The Opportunity in Big Data Analytics and Social Business

BLOGS.FORBES.COM/RAWNSHAH - CONNECTED BUSINESSThe Opportunity in Big Data Analytics and Social BusinessRAWN SHAH

About this E-book The format and most importantly the content for the e-book will change over time, as I modify, expand or add chapters, findings, interesting resources and interviews over time. If you have not seen my work before, perhaps a good start would be to look at my Forbes blog. As always, I am very open to discussions, conversations, commentary and criticism of my work, however it is delivered. You can reach me, regardless of if we are connected, through Twitter (@rawn), Face-We need to expand our minds of book (Rawn Shah), or Google+ (+Rawn Shah).how we convey thought leadership This e-book is not yet the level of production that Don Tap-knowledge. That has already begun scott achieved with his recent app with the Thinkers50, but itin the rise of social media interac- is a step closer. In my review of his app, The New Stage fortions and relationships creating Business Leadership Writing, published in Oct 2012 on mysmall format ways of quickly deliv- Connected Business Forbes blog, I referred to Victor Hugo’sering nuggets of information. I am words, “There is nothing more powerful than an idea whosean active participant in that do- time has come.”main. Yet, I believe we still needthe long-format of articles, blog posts, and even e-books. I amtaking this opportunity to expand a short series into an e-bookof its own focused around Analytics and Social Business Data. -rawnThe goal of this e-book is to explore and collaborate on thevarious details that emerge when you think of data analyticsand social business. In particular, social data is Big Data. Thechallenges are similar but also more specific because of the na-ture of social identities. © Copyright 2012 Rawn Shah. All Rights Reserved. i

C HAPTER 1What are BusinessesLooking to Gainfrom Big Dataanalytics?The SaÏd Business School at the Univer-sity of Oxford and the IBM Institute ofBusiness Value together have uncoveredthe state of Big Data use in their jointstudy, Analytics: The real-world use ofbig data, released in October 2012. Let ustake a look at the state of the industryfrom the viewpoints of businesses whoare actively involved in this space. Image: M. Schroeck, R. Shockley, J. Smart, D.R. Morales, P. Tufano Analytics: The Real-World Use of Big Data, Said School of Business, IBM IBV 2012

S ECTION 1 If there is something we can easily say today is that we have more information available openly to people than in all his-Does Big Data Matter? tory combined prior to two decades ago. Incidentally, that was just shortly after the protocols for the Web were first created, and later made famous by Mosaic, Netscape, and other popu- lar tools of the time. It has since become the Web and the Internet as we now know it. W HAT IS B IG D ATA A NALYTICS ? Yet, this is data in the public sphere. Enterprise data is an en- 1. Top 5 characteristics of Big Data: tirely different manner. We know we have lots of data across the enterprise and the challenge to manage and integrate that (a) A greater scope of information data into a cohesive whole continues. The goal: to understand (b) New kinds of data analysis what we know about our business. (c) Real-time information These two worlds are clashing as we realize the multiplied value of bringing together what we know from our inside-out (d) Data influx from new technologies view from the enterprise, our outside-in view from our custom- (e) Non-traditional forms of media ers, and from the plain outside-only view of the public. What we end up is a whole lot of information, creating the scenario 2. 63 percent of respondents report that the use that we now refer to as the Big Data world. of information (including big data) and analytics is creating a competitive advantage The challenge: how do we in a practical manner analyze all for their organizations – a 70 percent increase this information to get useful results to enhance and acceler- in just two years. ate our business. We grasp that Big Data not only means new technology components to simply have the capacity to ana- 3. The convergence of Volume, Variety, Velocity lyze, but also human components and skills as part of that ca- and Veracity of data pacity. As according to this joint IBM-Oxford study which This study from the joint efforts of IBM and University of Ox- is available, free with registration, from this site. ford is quite timely in answering the up front questions of what we should expect from Big Data analytics from the execu- 3

