Data Mining 
In the Social Media Age 
Sherman Mohr, CEO 
Shared Spirits, Inc
This is when I knew….
Where data mining fits into my work
Let’s do a focus group!
So for our purposes today..
Why are people giving up data? 
Community 
Conversation 
Education
Data from Community Builders
Data from Conversations
Data from Conversations
Data from Exploration and Education
Data from Exploration and Education
Your takeaways 
What Privacy? The Trade Off 
Trust Me
What Privacy?
What Privacy?
The Trade Off
Enhancement Data is Important 
From Umbel.com
What we’re willing to trade?
Willingness continued
One more tradeoff example
Trust Me…. ; )
What trust means
Friendship Trumps Research
What does this mean to you?
Who benefits? 
Those qualifying for 1.9 million IT jobs in the 
United States through 2015/2016
Summary 
1. Data aggregation is ubiquitous and pervasive. 
2. We utilize social media for community, 
conversation, and ed...
Thank You! 
Sherman Mohr, CEO 
sherman@sharedspirits.com
Understanding Data Mining in the Social Media Marketing Age
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Understanding Data Mining in the Social Media Marketing Age

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Our preferences, comments, sharing, and online community involvement is being analyzed. The analysis is so subtle that most participants don't even notice. We learn in this session how marketers are gathering, extracting, analyzing and then building advertising campaigns around social media participation.

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  • This is what got it all started for me. When I first saw a blue dot on a smart phone and when I looked down in real time and it followed me from what could easily be seen as from one side of the house to the other, I knew what my Econ Professor Walter Johnson said about the Russians and the cold war was true. The difference and it was profound was this…I was the target! The image never left me and still drives me.
  • Under the banner of being a spirits, wine, and craft beer firm, Shared Spirits at it’s heart is a data mining firm. Our model is a combination of technology, location based marketing, traditional spirits marketing, and social. All of the components are designed to transform the way spirits, wine, and beer companies market their products. At this point, the only measure of success or failure that a spirits, wine, or beer firm has is case counts. We live in what is called a three teir system in the adult beverage business. The manufacturer can seldom sell its product directly to the buyer. It has to by law, work through a system of distributors. The product that is delivered to the distributor is in cases. Cases of liquor, wine, or beer. It’s this case count that is measured. There is no individual relationship with you or I as the end consumer of a glass of wine. Sure, we may post a pic on Instagram or “Like” a page or Facebook or even share an experience or Tweet about a great cocktail but the brand has no ability to build relationship with individuals. This is where my company comes into the data mining world. Through providing a utility…a tool used to solve a problem… Shared Spirits’ App allows for the individual purchase of a cocktail, wine, or beer from venues all over the world, then provides for sharing it with any contact you happen to have a phone number, email or social media connection to and then provides for its subsequent redemption at that location with all of the activity taking place through your phone. We are taking adult beverages to what I call the “social rail”. The new success standards for brands on our app will be individual relationships with the consumers who love the brand. Data mining in the spirits, wine, and beer world as is the case with most industries leads to business intelligence that becomes very valuable.
  • What is data mining?
     
    Dr. Bruce Ratner reminds us that there is a problem with the definition of data mining. Data mining is not at all well - defined.
     
    “Today’s data mining is a high-concept: having elements of fast action in its development, glamour as it stirs the imagination for the unconventional and unexpected, and a mystic that appeals to a wide audience that knows curiosity feeds human thought. I googled “definition of data mining” and received a gross (vis-à-vis net) number of 40,300,000 definitions! (Curiously, the first entry was “Data mining is derogatory … ”) To have a sound working assumption for the task at hand, I netted the “gross” google-number to 4,030. (This netting in and of itself coincidentally reflects that the definition of google’s search engine optimization is also ill-defined.) Suffice it to say that data mining is an ill-defined concept, as 4,030 definitions are clearly not needed to unambiguously explain the concept. Unprecedentedly, the data mining concept early on (circa 1970s/early 1980s) did not have, and currrently does not have the scholarly cause to take form. I conclude that data mining is an ill-defined concept. And, I declare that the net number of definitions suggests there are discipline-specific data mining definitions; but how many are there: 18, 36, 54, … ? [2] Regardless of an agreed number of disciplines, 4,030 divided by the “agreed-number” presents data mining proper or data mining discipline-specific as an ill-defined concept.“
     
