[Slides] What Do We Do with All This Big Data by Altimeter Group
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The document discusses the challenges and implications of big data, particularly focusing on unstructured data and its impact on analytics methodologies. It emphasizes the need for new analytical approaches to extract meaningful insights from unstructured data and presents a case study on social media's role in smoking cessation. Additionally, it highlights ethical considerations in data usage and outlines strategies for organizations to adapt to the evolving data landscape.
[Slides] What Do We Do with All This Big Data by Altimeter Group
- 1. 1! What do we do with all this big data?! Fostering insight and trust in the digital age! Susan Etlinger, Industry Analyst, Altimeter Group! February 11, 2015! ! Photo: qthomasbower, cc 2.0!
- 2. 2! “ Orwell feared those who would deprive us of information. Huxley feared those who would give us so much that we would be reduced to passivity and egotism. Orwell feared that the truth would be concealed from us. Huxley feared the truth would be drowned in a sea of irrelevance.”! Neil Postman! Amusing Ourselves to Death, 1985!
- 3. 3! What’s so hard about big data?!
- 4. 4! “ Ninety percent of all the data in the world was created in the past two years.! − IBM!
- 5. 5! Big Data as defined by Gartner*: ! • Volume! • Velocity! • Variety! Variety is the most challenging.! What is big data?! * NB: This is really just a starting point for understanding big data. See Gartner for research on origins and definitions.
- 6. 6! With Big Data, Size Isn’t Everything! Images Text Video Audio
- 7. 7! How unstructured data disrupts! 1. Does not conform to standard data models! 2. Demands new analytical approaches! · Human expression: images, text. Much is unstructured.! · Raw material—requires processing to translate it into something a machine can understand & act upon! 3. Strains traditional methodologies ! Source: http://www.foreignpolicy.com/articles/2014/09/26/why_big_data_missed_the_early_warning_signs_of_ebola? utm_content=buffer6a337&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer#trending
- 9. 9! “ The nature of human language demands rigorous and repeatable processes to extract meaning from it in a transparent and defensible way.!
- 10. 10! Unstructured Data Requires New Analytics !
- 11. 11! Unstructured Data Requires New Analytics !
- 12. 12! Case Study: Health Media Collaboratory! What can social data tell us about smoking cessation?! • How much electronic promotion exists on Twitter?! • How much organic conversation about e- cigarettes is on Twitter?! Understanding the impact of CDC anti-smoking commercials ! • Did the commercials work?! • How can we prove it?!
- 15. 15! Methodology! 1. Data collection. Determine appropriate source and sample size of the data.! 2. Keyword selection. Generate the most comprehensive possible list of keywords, encompassing nonstandard English usages, slang terms, and misspellings.! 3. Metadata. Collect metadata related to the tweets: ! · A tweet ID (a unique numerical identifier assigned to each tweet) ! · The username and biographical profile of the account used to post the tweet! · Geolocation (if enabled by the user) ! · Number of followers of the posting account ! · The number of accounts the posting account follows!
- 16. 16! Methodology! 3. Metadata (continued). Collect metadata related to the tweets:! · The posting account’s Klout score ! · Hashtags ! · URL links ! · Media content attached to the tweet.! · Filtering for engagement. ! 4. Human coding. To assess relevance and code message content.! 5. Precision and relevance. Combination of human and machine coding.! 6. Recall. To determine whether data was generalizable.! 7. Content coding. To determine message effectiveness.!
- 17. 17! “ Eighty-seven percent of the tweets about the TV commercials expressed fear, and the ads had the desired result of jolting the audience into a thought process that might have some impact on future behavior.! ! − Health Media Collaboratory!
- 18. 18! From data to insight!
- 19. 19! “The type of data (structured, text, etc.) isn’t the point at all. The way of thinking matters.”! ! − Philip B. Stark, professor and chair of statistics, University of California, Berkeley!
- 20. 20! Logic problems: causation vs. correlation!
- 21. 21! From Insight to Trust!
- 22. 22! “ In civilized life, law floats in a sea of ethics.”! − Earl Warren, former chief justice of the United States!
- 25. 25! Planning for data ubiquity!
- 26. 26! Planning for 2015! ① Define data strategy and operating model! ② Update analytics methodology to reflect new data realities! ③ Seek out critical thinking and diverse skill sets! ④ Insist on ethical data use and transparent disclosure! ⑤ Reward and reinforce humility and learning!
- 27. 27! Thank You! Disclaimer: Although the information and data used in this report have been produced and processed from sources believed to be reliable, no warranty expressed or implied is made regarding the completeness, accuracy, adequacy or use of the information. The authors and contributors of the information and data shall have no liability for errors or omissions contained herein or for interpretations thereof. Reference herein to any specific product or vendor by trade name, trademark or otherwise does not constitute or imply its endorsement, recommendation or favoring by the authors or contributors and shall not be used for advertising or product endorsement purposes. The opinions expressed herein are subject to change without notice.! ! Altimeter Group provides research and advisory for companies challenged by business disruptions, enabling them to pursue new opportunities and business models. ! Susan Etlinger! susan@altimetergroup.com! susanetlinger.com! @setlinger!