tive point of view. Starting with the very obvious question of Research Group points out, there is “a morass of confused defi-how do we define Big Data itself. This is one of the first ques- nitions” around Big Data. His key point in this blog post istions the study has undertaken. But, first let us look at the that Big Data is about making decisions about the future notdata behind the study itself. just rehashing the past.According to the report, this is F IGURE 1 Different Views on the Meaning of Big Data Figure 1 from the study showsbased on the Big Data @ Work some top characteristics showSurvey conducted by IBM in some commonality in what theymid-2012 with 1144 profession- think is Big Data, but note thatals from 95 countries across they could pick up to two descrip-26 industries. Respondents in tions. Considering that even thea self-selected manner repre- top item is 18% while the lowestsent a mix of disciplines, in- is 7%, there is still some debatecluding both business profes- on the scope. The distribution ofsionals (54 percent of the total these characteristics describe pri-sample) and IT professionals orities, although none of these(46 percent). Study findings are divergent or exclusive of eachare based on analysis of survey other.Yet, we can observe that dif-data, and discussions with Uni- ferent groups have different pri-versity of Oxford academics, orities.subject matter experts and Based on this the study describesbusiness executives (notably four key dimensions that both de-from IBM). Of the respon- scribe the complexities of work-dents, 28% say they have initi- ing with Big Data and distinguishated or deployed Big Data pro- it from what we have been doingjects, while 47% are still at the with structured databases for dec-planning stage. Source: M. Schroeck, R. Shockley, J. Smart, D.R. Morales, P. Tufano Analytics: The Real- ades now. The four characteris- World Use of Big Data, Said School of Business, IBM IBV 2012As Ray Wang, Principal Ana- tics are described as follows (seelyst and CEO of Constellation Figure 2): 4

• Volume - this is obvious from the name itself. There is a ety of sources, many not even your own. much larger scale of data than what we have processed before. By itself it seems like a scaling issue alone. • Veracity - This is possibly the most interesting aspect of this study: the attention to detail and how much that data con-• Velocity - this is data in motion, changing over time, some- forms to facts or actual ‘truth’. I will dedicate a full chapter times on an hourly or daily basis, and at other times when to this topic. you have data from sensors, at a blinding fast pace of milli- Ray Wang describes two other dimensions that arise with so- seconds and microseconds. The real issue is how to process cial engagement in particular, as Viscosity and Virality. The and respond to it in good time. former refers to the resistance to flow of data such as friction• Variety - Speed and volume alone still call for technological due to integration flow rates from data sources. The latter de- scaling, but scribes how variety is F IGURE 2 Four Dimensions of Big Data quickly informa- where you tion gets distrib- need intelli- uted across peo- gence. The ple -to-people data comes networks. These in many for- factors essen- mats, tially describe a known and state of inertia unknown; it and its opposite, may be free-flow. structured To me Viscosity or unstruc- is an aspect of tured; it access and Veloc- may contain ity, not one of its multiple me- own. The chal- dia; and Source: M. Schroeck, R. Shockley, J. Smart, D.R. Morales, P. Tufano Analytics: The Real-World Use of Big Data, Said School of Business, IBM IBV 2012 lenge is in get- emerge ting access to from a vari- 5

the data at the speed that it is traveling, and not working without of date information.Virality occurs in two forms. The first is a metadata element of‘how fast is this information spreading’ which is accelerationand the various vectors it is going that is useful in determiningpriorities and significance of the incoming data. Once youhave analyzed the data, what is your ability to distribute theinformation effectively. Rohit Bhargava, author of PersonalityNot Included (McGraw-Hill, 2008) and Likenomics (Wiley,2012), shared other factors that describe how viral is the con-tent that you have: Is it Unique? Is it Authentic? Is it Talk-able? Both these views of the current state of data accelera-tion, and the projected state of the result spread are studied atlength by the Word of Mouth Marketing Association.I would therefore say they are either outcomes of the other di-mensions or factors that play after the actual analyses, ratherthan core dimensions.The four factors of Volume, Variety, Velocity and Veracity im-pact your ability to act on the data you have at hand, to hope-fully make decisions about the future. 6

S ECTION 2 claims, assessing each claim against identi-What is Driving the Need? fied risk factors and categorizing the risks. Vestas Wind Systems A/S, a Danish wind turbine producer, used a supercomputer to analyze a large num- ber of location- dependent factorsWith all the possibilities, we need to set our priorities on what such as temperature,to analyze and more so, the bigger goal of what business initia- precipitation, wind ve-tives are we trying to drive through this analysis. According to locity, humidity andthe study (see Figure 3), most respondents are looking to ex- atmospheric pressure.pand their capabilities for Customer-centric outcomes (49%). This led to increased Source: M. Schroeck, R. Shockley, J. Smart, D.R. Morales, P. Tufano Analytics: The Real-World Use of Big Data, SaidA far second is Operational optimization (18%), followed by predictability and reli- School of Business, IBM IBV 2012risk management and new business models, and finally em- ability, which in theployee collaboration. end decreases cost to customers per kilowatt hour produced.The focus on customer-centric issues is reinforced with their All these examples examine enterprise internal data, underreferences in different industries. For example, Premier their dominion in known data sources and formats. As theHealthcare Alliance used enhanced data sharing and analytics study says, “internal data is the most mature, well-understoodto improve patient outcomes while reducing spending by data available to organizations. It has been collected, inte-US$2.85 billion. Santam Insurance, in South Africa improved grated, structured and standardized through years of enter-the customer experience by implementing predictive analytics prise resource planning, master data management, businessto reduce fraud. Fraud losses accounted for 6 to 10 percent of intelligence and other related work.”annual premium costs for Santam customers. They gained theability to catch fraud early by capturing data from incoming 7