    Dr. Ratner goes on to explain, “Today, statisticians accept data mining only if it embodies Tukey’s EDA Paradigm. [3, 4] They define data mining as any process that finds unexpected structures in data and uses the EDA framework to insure that the process explores the data, not exploits it. See Figure 1.1. Note the word “unexpected,” which suggests that the process is exploratory, rather than a confirmation that an expected structure has been. By finding what one expects to find, there is no longer uncertainty as to the existence of the structure. Statisticians are mindful of the inherent nature of data mining and try to make adjustments to minimize the number of spurious structures identified. In data mining the statistician has no explicit analytical adjustments available, only the implicit adjustments affected by using the EDA paradigm itself.”
     
    Traditional marketing and sales views sales in macro-economic ways. In other words, sales and marketing folks from traditional schools of thought don’t always believe in the digital view of our world. They tend to remain focused on the print, TV, billboard, and radio versions of the narrative that companies want shared.
    I classify traditional marketing as pretty much anything that is “pushed” onto the listener or reader. If the audience wasn’t asked to participate in the results or the campaign in some proactive way, that’s a sure sign that the advertising was traditional.
     
  • Data gathering in traditional marketing:
     
    Traditional marketing research often involves assessing the overall market for a good or service, surveying consumers about their likes and dislikes, and conducting focus groups to gauge consumer responses to a new product. The growth of information technology has transformed market research, with a growing number of analysts learning about consumer preferences and buying habits by mining massive sets of quantitative data and employing complex algorithms to uncover patterns and correlations that enable more effective marketing.
     
    Traditional Market Research
     
    While data mining emphasizes extracting predictive information about customers and sales from large databases, traditional marketing research focuses on identifying factors that influence the buying decisions of households and organizations. Relevant data is then collected, often through sales data, surveys and focus groups, according to Professor Roger A. Kerin, author of the textbook "Marketing." Traditional market researchers identify an opportunity, collect the needed information, then formulate an appropriate sales strategy. Data mining relies on information that is already available.
    http://smallbusiness.chron.com/examples-data-mining-vs-traditional-marketing-research-24716.html
     
     
    Data Mining Features
     
    Data mining uses statistical techniques to discover correlations between different factors and variables in large data sets, according to Yale University Professor Ian Ayres, author of "Super Crunchers." These data sets are often measured in terabytes, a terabyte being equivalent to 1,000 gigabytes. Data mining often gives businesses enormous amounts of information about their customers' behaviors and buying habits, enabling them to more effectively market their goods.
    Data Mining Examples
     
    Ayres cited online retailer Amazon.com's feature that tells a potential customer that people who like one particular product also like certain other items as an example of marketing through data mining.
    In another example, credit card issuer Capital One generates for its customer service representatives a list of products and services that a consumer is likely to buy based on characteristics of the customers' credit card accounts. You may recall the bad press Target received when it went a little too far in its interpretation of data gleaned from purchases and how they started advertising to teen agers who they assumed were having babies.
    We see this now most routinely in marketing through online search. You enter a search term looking to buy something. You may or may not buy it but sometime soon, the next time you login to Google or Facebook, you’ll likely see an ad for that particular item.
  • Data Mining Features
     
    Data mining uses statistical techniques to discover correlations between different factors and variables in large data sets, according to Yale University Professor Ian Ayres, author of "Super Crunchers." These data sets are often measured in terabytes, a terabyte being equivalent to 1,000 gigabytes. Data mining often gives businesses enormous amounts of information about their customers' behaviors and buying habits, enabling them to more effectively market their goods.
    Data Mining Examples
     
    Ayres cited online retailer Amazon.com's feature that tells a potential customer that people who like one particular product also like certain other items as an example of marketing through data mining.
    In another example, credit card issuer Capital One generates for its customer service representatives a list of products and services that a consumer is likely to buy based on characteristics of the customers' credit card accounts. You may recall the bad press Target received when it went a little too far in its interpretation of data gleaned from purchases and how they started advertising to teen agers who they assumed were having babies.
    We see this now most routinely in marketing through online search. You enter a search term looking to buy something. You may or may not buy it but sometime soon, the next time you login to Google or Facebook, you’ll likely see an ad for that particular item.
     
