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Big Data Analytics: An Overview in 20 Tweets

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
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.

Organizational Issues and Big Data Analytics
9. #TDWI SURVEY SEZ: 30% consider #BigData a data mgt problem. 70% think it a biz opp when analyzed. Attend #TDWI Webinar
10. #TDWI SURVEY SEZ: #BigData #Analytics is owned by BI/DW team (41%), dep’ts (21%), IT/CIO (12%).
11. #TDWI SURVEY SEZ: Business analyst is most common job title for designer of #BigData #Analytics.

The State of Big Data Analytics
12. #TDWI SURVEY SEZ: 74% of orgs have some form of analytics today. But only 34% do #BigData #Analytics.
13. #TDWI SURVEY SEZ: 37% of orgs have 10Tb+ of #BigData just for #Analytics. More on #TDWI Webinar
14. #TDWI SURVEY SEZ: 20% of orgs expect to have 500Tb+ of #BigData just for #Analytics by 2013.
15. #TDWI SURVEY SEZ: 64% of orgs today manage #BigData for #Analytics in EDW, 38% outside EDW.
16. #TDWI SURVEY SEZ: 24% claim to have Hadoop today. #TDWI suspects most are experimental downloads. But still impressive
17. #TDWI SURVEY SEZ: #BigData is struc 92%, semi-struc 54%, hier 54%, events 45%, unstruc 35%, social 34%, Web 31%...

Future Trends in Big Data Analytics
18. #TDWI SURVEY SEZ: 33% will replace #Analytics platform within 3 yrs. Another 11% after that. 9% already replaced.
19. #TDWI SURVEY SEZ: Why replace #Analytics platform? Poor scale, loading, query speed, real time, SOA, self service, viz.
20. #TDWI SURVEY SEZ: #BigData #Analytics techs set to grow most: advanced analytics, data viz, in-memory DBs, unstruc data

Don’t miss my next TDWI Webinar on Hadoop. I’ll lead a panel of vendor representatives in a discussion of Hadoop and its value for BI, DW, and analytics. Register online, so you can join us December 14, 2011 at noon ET.

For a more detailed discussion of Big Data Analytics – in a traditional publication! – see the TDWI Best Practices Report, titled Big Data Analytics, which is available in a PDF file via a free download.

You can also register for and replay my TDWI Webinar, where I present the findings of the Big Data Analytics report.

Philip Russom is the research director for data management at The Data Warehousing Institute (TDWI). You can reach him at [email protected] or follow him as @prussom on Twitter.

Posted by Philip Russom, Ph.D. on December 7, 2011


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