Secure the Engine to Your Business Future

People use mobile devices, as James Hailey Jr. blogged, “for almost everything they do in their day to day activities like listening to music, work, social applications, and calendar functions.  They allow people to immediately get information and access different resources.  In today’s world, there are more mobile devices than there have ever been in recent years and companies are just realizing the potential opportunities that exist.”

As Daniel Newman blogged, “cloud, mobile devices, Big Data, and social media have become a permanent fixture of today’s business.  From solopreneurs to global enterprises, companies are more connected than ever before to their customers, employees, shareholders, and stakeholders.  Enabled by connectivity and powered by the cloud, this is more than just Marketechture, this is the engine of our business future.”

“By embracing social tools in the cloud,” Rebecca Buisan blogged, “organizations can now attract new customers while at the same time better serve their existing clients, employees, and business partners.”

While cloud and mobile are enabling social business, it is not all blue skies and rainbows.  The age of the mobile device is still young, so as you embrace, with youthful exuberance, the convenience of the mobile-app-portal-to-the-cloud computing model, convenience should not trump security.

As Marissa Tejada blogged, despite your employees’ hands being full of business-enabling mobile devices, too few organizations are making sure mobility and security go hand in hand.  Especially when BYOD puts personal devices into business hands.

One example Allan Pratt blogged about is iOS7’s AirDrop feature, which uses a combination of Bluetooth and Wi-Fi ad-hoc networks.  “The bottom line,” Pratt explained, “is that while AirDrop may sound like a good idea in theory, it needs more security embedded into it for data transfers to be considered.  For SMBs, this means you should be wary of new technology until it has been proven safe and effective for the enterprise.  You don’t want your data walking out the door without your knowledge.”

With big data providing the 1.21 gigawatts (often with a lot more than 1.21 gigabytes) of power, social, cloud, and mobile technology is the flux capacitor driving companies of all sizes forward to the future of business.  Just as lightning never strikes twice, you don’t want to end up looking back in time, second-guessing why you didn’t secure the engine to your business future.

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Data is a Game Changer

Data is a Game Changer.png

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?

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Caffeinated Thoughts on Technology for Midsize Businesses

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Smart Big Data Adoption for Midsize Businesses

In a previous post, I explained that big data is not just for big businesses.  During a recent interview Ed Abrams discussed how mobile, social, and cloud are driving big data adoption among midsize businesses.

As Sharon Hurley Hall recently blogged, midsize businesses are adopting social for the simple reason “a significant proportion of your potential customers are online, and while there they could be buying your product or service.”  She also makes a great point about social adoption not being only externally focused.  “Social business technologies will improve internal communication and knowledge-sharing.  The result is a better-informed and more engaged workforce, and the technology gives the ability to harness creativity and implement innovation to increase your competitive advantage.”

“Becoming more social,” Hall concluded, “doesn’t mean that employees waste time online; in fact, it means that everyone is better informed about both data and strategy, leading to business benefits.  The combination of integrating social technologies to improve your operational efficiency and harnessing social data to boost your knowledge base means that your business can be more competitive and more profitable.”

Hall’s insights also exemplify the proper perspective for midsize businesses to use when adopting big data.  No business of any size should adopt big data just because everyone is talking about it, nor simply because they think it might help their business.

As with everything in the business world, you should seek first to understand what big data adoption can offer, and what kind of investment it requires, before making any type of commitment.  The best thing about big data for midsize businesses is that it provides a big list of possibilities.  But trying to embrace all of the possibilities of big data would be a big mistake.  Start small with big data.  Smart big data adoption for midsize businesses means starting with well-defined, business-enhancing opportunities.

 

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.

 

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Big Data is not just for Big Businesses

“It is widely assumed that big data, which imbues a sense of grandiosity, is only for those large enterprises with enormous amounts of data and the dedicated IT staff to tackle it,” opens the recent article Big data: Why it matters to the midmarket.

Much of the noise generated these days about the big business potential of big data certainly seems to contain very little signal directed at small and midsize businesses.  Although it’s true that big businesses generate more data, faster, and in more varieties, a considerable amount of big data is externally generated, much of which is freely available for use by businesses of all sizes.

The easiest example is the poster child for leveraging big data — Google Search.  But there’s also a growing number of open data sources (e.g., weather data) and social data sources (e.g., Twitter), and, since more of the world is becoming directly digitized, more businesses are now using more data no matter how big they are.  Additionally, as Phil Simon wrote about in The New Small, the free and open source software, as-a-service, cloud, mobile, and social technology trends driving the consumerization of IT are enabling small and midsize businesses to, among other things, use more data and be more competitive with big businesses.

