Is big data just more marketecture? Or does the term refer to a set of approaches that are converging toward a common architecture that might evolve into a well-defined data analytics market segment?
That’s a huge question, and I won’t waste your time waving my hands with grandiose speculation. Let me get a bit more specific: When, if ever, will data scientists and others be able to lay their hands on truly integrated tools that speed development of the full range of big data applications on the full range of big data platforms?
Perhaps that question is also a bit overbroad. Here’s even greater specificity: When will one-stop-shop data analytic tool vendors emerge to field integrated development environments (IDEs) for all or most of the following advanced analytics capabilities at the heart of Big Data?
Of course, that’s not enough. No big data application would be complete without the panoply of data architecture, data integration, data governance, master data management, metadata management, business rules management, business process management, online analytical processing, dashboarding, advanced visualization, and other key infrastructure components. Development and deployment of all of these must also be supported within the nirvana-grade big data IDE I’m envisioning.
And I’d be remiss if I didn’t mention that the über-IDE should work with whatever big data platform — enterprise data warehouse, Hadoop, NoSQL, etc. — that you may have now or are likely to adopt. And it should support collaboration, model governance, and automation features that facilitate the work of teams of data scientists, not just individual big data developers.
It seems everyone’s obsessed with Facebook’s IPO right now. And while CMOs are beginning to understand the possibilities of Facebook, and other social technologies, to connect and engage with customers, many CIOs remain unclear on the value of Facebook.
A question many business executives ask is this: “What’s the value of having someone like your page?”
On its own, maybe not much. But the true potential lies in the ability to collect insights about the people who like brands, products or services – be it your own or someone else’s.
For example, the chart below shows the percentage of consumers by age group who have “liked” Pepsi or Coca-Cola. These data suggest Coca-Cola is significantly more popular with 17-28 year olds than Pepsi, while Pepsi appears more popular with the 36-70 crowd. I pulled these data points directly from the Facebook likes of each of the brand pages using a free consumer tool from MicroStrategy called Wisdom. Using this tool I can even tell that Coca-Cola fans are likely to also enjoy the odd Oreo cookie and bag of Pringles.