By Philip Russom, TDWI
To raise an awareness of the new tool features, user techniques, and team structures of Big Data Analytics, I recently issued a series of twenty tweets via Twitter, over a two-week period. The tweets also helped promote a TDWI Webinar on Big Data Analytics. Most of these tweets triggered responses to me or retweets. So I seem to have reached the business intelligence (BI) and data warehouse (DW) audience I was looking for – or at least touched a nerve!
To help you better understand Big Data Analytics and why you should care about it, I’d like to share some of the thoughts from these tweets with you. I think you’ll find them interesting because they provide an overview of Big Data Analytics in a form that’s compact, yet amazingly comprehensive.
Every tweet I wrote was a short sound bite or stat bite drawn from TDWI’s recent report on Big Data Analytics, which I researched and wrote. Many of the tweets focus on a statistic cited in the report, while other tweets are definitions stated in the report.
I left in the arcane acronyms, abbreviations, and incomplete sentences typical of tweets, because I think that all of you already know them or can figure them out. Even so, I deleted a few tiny URLs and repetitive phrases. I issued the tweets in groups, on related topics; so I’ve added some headings to this blog to show that organization. Otherwise, these are raw tweets.
Defining Big Data, Advanced Analytics, and Big Data Analytics
1. #BigData #Analytics = where advanced analytics operate on big data sets. So, it’s about 2 things. Learn more in Webinar http://bit.ly/qp4wp6
2. Advanced #Analytics = data mining, statistics, extreme SQL, data viz, artificial intell, language processing.
3. Advanced #Analytics = database techs like MapReduce, in-database & in-memory analytics, column stores.
4. Advanced #Analytics = discovering unknown biz facts. Instead of advanced, should call it discovery analytics
5. #BigData = not just multi-terabyte datasets. Also about diverse data types & real-time or streaming data.
6. Bleeding edge of #BigData = data streaming from sensors, robotics, monitor devices, Web logs.
Benefits and Barriers for Big Data Analytics
7. #TDWI SURVEY SEZ: #BigData #Analytics benefits customer relations, BI, most pre-existing analytic apps.
8. #TDWI SURVEY SEZ: Bad skills, sponsors, & database software are leading barriers to #BigData #Analytics.
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Posted by Philip Russom, Ph.D. on December 7, 20110 comments
Blog by Philip Russom
Research Director for Data Management, TDWI
I’m currently researching a TDWI Best Practices Report that will redefine master data management (MDM) by describing what its next generation should look like. As part of the research, I’ve been interviewing users on the phone about their MDM programs.
The news so far is a mix of good and bad. I hate saying it, but half of the organizations I’ve talked with are mired in early lifecycle stages of their MDM programs, unable to get over certain humps and mature into the next generation. On the flip side, the other half is well into the next generation; so I know it can be done.
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Posted by Philip Russom, Ph.D. on November 17, 20110 comments
Blog by Philip Russom
Research Director for Data Management, TDWI
Early this morning, Informatica Corporation announced Informatica HParser, a new product for parsing data in Apache Hadoop environments. Instead of repeating the details of the announcement – which you can read on www.informatica.com, etc. – I’d rather use the announcement as a springboard for my own thoughts about the bigger trends and issues in Big Data Analytics and Hadoop that the announcement fits into. The catch is that there are so many myths and misconceptions (i.e., “mythconceptions”) about Hadoop right now, that I can’t bust them all in a short piece like this blog. So I’ll just present the two leading mythconceptions as background, plus a brief rant for color.
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Posted by Philip Russom, Ph.D. on November 2, 20110 comments
Blog by Philip Russom
Research Director for Data Management, TDWI
Just moments ago, Teradata Corporation issued three announcements describing new capabilities, products, and releases. Instead of repeating the details of Teradata’s new stuff -- which you can read on www.teradata.com, etc. -- I’d rather be self-indulgent and use each announcement as a springboard for my own thoughts about the bigger trends in Big Data Analytics these relate to.
Announcement Number One: Teradata Columnar
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Posted by Philip Russom, Ph.D. on September 22, 20110 comments
Where is the biggest battleground today in the business intelligence and analytics software market? On the technology front, one of the main battles is in the addressable memory space of systems that feature 64-bit computing and operating system platforms. The “in-memory” revolution is upon us, and no BI or analytics vendor wants to be left out. Large memory platforms will be critical to users working with tools for big data analytics, data discovery, data visualization, and more.
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Posted by David Stodder on September 15, 20110 comments
On airplanes, at coffee bars, at ballgames, and even while waiting out an oil change, I am, like many of you, encountering people intensely focused on their mobile smartphones and tablets. I can’t say that I’ve been nosy enough to check out whether those I’ve seen are using the devices for business intelligence, but some – at least the fellow at the oil change shop – do seem to be working with spreadsheets and charts, not just enjoying social media or entertainment. As technology and software options evolve, there’s less and less standing in the way of people using the devices for BI. The revolution is coming.
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Posted by David Stodder on September 9, 20110 comments