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The Cloud is shifting our Center of Gravity

September 19, 2013

Is DG a D-O-G?

September 19, 2013/ Jim Harris

Convincing your organization to invest in a sustained data quality program implemented within a data governance framework can be a very difficult task requiring an advocate with a championship pedigree. But sometimes it seems like no matter how persuasive your sales pitch is, even when your presentation is judged best in show, it appears to fall on deaf ears.

Perhaps, data governance (DG) is a D-O-G. In other words, maybe the DG message is similar to a sound only dogs can hear.

Galton’s Whistle

In the late 19th century, Francis Galton developed a whistle (now more commonly called a dog whistle), which he used to test the range of frequencies that could be heard by various animals. Galton was conducting experiments on human faculties, including the range of human hearing. Although not its intended purpose, today Galton’s whistle is used by dog trainers. By varying the frequency of the whistle, it emits a sound (inaudible to humans) used either to simply get a dog’s attention, or alternatively to inflict pain for the purpose of correcting undesirable behavior.

Bad Data, Bad, Bad Data!

Many organizations do not become aware of the importance of data governance until poor data quality repeatedly “bites” critical business decisions. Typically following a very nasty bite, executives scream “bad data, bad, bad data!” without stopping to realize the enterprise’s poor data management practices unleashed the perpetually bad data now running amuck within their systems.

For these organizations, advocacy of proactive defect prevention was an inaudible sound, and now the executives blow harshly into their data whistle and demand a one-time data cleansing project to correct the current data quality problems.

However, even after the project is over, it’s often still a doggone crazy data world.

The Data Whisperer

Executing disconnected one-off projects to deal with data issues when they become too big to ignore doesn’t work because it doesn’t identify and correct the root causes of data’s bad behavior. By advocating root cause analysis and business process improvement, data governance can essentially be understood as The Data Whisperer.

Data governance defines policies and procedures for aligning data usage with business metrics, establishes data stewardship, prioritizes data quality issues, and facilitates collaboration among all of the business and technical stakeholders.

Data governance enables enterprise-wide data quality by combining data cleansing (which will still occasionally be necessary) and defect prevention into a hybrid discipline, which will result in you hearing everyday tales about data so well behaved that even your executives’ tails will be wagging.

Data’s Best Friend

Without question, data governance is very disruptive to an organization’s status quo. It requires patience, understanding, and dedication because it will require a strategic enterprise-wide transformation that doesn’t happen overnight.

However, data governance is also data’s best friend.

And in order for your organization to be successful, you have to realize that data is also your best friend. Data governance will help you take good care of your data, which in turn will take good care of your business.

Basically, the success of your organization comes down to a very simple question — Are you a DG person?

September 10, 2013

Data Quality Project Management

September 10, 2013/ Jim Harris

OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, Adam Cox and I discuss data quality project management, avoiding data quality becoming an afterthought on data integration and data migration projects, the difference and relationship between data ownership and data stewardship, regulatory requirements for data quality, and the importance of getting buy-in from business stakeholders.

Adam Cox is a data management professional with over ten years of experience working in the public and private sector in the United Kingdom (UK). He is an experienced project and technical manager working on large-scale projects involving significant data migration and data integration. Adam Cox is currently working for an established UK financial institution as a Data Quality Consultant, mainly on regulatory reporting projects.

Popular OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.

Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.

Doing Data Governance — Guest John Ladley discusses his book How to Design, Deploy and Sustain Data Governance and how to understand the difference and relationship between data governance and enterprise information management.

Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).

Measuring Data Quality for Ongoing Improvement — Guest Laura Sebastian-Coleman discusses bringing together a better understanding of what is represented in data with the expectations for use in order to improve the overall quality of data.

The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside, using stories as an analytical tool and change management technique, and why we must never forget that “people are cool.”

Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.

Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt discusses Data Quality and Business Intelligence, including the speed versus quality debate of near-real-time decision making, and the future of predictive analytics.

The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.

The Art of Data Matching — Guest Henrik Liliendahl Sørensen discusses data matching concepts and practices, including different match techniques, candidate selection, presentation of match results, and business applications of data matching.

Data Profiling Early and Often — Guest James Standen discusses data profiling concepts and practices, and how bad data is often misunderstood and can be coaxed away from the dark side if you know how to approach it.

Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

September 03, 2013

Data is a Game Changer

September 03, 2013/ Jim Harris

Nowadays we hear a lot of chatter, rather reminiscent of the boisterous bluster of sports talk radio debates, about the potential of big data and its related technologies to enable predictive and real-time analytics and, by leveraging an infrastructure provided by the symbiotic relationship of cloud and mobile, serve up better business performance and an enhanced customer experience.

Sports have always provided great fodder for the data-obsessed with its treasure troves of statistical data dissecting yesterday’s games down to the most minute detail, which is called upon by experts and amateurs alike to try to predict tomorrow’s games as well as analyze in real-time the play-by-play of today’s games. Arguably, it was the bestselling book Moneyball by Michael Lewis, which was also adapted into a popular movie starring Brad Pitt, that brought data obsession to the masses, further fueling the hype and overuse of sports metaphors such as how data can be a game changer for businesses in any industry and of any size.

The Future is Now Playing on Center Court

Which is why it is so refreshing to see a tangible real-world case study for big data analytics being delivered with the force of an Andy Murray two-handed backhand as over the next two weeks the United States Tennis Association (USTA) welcomes hundreds of thousands of spectators to New York City’s Flushing Meadows for the 2013 U.S. Open tennis tournament. Both the fans in the stands and the millions more around the world will visit USOpen.org, via the web or mobile apps, in order to follow the action, watch live-streamed tennis matches, and get scores, stats, and the latest highlights and news thanks to IBM technologies.

Before, during, and after each match, predictive and real-time analytics drive IBM’s SlamTracker tool. Before matches, IBM analyzes 41 million data points collected from eight years of Grand Slam play, including head-to-head matches, similar player types, and playing surfaces. SlamTracker uses this data to create engaging and compelling tools for digital audiences, which identify key actions players must take to enhance their chances of winning, and give fans player information, match statistics, social sentiment, and more.

The infrastructure that supports the U.S. Open’s digital presence is hosted on an IBM SmartCloud. This flexible, scalable environment, managed by IBM Analytics, lets the USTA ensure continuous availability of their digital platforms throughout the tournament and year-round. The USTA and IBM give fans the ability to experience the matches from anywhere, with any device via a mobile-friendly site and engaging apps for multiple mobile platforms. Together these innovations make the U.S. Open experience immediate and intimate for fans sitting in the stands or on another continent.

Better Service, More Winners, and Fewer Unforced Errors

In tennis, a service (also known as a serve) is a shot to start a point. In business, a service is a shot to start a point of positive customer interaction, whether that’s a point of sale or an opportunity to serve a customer’s need (e.g., resolving a complaint).

In tennis, a winner is a shot not reached by your opponent, which wins you a point. In business, a winner is a differentiator not reached by your competitor, which wins your business a sale when it makes a customer choose your product or service.

In tennis, an unforced error is a failure to complete a service or return a shot, which cannot be attributed to any factor other than poor judgement or execution by the player. In business, an unforced error is a failure to service a customer or get a return on an investment, which cannot be attributed to any factor other than poor decision making or execution by the organization.

Properly supported by enabling technologies, businesses of all sizes, and across all industries, can capture and analyze data to uncover hidden patterns and trends that can help them achieve better service, more winners, and fewer unforced errors.

How can Data change Your Game?

Whether it’s on the court, in the stands, on the customer-facing front lines, in the dashboards used by executive management, or behind the scenes of a growing midsize business, data is a game changer. How can data change your game?

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don’t necessarily represent IBM’s positions, strategies, or opinions.

Caffeinated Thoughts on Technology for Midsize Businesses

The Symbiotic Relationship of Cloud and Mobile

Social Media Marketing: From Monologues to Dialogues

August 27, 2013

The Stone Wars of Root Cause Analysis

August 27, 2013/ Jim Harris

“As a single stone causes concentric ripples in a pond,” Martin Doyle commented on my blog post There is No Such Thing as a Root Cause, “there will always be one root cause event creating the data quality wave. There may be interference after the root cause event which may look like a root cause, creating eddies of side effects and confusion, but I believe there will always be one root cause. Work backwards from the data quality side effects to the root cause and the data quality ripples will be eliminated.”

Martin Doyle and I continued our congenial blog comment banter on my podcast episode The Johari Window of Data Quality, but in this blog post I wanted to focus on the stone-throwing metaphor for root cause analysis.

Let’s begin with the concept of a single stone causing the concentric ripples in a pond. Is the stone really the root cause? Who threw the stone? Why did that particular person choose to throw that specific stone? How did the stone come to be alongside the pond? Which path did the stone-thrower take to get to the pond? What happened to the stone-thrower earlier in the day that made them want to go to the pond, and once there, pick up a stone and throw it in the pond?

