Presentation big data and social media final_video


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Presentation big data and social media final_video

  1. 1. Presented by: Raminder Kaur Wayne State University
  2. 2.  Introduction  Challenges in handling Big Data  Challenges with Social Media Big Data  How can Big Data help?  What can you do with Big Social Data?  Issues in Social Media Data Mining  Best Practices in Social Media Data Mining  Social Media Data Mining for Marketing  Future Work -What big data means for the future of social media users - What big data means for the future of social media marketers  Conclusion  References Wayne State University
  3. 3. “Big Data” is data whose scale, diversity, and complexity require new architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it… “Social media ” refers to the means of interactions among people in which they create, share, and exchange information and ideas in virtual communities and networks Wayne State University
  4. 4.  Social media: largest fine-grained record of human activity ever  Big data itself isn’t new – its been here for a while and growing exponentially. What is new is the technology to process and analyze it. The purpose of big data technology is to cost effectively manage and analyze all of the available data. Any data, as is.  If you want to analyze structured data, then structure it.  If you want to analyze an acoustic file, then analyze the acoustic file with appropriate analytics.
  5. 5.  The Bottleneck is in technology ◦ New architecture, algorithms, techniques are needed  Also in technical skills ◦ Experts in using the new technology and dealing with big data
  6. 6.  Analyze social media and consumer information data sources to gain customer insights and take informed sales, marketing or services actions  SOCIAL MEDIA BIG DATA 400 Million Tweets sent per day 2.5 billion content items shared on Facebook 2.7 billion Likes 300 million photos uploaded 500+ TB data ingested 100+ PB disk space in a single HDFS cluster 105 TB data scanned and much more… Wayne State University
  7. 7. Information Business Advantage Media and social media Finances Communications
  8. 8. One of the challenge with so much data on social media is: deriving a meaningful contextual information.  Social media data is unstructured. Unlike other customer data from retail, banking etc. which is structured.  Most organizations want to capture contextual conversations and other widely available sources of unstructured data from social media, blog commentaries and other sources in real time, and put them side by side with structured data in their information ecosystem for a much clearer picture of what is going on.  Making sense out of the unstructured data from social media biggest challenge Wayne State University
  9. 9. Any data regarding donuts can be collected from social networking sites from Facebook, twitter, linkedin , yahoo, etc. This data can then be analyzed for future use for various analytics and BI purposes. Wayne State University
  10. 10. Such unstructured data from social media has to be approached in a non traditional manner. Mastering the science of understanding the exploding big data from social media will definitely be a game changer
  11. 11.  HSBC mines social media profiles to manage credit risk.  NETFLIX personalize content for customers.  AMAZON dynamically price and recommend products.  LINKEDIN suggests you new available jobs whenever there is a job posted by an organization.  Much more…. Wayne State University
  12. 12. The following are the key areas where big data can help in marketing • Implement more targeted marketing campaigns for specific geographies or individual consumers • Track and respond to promotions in real time to ensure the most profitable outcomes • Identify which promotion strategy will yield the best results in a specific chain or cluster of stores • Determine which new product options are the most profitable and least risky to pursue better assess product price elasticity before implementing price changes • Perform predictive analytics across all areas of the business to improve performance • Process larger volumes of data faster, including batch data provided by external sources Source Wayne State University
  13. 13.  Remarket your Social activity during campaigns  Ensure Social is fully tracked and sharable  Investigate DMPs to connect Social Wayne State University
  14. 14.  Billions of social media messages  Extract insights to support CRM and marketing  Monitor reputation and perception  Combine social data with other data sources, relational as well as unstructured, both on premise and in the cloud  Bridge Hadoop processing environments with traditional relational database environments to deliver the best of both worlds  Ensure cost-effective scalability, regardless of the data type or volume  Enrich customer master data with social media data for a true 360- degree view Wayne State University
  15. 15.  While there have been few cases of the use of data from social media sites for illegal or unethical purposes, many in the industry believe it is more of a matter of when, not if.  Companies mining data from social media sites are also very secretive about what they do, and how they do it. This is partially due to the fact that it is a new frontier and they do not want to give away trade secrets, however the opacity makes some experts nervous. Wayne State University
  16. 16.  Privacy issues ◦ Thoughts and feelings shared become part of a “vast market research project” ◦ Data is readily available through social networks. More the data, the more of a chance for problems  Employees leaking customer information  Hackers  Mobile ◦ Over 75% people own a smartphone  Apps(applications) have location data and access to address books in phone  Companies can predict where users will be throughout the day  Companies know who your friends, family, and coworkers are  Ethics ◦ How are companies obtaining their data? ◦ Do consumers know they are being tracked? Wayne State University
  17. 17.  Employ a combination of computer technology and human analysis ◦ Even sophisticated programs have difficulty extracting meaningful insight because of the prevalence of slang, abbreviations, humor, and sarcasm on social media sites  Ethical collection of data ◦ Collect only data that is considered public ◦ Use a reputable third party company for social media data mining to avoid ethical issues  Do not collect personally identifiable information  Focus on using data from big groups to create psychographic profiles and uncover the general sentiment  “Listen” to what customers are saying to provide better products and services ◦ As opposed to monitoring to keep control of the online conversation Wayne State University
  18. 18.  Mining and analyzing the vast amount of information available on social networks will be a win-win situation for marketers and consumers if ethical issues can be avoided. Currently, companies who mine big data average 6% higher productivity than those that do not.  Marketers can: ◦ “Listen in” on online conversations to get a better understanding of:  Who their customers are  What products they want and need  What advertising messages are most effective  What channels are most effective ◦ Change their messages or target groups in real-time ◦ Help manage PR issues through quick customer service ◦ Aid in the development of new products and services Wayne State University
  19. 19.  The future of social media for users and marketers alike is big data-driven. It will no longer be based on the fact that you like golf or cricket. In the era of big data, marketers are already able to track your purchasing and viewing trends to extrapolate data  Social media users are trending towards social aggregators. The chat app that combined AOL Messenger, Yahoo Messenger, could organize contact lists across platforms and chat with all your friends through a single platform. Users will be looking for that tool in social media and come to some amazing conclusions.  Communicate across networks. In a world with such a variety of networks, users will look for a tool that can help make multi-network communication easier. Wayne State University
  20. 20.  The same is true for marketers as it is for social media users. Social media marketers are looking for the tool that makes cross-platform monitoring, ad placement, and measurement easier. Example:  Social media networks store huge volumes of data on users. Facebook, for example, tracks every computer IP you’ve ever logged in with, and which users logged in from that computer immediately before and after you .  GPS coordinates when logged into their mobile apps or pages you were visiting before clicking back to their networks. Others are allowing users to integrate other apps into their platforms, Facebook integration among thousands of already existing partnerships and integrations. These integrations with other devices, networks and services share thousands of points of data.  Facebook and Twitter both have algorithms built around finding potential marketing targets based on likeness to current customers or target market.  What are the chances that social networks are judging your preferences based on what website you were on immediately before logging into Facebook, or what types of comments you’re posting while logged into a blog and much more….. Wayne State University
  21. 21.  The issue is not that you are acquiring large amounts of data. You definitely have big data.  It’s what you do with your Big Data that matters.  Harnessing and using relevant data is key. This is vision for Big Data. Wayne State University
  22. 22.  Debates in the Digital Humanities by Matthew K. Gold  Socialnomics: How Social Media Transforms the Way We Live and Do Business by Erik Qualman    media-users-avoid-getting-turned-into-big-data/360416/     analytics  take-trend-watching-to-new-level/2012/06/06/gJQArWWpJV_story.html  Wayne State University
  23. 23. Thanks !!!