard it I jumped). Personality is what you relate to.
The impression of intelligence is a factor of what is said and how it is delivered. Think about how accents influence our perception of people. It is why news media personalities work hard to refine and master a Mid-west accent. And, how one presents themselves in professional situations says a lot about whether you can trust their judgment. As much as I love my home town of Boston, our native accent and sometimes cold personalities have much to be desired by the rest of the country. And we have Harvard and MIT! Oh so smart maybe, but some feel we are not always easy to connect with.
On May 14, Acxiom announced its intention to acquire LiveRamp, a "data onboarding service," to the tune of $310 million in cash. Several Forrester analysts (Fatemeh Khatibloo, Susan Bidel, Sri Sridharan, and I) cover these two firms, and what follows is our collective thinking on the impending acquisition after having been briefed by Acxiom's leadership on the matter.
“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.
No self-respecting EA professional would enter into planning discussions with business or tech management execs without a solid grasp of the technologies available to the enterprise, right? But what about the data available to the enterprise? Given the shift towards data-driven decision-making and the clear advantages from advanced analytics capabilities, architecture professionals should be coming to the planning table with not only an understanding of enterprise data, but a working knowledge of the available third-party data that could have significant impact on your approach to customer engagement or your B2B partner strategy.
Data discussions can't be simply about internal information flow, master data, and business glossaries any more. Enterprise architects, business architects, and information architects working with business execs on tech-enabled strategies need to bring third-party data know-how to their brainstorming and planning discussions. As the data economy is still in its relatively early stages and, more to the point, as organizational responsibilities for sourcing, managing, and governing third-party data are still in their formative states, it behooves architects to take the lead in understanding the data economy in some detail. By doing so, architects can help their organizations find innovative approaches to data and analytics that have direct business impact by improving the customer experience, making your partner ecosystem more effective, or finding new revenue from data-driven products.