My point is that while root cause analysis is important to data quality improvement, too often we can get carried away riding the ripples of what we believe to be the root cause of poor data quality. Adding to the complexity is the fact there’s hardly ever just one stone. Many stones get thrown into our data ponds, and trying to un-ripple their poor quality effects can lead us to false conclusions because causation is non-linear in nature. Causation is a complex network of many interrelated causes and effects, so some of what appear to be the effects of the root cause you have isolated may, in fact, be the effects of other causes.

As Laura Sebastian-Coleman explains, data quality assessments are often “a quest to find a single criminal—The Root Cause—rather than to understand the process that creates the data and the factors that contribute to data issues and discrepancies.” Those approaching data quality this way, “start hunting for the one thing that will explain all the problems. Their goal is to slay the root cause and live happily ever after. Their intentions are good. And slaying root causes—such as poor process design—can bring about improvement. But many data problems are symptoms of a lack of knowledge about the data and the processes that create it. You cannot slay a lack of knowledge. The only way to solve a knowledge problem is to build knowledge of the data.”

Believing that you have found and eliminated the root cause of all your data quality problems is like believing that after you have removed the stones from your pond (i.e., data cleansing), you can stop the stone-throwers by building a high stone-deflecting wall around your pond (i.e., defect prevention). However, there will always be stones (i.e., data quality issues) and there will always be stone-throwers (i.e., people and processes) that will find a way to throw a stone in your pond.

In our recent podcast Measuring Data Quality for Ongoing Improvement, Laura Sebastian-Coleman and I discussed although root cause is used as a singular noun, just as data is used as a singular noun, we should talk about root causes since, just as data analysis is not analysis of a single datum, root cause analysis should not be viewed as analysis of a single root cause.

The bottom line, or, if you prefer, the ripple at the bottom of the pond, is the Stone Wars of Root Cause Analysis will never end because data quality is a journey, not a destination. After all, that’s why it’s called ongoing data quality improvement.

August 22, 2013

Measuring Data Quality for Ongoing Improvement

August 22, 2013/ Jim Harris

OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

Listen to Laura Sebastian-Coleman, author of the book Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework, and I discuss bringing together a better understanding of what is represented in data, and how it is represented, with the expectations for use in order to improve the overall quality of data. Our discussion also includes avoiding two common mistakes made when starting a data quality project, and defining five dimensions of data quality.

Laura Sebastian-Coleman has worked on data quality in large health care data warehouses since 2003. She has implemented data quality metrics and reporting, launched and facilitated a data quality community, contributed to data consumer training programs, and has led efforts to establish data standards and to manage metadata. In 2009, she led a group of analysts in developing the original Data Quality Assessment Framework (DQAF), which is the basis for her book.

Laura Sebastian-Coleman has delivered papers at MIT’s Information Quality Conferences and at conferences sponsored by the International Association for Information and Data Quality (IAIDQ) and the Data Governance Organization (DGO). She holds IQCP (Information Quality Certified Professional) designation from IAIDQ, a Certificate in Information Quality from MIT, a B.A. in English and History from Franklin & Marshall College, and a Ph.D. in English Literature from the University of Rochester.

Popular OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.

Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.

Doing Data Governance — Guest John Ladley discusses his book How to Design, Deploy and Sustain Data Governance and how to understand the difference and relationship between data governance and enterprise information management.

Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).

The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside, using stories as an analytical tool and change management technique, and why we must never forget that “people are cool.”

Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.

Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt discusses Data Quality and Business Intelligence, including the speed versus quality debate of near-real-time decision making, and the future of predictive analytics.

The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.

The Art of Data Matching — Guest Henrik Liliendahl Sørensen discusses data matching concepts and practices, including different match techniques, candidate selection, presentation of match results, and business applications of data matching.

Data Profiling Early and Often — Guest James Standen discusses data profiling concepts and practices, and how bad data is often misunderstood and can be coaxed away from the dark side if you know how to approach it.

Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

August 19, 2013

The Symbiotic Relationship of Cloud and Mobile

August 19, 2013/ Jim Harris

“Although many people are calling for a cloud revolution in which everyone simultaneously migrates their systems to the cloud,” David Linthicum recently blogged, “that’s not going to happen. While there will be no mass migration, there will be many one-off cloud migration projects that improve the functionality of systems, as well as cloud-based deployments of new systems.”