  • Simply put, digital technologies have caught fire because they address three core human needs: the need for connection with other humans, the need for self-expression, and the need for exploration. Wrapped up in the seductive ribbon of convenience, there has never been a better formula for consumer engagement. Understanding the human side of the digital revolution will be a key success factor for businesses trying to compete in a digital world. The author of the article cited here, Hana Ben-Shabat, states that most users of social networks fall into these categories adding that “Going forward, leading companies will play multiple roles that transcend the transaction.” They will have to provide for the following three types of people.

  • The community builder. Whether online or offline, the notion of community has always done miracles for businesses. From the cozy environment of a Starbucks café and the communal table at your local restaurants to the online communities of Burberry and Nike+, rallying consumers around a common interest, idea, or value gives them reasons to come back, engage, and advocate for a brand. Good community builders are good entertainers; like every gracious host they know when to be at the center and when to step back and let their guests take center stage.
    In my case it was initiated by my involvement with Meetup.com. Meetup features 177,000 “meetups” averaging nearly 500K monthly meetings. I started running or organizing a meetup around entrepreneurship seven years ago. This online platform called Meetup.com allows one to organize groups around almost any topic and establish meetings, workshops, activities, and more around the topic. The company aggregates anyone with an expressed interest in the topic and shares the meetup and the opportunities with those that are interested. What this allowed me to do was build community around a topic. I provided speakers, programs, and more and the group has grown to over 1200 participants. It truly was the gateway tool for me and my entry into the serious work of social media and conversion. The early work around data mining I was exposed to came from Meetup.com. Their ability to target specific influencers and their groups was my first exposure to targeted social media marketing.
  • The conversationalist. To drive self-expression, one must first be willing to have a conversation, be open to feedback, and take risks. Having a Facebook page means allowing consumer sentiment, positive or negative, to be there in the open. The idea of “brand control” is slowly becoming obsolete, but as brands “lose control” they “gain engagement.” Good conversationalists are provocative, encourage openness, and ignite creativity that ultimately enriches the community and the brand.
     
    If the brand is willing to allow the conversationalist the floor, there are dozens of ways to measure the data and gauge what is being said. Companies and organizations of all types are beginning to utilize tools that allow for the real time listening of the audience.
     
    Check out Mention.net for ways to monitor conversations around your brand or organization across social networks in real time. Hootsuite is a powerful monitoring tool as well and the venerable Google Alerts still renders results from across the web.
     
    One of my favorite web applications was built by BNL consulting of DC. This link is here:
    http://apache.bnl-consulting.com/stage/TwitterStreamDashboard/
     
    The Twitter Dashboard used for real time marketing purposes by an insurance firm to gauge flu symptoms across the country. A tool similar to this is being used by Clorox as well to modify delivery schedules to retailers where Twitter and social media traffic call for the highest likelihood of sales increases.
     
    Step One: Enter a series of search terms
    Step Two: View the stream from Twitter that is in real time and reflective of all who posted Tweets on that topic or using that word.
    Step Three: Enjoy your visual map on the Twitter cloud that shows you where the action is. You can then begin to target offers, ads, and more all based on what your real time results suggests is happening.
  • The conversationalist. To drive self-expression, one must first be willing to have a conversation, be open to feedback, and take risks. Having a Facebook page means allowing consumer sentiment, positive or negative, to be there in the open. The idea of “brand control” is slowly becoming obsolete, but as brands “lose control” they “gain engagement.” Good conversationalists are provocative, encourage openness, and ignite creativity that ultimately enriches the community and the brand.
     