“Each minute of every day, information is produced about the activities of your business, your customers, and your industry,” explained Sarita Harbour in her recent blog post Harnessing Big Data: Giving Midsize Business a Competitive Edge.  “Hidden within this enormous amount of data are trends, patterns, and indicators that, if extracted and identified, can yield important information to make your business more efficient and more competitive, and ultimately, it can make you more money.”

However, the biggest driver of the misperception about big data is its over-identification with data volume.  Which is why earlier this year in his blog post It’s time for a new definition of big data, Robert Hillard used several examples to explain that big data refers more to big complexity than big volume.  While acknowledging that complex datasets tend to grow rapidly, thus making big data voluminous, his wonderfully pithy conclusion was that “big data can be very small and not all large datasets are big.”

Therefore, by extension we could say that the businesses using big data can be small, or mid-sized, and not all the businesses using big data are big.  But, of course, that’s not quite pithy enough.  So let’s simply say that big data is not just for big businesses.

 

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.

 

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Devising a Mobile Device Strategy

As I previously blogged in The Age of the Mobile Device, the disruptiveness of mobile devices to existing business models is difficult to overstate.  Mobile was also cited as one of the complementary technology forces, along with social and cloud, in the recent Harvard Business Review blog post by R “Ray” Wang about new business models being enabled by big data.

Since their disruptiveness to existing IT models is also difficult to overstate, this post ponders the Bring Your Own Device (BYOD) trend that’s forcing businesses of all sizes to devise a mobile device strategy.  BYOD is often not about bringing your own device to the office, but about bringing your own device with you wherever you go (even though the downside of this untethered enterprise may be that our always precarious work-life balance surrenders to the pervasive work-is-life feeling mobile devices can enable).

In his recent InformationWeek article, BYOD: Why Mobile Device Management Isn’t Enough, Michael Davis observed that too many IT departments are not devising a mobile device strategy, but instead “they’re merely scrambling to meet pressure from the CEO on down to offer BYOD options or increase mobile app access.”  Davis also noted that when IT creates BYOD policies, they often to fail to acknowledge mobile devices have to be managed differently, partially since they are not owned by the company.

An alternative to BYOD, which Brian Proffitt recently blogged about, is Corporate Owned, Personally Enabled (COPE). “Plenty of IT departments see BYOD as a demon to be exorcised from the cubicle farms,” Proffitt explained, “or an opportunity to dump the responsibility for hardware upkeep on their internal customers.  The idea behind BYOD is to let end users choose the devices, programs, and services that best meet their personal and business needs, with access, support, and security supplied by the company IT department — often with subsidies for device purchases.”  Whereas the idea behind COPE is “the organization buys the device and still owns it, but the employee is allowed, within reason, to install the applications they want on the device.”

Whether you opt for BYOD or COPE, Information Management recently highlighted 5 Trouble Spots to consider, which included assuming that mobile device security is already taken care of by in-house security initiatives, data integration disconnects with on-premises data essentially turning mobile devices into mobile data silos, and the combination of personal and business data, which complicates, among other things, remote wiping the data on a mobile device in the event of a theft or security violation, which is why, as Davis concluded, managing the company data on the device is more important than managing the device itself.

With the complex business and IT challenges involved, how is your midsize business devising a mobile device strategy?

 

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.

 

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The Wisdom of Crowds, Friends, and Experts

I recently finished reading the TED Book by Jim Hornthal, A Haystack Full of Needles, which included an overview of the different predictive approaches taken by one of the most common forms of data-driven decision making in the era of big data, namely, the recommendation engines increasingly provided by websites, social networks, and mobile apps.

These recommendation engines primarily employ one of three techniques, choosing to base their data-driven recommendations on the “wisdom” provided by either crowds, friends, or experts.

 

The Wisdom of Crowds

In his book The Wisdom of Crowds, James Surowiecki explained that the four conditions characterizing wise crowds are diversity of opinion, independent thinking, decentralization, and aggregation.  Amazon is a great example of a recommendation engine using this approach by assuming that a sufficiently large population of buyers is a good proxy for your purchasing decisions.

For example, Amazon tells you that people who bought James Surowiecki’s bestselling book also bought Thinking, Fast and Slow by Daniel Kahneman, Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business by Jeff Howe, and Wikinomics: How Mass Collaboration Changes Everything by Don Tapscott.  However, Amazon neither provides nor possesses knowledge of why people bought all four of these books or qualification of the subject matter expertise of these readers.

However, these concerns, which we could think of as potential data quality issues, and which would be exacerbated within a small amount of transaction data where the eclectic tastes and idiosyncrasies of individual readers would not help us decide what books to buy, within a large amount of transaction data, we achieve the Wisdom of Crowds effect when, taken in aggregate, we receive a general sense of what books we might like to read based on what a diverse group of readers collectively makes popular.