“This means that,” Linthicum predicted, “cloud computing’s growth will follow the same patterns of adoption we saw for the PC and the Web. We won’t notice many of the changes as they occur, but the changes will indeed come.” Perhaps the biggest driver of the cloud-based changes to come is the way many of us are using the cloud today — as a way to synchronize data across our multiple devices, the vast majority of which nowadays are mobile devices.

John Mason recently blogged about the symbiotic relationship between the cloud and mobile devices, which “not only expands the reach of small and midsize businesses, it levels the playing field too, helping them compete in a quickly changing business environment. Cloud-based applications help businesses stay mobile, agile, and responsive without sacrificing security or reliability, and even the smallest of companies can provide their customers with fast, around-the-clock access to important data.”

The age of the mobile device is upon us and it is thanks mainly to the cloud-based applications floating above us, enabling a mobile-app-portal-to-the-cloud computing model that is well-supported by the widespread availability of high-speed network connectivity options since, no matter where we are, it seems like a Wi-Fi or broadband mobile network is always available.

As more and more small and midsize businesses continue to leverage the symbiotic relationship between the cloud and mobile to build relationships with customers and rethink how work works, they are enabling the future of the collaborative economy.

Social Media Marketing: From Monologues to Dialogues

An Enterprise Carol

Caffeinated Thoughts on Technology for Midsize Businesses

i blog of Data glad and big

August 11, 2013

DQ-View: Microscopes, Telescopes, and Datascopes

August 11, 2013/ Jim Harris

A Data Quality (DQ) View is a brief video discussion of a data quality key concept.

August 06, 2013

Big Data is Just Another Brick in the Wall

August 06, 2013/ Jim Harris

The title of my recent blog post Chaos in the Big Data Brickyard made Mike Wheeler think it was a reference to the Indianapolis Motor Speedway, which is known as “The Brickyard” because it was paved entirely with bricks way back in 1909 (today, three feet of the original bricks remain at the start/finish line). This was a reasonable assumption by Wheeler since he is a NASCAR fan (thus making his last name a great example of an aptronym) and thus prompted his blog post Yeah, But Who Won The Race?

“The term brickyard taken without any context,” Wheeler explained, “turned out to be another random brick of fact laid on an already crowded foundation. Context is what provides relevance to facts. Without a frame of reference into which a fact can be inserted it can easily become meaningless or, even worse, detrimental to the decision-making process.”

As usual, I agree with Wheeler (except about being a NASCAR fan — my apologies to Mike and his fellow auto racing fans).

In my post Big Data, Sporks, and Decision Frames, I blogged about how having the right decision frame (i.e., understanding the business context of a decision) is essential to whether big data and data science can provide meaningful business insight.

Additional context often missing from discussions about big data and data science is that they are not the only bricks in the yard.

Data modeling is still important and data quality still matters. As does metadata, data management and business intelligence, data monitoring, communication, collaboration, change management and the many other aspects of data governance.

“A successful man,” David Brinkley once said, “is one who can lay a firm foundation with the bricks others have thrown at him.” A successful big data initiative is one that can lay a firm foundation with the bricks of best practices that the data management industry has been rightfully throwing at us for a long time now. Big data does not obviate the need for those best practices — even though it does occasionally require adapting our best practices as well as adopting new practices.

Big data is not the be all and end all, as it is sometimes overhyped, but instead, to paraphrase the great philosophers Pink Floyd:

All in all, big data is just another brick in the wall.

Related Podcasts

Clicking on the link will take you to the episode’s blog post:

Defining Big Data — This episode of the Open MIKE Podcast, with assistance from Robert Hillard, discusses how big data refers to big complexity, not big volume, even though complex datasets tend to grow rapidly, thus making them voluminous.

Too Big to Ignore — Guest Phil Simon, author of the book Too Big to Ignore: The Business Case for Big Data, offers advice on getting started with big data and remembering that big data is just another means toward solving business problems.

Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.

Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, experimentation, and correlation.