    If the brand is willing to allow the conversationalist the floor, there are dozens of ways to measure the data and gauge what is being said. Companies and organizations of all types are beginning to utilize tools that allow for the real time listening of the audience.
     
    Check out Mention.net for ways to monitor conversations around your brand or organization across social networks in real time. Hootsuite is a powerful monitoring tool as well and the venerable Google Alerts still renders results from across the web.
     
    One of my favorite web applications was built by BNL consulting of DC. This link is here:
    http://apache.bnl-consulting.com/stage/TwitterStreamDashboard/
     
    The Twitter Dashboard used for real time marketing purposes by an insurance firm to gauge flu symptoms across the country. A tool similar to this is being used by Clorox as well to modify delivery schedules to retailers where Twitter and social media traffic call for the highest likelihood of sales increases.
     
    Step One: Enter a series of search terms
    Step Two: View the stream from Twitter that is in real time and reflective of all who posted Tweets on that topic or using that word.
    Step Three: Enjoy your visual map on the Twitter cloud that shows you where the action is. You can then begin to target offers, ads, and more all based on what your real time results suggests is happening.
  •  The educator. To satisfy consumers’ curiosity and need for exploration, companies are increasingly assuming the roles of educator and storyteller. From teaching consumers about their brand and its history to offering “how-to” videos for cosmetics or recipes for food and drinks, companies are seeking to develop new and interesting content to bring consumers back to their stores or websites.
     
    Facebook’s Movie is one such example. The ability to see your FB movie and post and share it has been extremely popular.
    On the “How to” front, 25.32% of the 1.2 million apps in the Apple App store fit in this category. A full 29.62% serve to allow exploration in areas around medicine, food, drink, travel, health, and more.
     
    Tremendously popular platforms are being provided as vehicles for education. Udemy.com is one such example. You can choose classes from a list of thousands. Every time you search, select, and finish a course, you’ve offered up digital signatures allowing for marketing professionals to utilize the social nature of taking a digitally delivered course to more easily model a marketing experience to you.
  • Tremendously popular platforms are being provided as vehicles for education. Udemy.com is one such example. You can choose classes from a list of thousands. Every time you search, select, and finish a course, you’ve offered up digital information allowing for marketing professionals to utilize the social nature of taking a digitally delivered course to more easily model a marketing experience to you.
    Would you be willing to part with some personal information to take a course? If you received a better price for trading some personal details or shopping preferences would it make a difference?
  • We’ll continue down the path toward our takeaways with these three points.
    What Privacy?
    The Trade Off
    And Trust Me.
    All three play an important role in why people offer what they offer allowing those of us in the data mining world more access than ever before.
  • A new study conducted by Accenture found that the majority of consumers in both the U.S. and UK are willing to have trusted retailers use some of their personal data in order to present personalized and targeted products, services, recommendations and offers.
    The study, which surveyed 2,000 U.S. and UK consumers, found that while 86 percent of those surveyed said they were concerned that their data was being tracked, 85 percent said they realized that data tracking make it possible for retailers to present them with relevant and targeted content.
    Read more: http://www.digitaltrends.com/social-media/why-consumers-are-increasingly-willing-to-trade-data-for-personalization/#ixzz3I7ZQWhi6 
    When consumers were asked to choose between personalized shopping experiences based on their past consumer behavior, or non-personalized experiences in exchange for having retailers not track their data, 64 percent of respondents said they’d prefer the personalized experience.  Read more: http://www.digitaltrends.com/social-media/why-consumers-are-increasingly-willing-to-trade-data-for-personalization/#ixzz3I7Z45OOx  Follow us: @digitaltrends on Twitter | digitaltrendsftw on Facebook
     
    But while 73 percent of consumers surveyed said they prefer do business with retailers who use personal information to make their shopping experience more relevant, the vast majority of consumers (88 percent) think that companies should give them the flexibility to control how their personal information is being used to personalize their shopping experience.
    Read more: http://www.digitaltrends.com/social-media/why-consumers-are-increasingly-willing-to-trade-data-for-personalization/#ixzz3I7ZBmh7L 
  • "It is crazy what people were willing to give me," said artist Risa Puno, who conducted the experiment, which she called "Please Enable Cookies," at a Brooklyn arts festival. The cookies — actual cookies — came in flavors such as "Chocolate Chili Fleur de Sel" and "Pink Pistachio Peppercorn."
     