From my view in the social world, implementing on internaldata relatively speaking, is much easier to accomplish. Thisagain is the issues that rise with Veracity.Todd Watson of IBM points out in his blog, “Most Big Data ini-tiatives currently being deployed by organizations are aimedat improving the customer experience, yet less than half of theorganizations involved in active Big Data initiatives are cur-rently collecting and analyzing external sources of data, likesocial media.”Regardless of the internal-external data focus, you can andshould examine social data from a customer-centric purpose.What that suggests is that you need to understand not justhow customers interact with your company directly (which cre-ates data such as transactions, service calls, sales requests,event participation), but also how the customer is interactingin the social world around the topic of what they are inter-ested in.Are they looking for advice on the need or use of the product?Are they looking for recommendations? Are they comparisonshopping? Are they influencing others about that product orservice category?These translate to building a deeper understanding and betterpreparation to anticipate needs of the customer and the mar-ket. 8

C HAPTER 2The Veracity of DataThere are increasing levels of uncertaintysurrounding data as we move from thewell-defined world of transactions to thecontext-free world of language and inter-action. Yet, this is the precisely road totake to future opportunities for thecustomer-centric organization.

S ECTION 1Veracity goes beyond The IBM-Oxford study does well by introducing Veracity but even the term has some degree of uncertainty because of theUncertainty multiple implications that fall under this umbrella term. Id like to add some of the different ways that this comes to light and go into depth with one aspect that impacts the area of analytics in Social Business. • Accuracy - at the heart of veracity is how accurately does it portray the real state of information • Precision - how much precision is actually available. You can L ET ’ S TALK U NCERTAINTY have high precision but it may still not be accurate to reality Uncertainty comes in many shapes and forms, and • Reliability - will the data continue to be available at the we need to understand that they can change same quality in the future or does become corrupted some- substantially in meaning and in the amount of how (not including intentional changes to the data contents) work-to-be-done when we look at the internal • Provenance - can you determine the path where the origin of versus external data: the data came from, not simply where you picked it up 1. Accuracy • Fidelity - goes with provenance to how much did the data change meaning from the original to where you got it 2. Precision 3. Reliability • Permission - do you actually have the right to use the data the way you intend to 4. Provenance Most of these aspects have been understood for quite some 5. Fidelity time in the years spent looking at data internally. As indicated 6. Permission in earlier sections, internal data is much better understood 10

and trusted to a degree. However, once you begin to look atthe external world, the rules begin to change. I NTERACTIVE 1   Veracity in relation to Analytics CapabilitiesIt is this last element of Permission that is a big impacting fac-tor on using social data, very often because we simply pre-sume that we have that right if we can get the data. This verymuch applies to external public data from databases, websources, online applications, and so on that all contain somebit of the data you may want. Even data within the enterprise Structured Queriescan have that limit.Even when those who have worked out the rights to access theinformation, consider that this is not a fixed state, but has itsown Velocity and its own path of dependencies. This is data inmotion as the study describes, and more than just a changingflow of information, it is also changing metadata. For exam-ple, the data that describes your network of relationships Natural Language Processingchanges when you add a new person to your network, and thismetadata then changes the information that comes through 1 2your network overall. You dont even need to add anyone newdirectly. Your network itself is alive and evolving as people Image Source: M. Schroeck, R. Shockley, J. Smart, D.R. Morales, P. Tufano Analytics: The Real-World Use of Big Data, Said School of Business, IBM IBV 2012. Comments: Rawn Shahchange jobs, move organizations, add new relationships andinteract socially. This is arguably yet to be enforced thorough across bordersThe global economy complicates this further because of territo- but it is a risk that still exists.rial, national and other regional rules on rights to information What all this represents can be loosely termed friction thatin the social space. Access to your social information, and not limits what you can learn from the data itself. Big data has dif-even sensitive private information, falls into different jurisdic- ferent issues in the social space and regardless of all the mar-tional precepts, sometimes with their own compliance needs. ket interest in social analytics, we have yet to really play in the new reality of the Social Customer. 11