The Laugh-In Effect of Big Data

The Need for Data Philosophers

The Wisdom of Crowds, Friends, and Experts

HoardaBytes and the Big Data Lebowski

Magic Elephants, Data Psychics, and Invisible Gorillas

Information Overload Revisited

Our Increasingly Data-Constructed World

Big Data and the Infinite Inbox

Little Ooches prevent Big Data Ouches

It’s Not about being Data-Driven

Data Separates Science from Superstition

Through a PRISM, Darkly

Predictive Analytics, the Data Effect, and Jed Clampett

Rage against the Machines Learning

Darth Vader, Big Data, and Predictive Analytics

The Flying Monkeys of Big Data

Big Data, Sporks, and Decision Frames

Big Data, Predictive Analytics, and the Ideal Chronicler

July 30, 2013

Caffeinated Thoughts on Technology for Midsize Businesses

July 30, 2013/ Jim Harris

If you are having trouble viewing this video, then you can watch it on Vimeo via this link:vimeo.com/71338997

The following links are to the resources featured in or related to the content of this video:

IBM Coffee + Conversation on Reinvention in the Cloud: http://youtu.be/COcKGTTmSUA (YouTube Video)

Getting the Most from Cloud: http://goo.gl/ikFK5 (SlideShare Presentation)

IBM Coffee + Conversation on the Rise of Mobile: http://youtu.be/1nwF-nFL_jI (YouTube Video)

Mobile Device Defense: http://goo.gl/vmF2p (Infographic on Mobile Security)

IBM Coffee + Conversation on the Business of Social: http://youtu.be/xjFNoZKMgRo (YouTube Video)

Get Bold with Your Social Media: http://goo.gl/PCQ11 (Sandy Carter Book Review by Debbie Laskey)

IBM Coffee + Conversation on Partnering with Managed Service Providers: http://youtu.be/BFvgtnHui_I (YouTube Video)

IBM’s Ed Abrams discusses Managed Service Providers (MSPs): MSPs and Midsize Businesses (Audio Podcast)

IBM Coffee + Conversation on Evolving with Analytics: http://youtu.be/BFvgtnHui_I (YouTube Video)

Big Data, Small Data – or YOUR Data?: http://goo.gl/vgS1I (Blog Post by Eric D. Brown)

Social Media Marketing: From Monologues to Dialogues

DQ-BE: Invitation to Duplication

July 23, 2013

DQ-BE: The Time Traveling Gift Card

July 23, 2013/ Jim Harris

Data Quality By Example (DQ-BE) is an OCDQ regular segment that provides examples of data quality key concepts.

As an avid reader, I tend to redeem most of my American Express Membership Rewards points for Barnes & Noble gift cards to buy new books for my Nook. As a data quality expert, I tend to notice when something is amiss with data. As shown above, for example, my recent gift card was apparently issued on — and only available for use until — January 1, 1900.

At first, I thought I might have encountered the time traveling gift card. However, I doubted the gift card would be accepted as legal tender in 1900. Then I thought my gift card was actually worth $1,410 (what $50 in 1900 would be worth today), which would allow me to buy a lot more books — as long as Barnes & Noble would overlook the fact the gift card expired 113 years ago.

Fortunately, I was able to use the gift card to purchase $50 worth of books in 2013.

So, I guess the moral of this story is that sometimes poor data quality does pay. However, it probably never pays to display your poor data quality to someone who runs an obsessive-compulsive data quality blog with a series about data quality by example.

What examples (good or poor) of data quality have you encountered in your time travels?

DQ-BE: Dear Valued Customer

DQ-BE: Single Version of the Time

DQ-BE: Data Quality Airlines

Retroactive Data Quality

Sometimes Worse Data Quality is Better

Data Quality, 50023

DQ-IRL (Data Quality in Real Life)

The Seven Year Glitch

When Poor Data Quality Calls

Data has an Expiration Date

Sometimes it’s Okay to be Shallow

July 18, 2013

A Big Data Platform for Midsize Businesses

July 18, 2013/ Jim Harris

If you’re having trouble viewing this video, watch it on Vimeo via this link:A Big Data Platform for Midsize Businesses

The following links are to the infographics featured in this video, as well as links to other related resources:

Big Data: Making the World Go Round (IBM Blog Post containing the downloadable Infographic)

Big Data Exploration (Direct Link to the Infographic as a PDF document)

Webcast Replay: Why Big Data Matters to the Midmarket: http://goo.gl/A1WYZ (No Registration Required)

IBM’s 2012 Big Data Study with Feedback from People who saw Results: http://goo.gl/MmRAv (Registration Required)

Participate in IBM’s 2013 Business Value Survey on Analytics and Big Data: http://goo.gl/zKSPM (Registration Required)

Too Big to Ignore

Social Media Marketing: From Monologues to Dialogues