    To get a cookie, people had to turn over personal data that could include their address, driver's license number, phone number and mother's maiden name.
    More than half of the people allowed Puno to take their photographs. Just under half — or 162 people — gave what they said were the last four digits of their Social Security numbers. And about one-third — 117 people — allowed her to take their fingerprints. She examined people's driver's licenses to verify some of the information they provided.
     
    http://www.propublica.org/article/how-much-of-your-data-would-you-trade-for-a-free-cookie
    http://www.longislandpress.com/2014/10/05/how-much-of-your-data-would-you-trade-for-a-free-cookie/
     
  • Technology now permits an individual to trade something they possess, i.e. an online identity, for a better value proposition.
     
    Social technology and social marketing is working best when users are receiving value in trade for the message.
    http://www.fastcompany.com/3026985/leadership-now/essential-tips-for-creating-apps-people-will-actually-use
    Forty-two percent said they prefer apps that help them gain access to discounts and lower prices, and an equal percentage want apps that help them simplify or organize their lives. This tells us that data mining in social media is facilitated by smart marketers building utility into their apps. The utility of something makes data sharing by consumers seem okay. Examples would include Expedia, Fandango, Phonto, Pandora and more.
    http://www.corporate-eye.com/main/good-news-for-mobile-advertising-in-apps/
    A recent study found that more than 65% of mobile users would not mind seeing relevant mobile ads within the apps they use as long as advertisers provide some type of proof that users’ privacy is well protected - See more at: http://www.corporate-eye.com/main/good-news-for-mobile-advertising-in-apps/#sthash.0Cdleu2K.dpuf
    This is relevant because people are trading highly personal information with every app download and log in. Things like Open ID’s, Social Media Profile Info, Location Info, and Increased data on your preferences with every login and use.
  • Slides: 17, 18 and 19, 20
    In this slide I’m showing what Umbel.com titled enhancement data and the primary importance assigned to it. Following are some examples of what we see as marketers when we go to place adds on Facebook. The FB Advertising tool allows us to see data people have provided relative to details associated with relationship, education, work, finances, home, ethnic affiliation, generation, parents, politics, and life events. The headings allow us to click and get extremely granular.
    In addition to these examples, I have tools that allow me to download the user ID’s of all participants in Events, Groups, and Pages. Those folks that have their privacy settings set to prevent it can’t be retrieved. The premise is that if someone joins a group or RSVPs for an event, they are more likely to be positive to advertising.
  • Slides: 17, 18 and 19
    Here are some examples of what we see as marketers when we go to place adds on Facebook. The FB Advertising tool allows us to see data people have provided relative to details associated with relationship, education, work, finances, home, ethnic affiliation, generation, parents, politics, and life events. The headings allow us to click and get extremely granular.
    In addition to these examples, I have tools that allow me to download the user ID’s of all participants in Events, Groups, and Pages. Those folks that have their privacy settings set to prevent it can’t be retrieved. The premise is that if someone joins a group or RSVPs for an event, they are more likely to be positive to advertising.
  • Slides: 17, 18 and 19
    Here are some examples of what we see as marketers when we go to place adds on Facebook. The FB Advertising tool allows us to see data people have provided relative to details associated with relationship, education, work, finances, home, ethnic affiliation, generation, parents, politics, and life events. The headings allow us to click and get extremely granular.
    In addition to these examples, I have tools that allow me to download the user ID’s of all participants in Events, Groups, and Pages. Those folks that have their privacy settings set to prevent it can’t be retrieved. The premise is that if someone joins a group or RSVPs for an event, they are more likely to be positive to advertising.
  • Slides: 17, 18 and 19 and 20
    Here are some examples of what we see as marketers when we go to place adds on Facebook. The FB Advertising tool allows us to see data people have provided relative to details associated with relationship, education, work, finances, home, ethnic affiliation, generation, parents, politics, and life events. The headings allow us to click and get extremely granular.
    In addition to these examples, I have tools that allow me to download the user ID’s of all participants in Events, Groups, and Pages. Those folks that have their privacy settings set to prevent it can’t be retrieved. The premise is that if someone joins a group or RSVPs for an event, they are more likely to be positive to advertising.
  • Trust is the key component.
     