S ECTION 2Moving from Transaction- T-mind is where most business thinking has been for some decades since the mass adoption of relational databases. Wemindedness to all incorporate it into our operations, our marketing tactics, our customer service, our product development, our ERP, andRelationship-mindfulness other aspects of our businesses. In fact, at scale, we began to look at Aggregates and thinking of demographics, and other forms of groupings; at first, in the broad sense (e.g. male cus-Transaction data is nice and easy. It can be very voluminousbut the context behind that data is often well recorded with alot of meta data about who, what, when, how, etc. A conversa-tion by comparison is vague, multitudinal, multi-party, lan-guage dependent, and may contain references to outside infor-mation not included in the data. A relationship multiplies allthe conversations you may have had with someone over multi-ple occurrences over time, as well as different modes of howyou interact, and changing parties of who else you interactedwith.The contextual differences behind a transaction and a relation-ship is leagues apart, and with Social Business, what we reallywant to get to understanding is the state and the opportunitiesfor the relationship, not just the history. In a mentoring calllast week to some new consultants, I described the difference Image: Generated by LinkedIn Labs. Data: Rawn Shahin thinking Transactions (lets say T-mind) and Relationships(R-mind) poses several magnitudes of data needs. 12

tomers aged between 19 and 25), to a much narrower focus Phoenix area, but the actual communities they engage and(e.g., men 19-25 in my territory who commute to work and even the topics, car parts, people and other subjects they likelike sports). By aggregating volume transactions into sets, we to talk about.discovered new opportunities for business for smaller groups R-mind is when you start to really look at the networks of indi-of people, and therefore arrive at the A-mind thinking in ag- vidual people when making business decisions.gregates. What happens when you get to this state is the ultimate in per-Peter Kim of digital agency R/GA, and former Chief Strategy sonalization and customer-centricity. One aspect of the transi-Officer of Dachis Group, recently shared in his blog that “func- tions from T-mind to A-mind to S-mind and to R-mind is thattional integration of ecosystems is emerging as the path to- the scope of what you are looking at is getting smaller, but awards maximizing value.” His view is that focused aggregation second aspect is that the data and analytics per person is alsoat the interface to the customer will be a continuing principle increasing in volume.of reaching social masses, versus a strategy that distributeshow you interact with customers across different places. Yet, the challenge remains is what you also run into is the big data problem with Permission as I explained earlier.When social media arrived, we realized that you could sharethis with customers to help their decisions (e.g., "People whobought this, also bought ..." on Amazon). We also realized thatthat information is spread around the Web in data sources out-side our control. Thus, social media analytics was born to getthat sense of information.This is still not quite reaching R-mind, but for the sake of thispiece, lets call it S-mind to note that it includes the socialview and not just aggregates of data. The A-mind to S-mindchange happens when you consider not just demographicsdata you defined yourself, but what you discovered from thesocial web. In other words, you are creating new categories ofdata based on the social data. For example, you are not onlythinking of people who like to work on cars as a hobby in the 13

C HAPTER 3Aggregating SocialData Coming SoonIt is easy to misunderstand the title as atechnical issue of integrating datasources. It is something more complex.Social data is the basis of demographicsand psychographics, and essential to theway we understand the customer. Let’stake a step into the A-mind.

http://www.scoop.it	782
http://lonewolflibrarian.wordpress.com	81
http://staging.slideshare.com	11
http://yeseniaplascenciar.blogspot.mx	11
http://rawnshah.wordpress.com	9
https://suki-sohn.squarespace.com	7
https://twitter.com	6
http://www.library.ceu.hu	5
https://si0.twimg.com	3
http://www.twylah.com	3
http://bundlr.com	2
http://bottlenose.com	2
http://app22.sl.ss	1
http://tweetedtimes.com	1
http://www.onlydoo.com	1
http://pinterest.com	1

The Opportunity in Big Data Analytics and Social Business

by Rawn Shah on Oct 19, 2012

Accessibility

Categories

Upload Details

Usage Rights

16 Embeds 926

Statistics

The Opportunity in Big Data Analytics and Social Business Presentation Transcript