    Trust in complete strangers is now common place and a bit of a risk. Tools and sites like Uber, Lyft, AirBnB as well as Fivr and others have us more used to sharing personal info with strangers than ever before.
    http://www.sfgate.com/bayarea/article/Sharing-economy-means-putting-your-trust-in-5784060.php
     
    Community "pressure" plays into trust. Friendship Trumps Research!
     
     
    Google research says nearly half of us consult friends and family before purchasing. 92% of people use social media to connect to friends and family.
    http://blog.blucarat.com/gallup-social-media-poll-buyers-trust-people-not-brands/
    http://arxiv.org/pdf/1305.7440.pdf How friends influence us on Social Media https://blog.bufferapp.com/social-media-friendships
     
    Studies show that 70% of consumers say they look at product reviews before making a purchase, and product reviews are 12x more trusted than product descriptions from manufacturers.
    https://blog.bufferapp.com/the-ultimate-guide-to-social-proof
     
  • A social network perspective requires abandoning some of the core assumptions in traditional analytics:
    Data about relationships between individuals are often more important than data about the individuals themselves.
    Individual nodes can be directly connected, indirectly linked, or completely unconnected from other nodes.
    Some nodes have considerably more connections than other nodes.
    Some parts of the network clump together into tightly-knit clusters.
    Traditional analytical approaches take each individual user as an atom, separate and independent: my height doesn’t depend on the height of my close friends. In many contexts this individualistic assumption is an appropriate. But in the vast majority of applications, it’s incomplete or just wrong. A social network perspective says my connections to others influence my own behavior: research has shown that my weight is actually influenced by the weight of my close friends. Ignoring these relationships obscures important processes of how information and influence travels between individuals over these relationships. 
    The data to be collected for social network analysis must reflect observations about relationships. Who is friends with whom? Who teams up with whom? Who buys what? Who plays where? These types of relationships are pervasive in many types of data but the relational implications are often overlooked.

    Trust is the key component.
     
     
    Community "pressure" plays into trust. Friendship Trumps Research!
     
     
  • A social network perspective requires abandoning some of the core assumptions in traditional analytics:
    Data about relationships between individuals are often more important than data about the individuals themselves.
    Individual nodes can be directly connected, indirectly linked, or completely unconnected from other nodes.
    Some nodes have considerably more connections than other nodes.
    Some parts of the network clump together into tightly-knit clusters.
    Traditional analytical approaches take each individual user as an atom, separate and independent: my height doesn’t depend on the height of my close friends. In many contexts this individualistic assumption is an appropriate. But in the vast majority of applications, it’s incomplete or just wrong. A social network perspective says my connections to others influence my own behavior: research has shown that my weight is actually influenced by the weight of my close friends. Ignoring these relationships obscures important processes of how information and influence travels between individuals over these relationships. 
    The data to be collected for social network analysis must reflect observations about relationships. Who is friends with whom? Who teams up with whom? Who buys what? Who plays where? These types of relationships are pervasive in many types of data but the relational implications are often overlooked.

    Trust is the key component.
     
     
    Community "pressure" plays into trust. Friendship Trumps Research!
  • This means what to you as an educator?
     
    Everything that is required to build a new data scientist, thinker, marketing, or strategists begins with you. The technical skills, the critical thinking skills that lead young people to ask the right questions and likely the most important skill of all, the sensitivity toward an ethical view of how data will be gathered and leveraged.
     
    Data analysts positions are slated to rise 3 to 7% over next 8 years.
    http://degreedirectory.org/articles/Data_Analyst_Career_Definition_Job_Outlook_and_Training_Requirements.html
     
    The McKinsey Global Institute, the business and economics research arm of McKinsey & Co., has predicted that by 2018 the United States could face a shortage of between 140,000 to 190,000 people with deep analytical skills, as well as a shortage of 1.5 million managers and analysts who know how to use the analysis of big data to make effective decisions. http://www.forbes.com/sites/emc/2014/06/26/the-hottest-jobs-in-it-training-tomorrows-data-scientists/
  • Those interested in data have a world of opportunity ahead of them:
     
    Tam Harbert of ComputerWorld.com describes the future leaders in this field -
     
    "These are people who fit at the intersection of multiple domains," he says. "They have to take ideas from one field and apply them to another field, and they have to be comfortable with ambiguity. He goes on to say, That said, most of the jobs emerging in big data require knowledge of programming and the ability to develop applications, as well as an understanding of how to meet business needs.
     
    The most important qualifications for these positions aren't academic degrees, certifications, job experience or titles. Rather, they seem to be soft skills: a curious mind, the ability to communicate with nontechnical people, a persistent -- even stubborn character and a strong creative bent.”
    http://www.computerworld.com/article/2493991/it-management/big-data-means-big-it-job-opportunities----for-the-right-people.html?page=2
    “By 2015, 4.4 million IT jobs globally will be created to support Big Data, generating 1.9 million IT jobs in the United States,” said Peter Sondergaard, senior vice president at Gartner and global head of Research. “In addition, every big data-related role in the U.S. will create employment for three people outside of IT, so over the next four years a total of 6 million jobs in the U.S. will be generated by the information economy.“
    http://www.itbusinessedge.com/slideshows/big-data-is-creating-big-jobs-4.4-million-by-2015.html
  • In Summary
     
    Data aggregation is ubiquitous and pervasive.
    We utilize social media for community, conversation, and education.
    There is little privacy – we don’t care..much.
    We’ll trade a lot for a little.
    We trust friends more than anything or anybody.
    There is great opportunity for leadership.
    There are great opportunities for future positions
     
    Data mining occurs in ways you would never expect. Data is used in ways that one would have found inconceivable a short five years ago. The world of data mining, analytics, and data science does call for some programming skills, however, the ability to marry data to visual dashboards and make a business case for strategies or against strategies is where the greatest value will be delivered.
  • Understanding Data Mining in the Social Media Marketing Age

    1. 1. Data Mining In the Social Media Age Sherman Mohr, CEO Shared Spirits, Inc
    2. 2. This is when I knew….
    3. 3. Where data mining fits into my work
    4. 4. Let’s do a focus group!
    5. 5. So for our purposes today..
    6. 6. Why are people giving up data? Community Conversation Education
    7. 7. Data from Community Builders
    8. 8. Data from Conversations
    9. 9. Data from Conversations
    10. 10. Data from Exploration and Education
    11. 11. Data from Exploration and Education
    12. 12. Your takeaways What Privacy? The Trade Off Trust Me
    13. 13. What Privacy?
    14. 14. What Privacy?
    15. 15. The Trade Off
    16. 16. Enhancement Data is Important From Umbel.com
    17. 17. What we’re willing to trade?
    18. 18. Willingness continued
    19. 19. One more tradeoff example
    20. 20. Trust Me…. ; )
    21. 21. What trust means
    22. 22. Friendship Trumps Research
    23. 23. What does this mean to you?
    24. 24. Who benefits? Those qualifying for 1.9 million IT jobs in the United States through 2015/2016
    25. 25. Summary 1. Data aggregation is ubiquitous and pervasive. 2. We utilize social media for community, conversation, and education. 3. There is little privacy – we don’t care..much. 4. We’ll trade a lot for a little. 5. We trust friends more than anything or anybody. 6. There is great opportunity for leadership. 7. There are great opportunities for future positions
    26. 26. Thank You! Sherman Mohr, CEO sherman@sharedspirits.com

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