As I blogged about in my post Sometimes it’s Okay to be Shallow, sometimes the aggregated, general sentiment of a large group of unknown, unqualified strangers will be sufficient to effectively make certain decisions.

 

The Wisdom of Friends

Although the influence of our friends and family is the oldest form of data-driven decision making, historically this influence was delivered by word of mouth, which required you to either be there to hear those influential words when they were spoken, or have a large enough network of people you knew that would be able to eventually pass along those words to you.

But the rise of social networking services, such as Twitter and Facebook, has transformed word of mouth into word of data by transcribing our words into short bursts of social data, such as status updates, online reviews, and blog posts.

Facebook “Likes” are a great example of a recommendation engine that uses the Wisdom of Friends, where our decision to buy a book, see a movie, or listen to a song might be based on whether or not our friends like it.  Of course, “friends” is used in a very loose sense in a social network, and not just on Facebook, since it combines strong connections such as actual friends and family, with weak connections such as acquaintances, friends of friends, and total strangers from the periphery of our social network.

Social influence has never ended with the people we know well, as Nicholas Christakis and James Fowler explained in their book Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives.  But the hyper-connected world enabled by the Internet, and further facilitated by mobile devices, has strengthened the social influence of weak connections, and these friends form a smaller crowd whose wisdom is involved in more of our decisions than we may even be aware of.

 

The Wisdom of Experts

Since it’s more common to associate wisdom with expertise, Pandora is a great example of a recommendation engine that uses the Wisdom of Experts.  Pandora used a team of musicologists (professional musicians and scholars with advanced degrees in music theory) to deconstruct more than 800,000 songs into 450 musical elements that make up each performance, including qualities of melody, harmony, rhythm, form, composition, and lyrics, as part of what Pandora calls the Music Genome Project.

As Pandora explains, their methodology uses precisely defined terminology, a consistent frame of reference, redundant analysis, and ongoing quality control to ensure that data integrity remains reliably high, believing that delivering a great radio experience to each and every listener requires an incredibly broad and deep understanding of music.

Essentially, experts form the smallest crowd of wisdom.  Of course, experts are not always right.  At the very least, experts are not right about every one of their predictions.  Nor do experts always agree with other, which is why I imagine that one of the most challenging aspects of the Music Genome Project is getting music experts to consistently apply precisely the same methodology.

Pandora also acknowledges that each individual has a unique relationship with music (i.e., no one else has tastes exactly like yours), and allows you to “Thumbs Up” or “Thumbs Down” songs without affecting other users, producing more personalized results than either the popularity predicted by the Wisdom of Crowds or the similarity predicted by the Wisdom of Friends.

 

The Future of Wisdom

It’s interesting to note that the Wisdom of Experts is the only one of these approaches that relies on what data management and business intelligence professionals would consider a rigorous approach to data quality and decision quality best practices.  But this is also why the Wisdom of Experts is the most time-consuming and expensive approach to data-driven decision making.

In the past, the Wisdom of Crowds and Friends was ignored in data-driven decision making for the simple reason that this potential wisdom wasn’t digitized.  But now, in the era of big data, not only are crowds and friends digitized, but technological advancements combined with cost-effective options via open source (data and software) and cloud computing make these approaches quicker and cheaper than the Wisdom of Experts.  And despite the potential data quality and decision quality issues, the Wisdom of Crowds and/or Friends is proving itself a viable option for more categories of data-driven decision making.

I predict that the future of wisdom will increasingly become an amalgamation of experts, friends, and crowds, with the data and techniques from all three potential sources of wisdom often acknowledged as contributors to data-driven decision making.

 

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Social Business is more than Social Marketing

Although much of the early business use of social media was largely focused on broadcasting marketing messages at customers, social media transformed word of mouth into word of data and empowered customers to add their voice to marketing messages, forcing marketing to evolve from monologues to dialogues.  But is the business potential of social media limited to marketing?

During the MidMarket IBM Social Business #Futurecast, a panel discussion from earlier this month, Ed Brill, author of the forthcoming book Opting In: Lessons in Social Business from a Fortune 500 Product Manager, defined the term social business as “an organization that engages employees in a socially-enabled process that brings together how employees interact with each other, partners, customers, and the marketplace.  It’s about bringing all the right people, both internally and externally, together in a conversation to solve problems, be innovative and responsive, and better understand marketplace dynamics.”

“Most midsize businesses today,” Laurie McCabe commented, “are still grappling with how to supplement traditional applications and tools with some of the newer social business tools.  Up until now, the focus has been on integrating social media into a lot of marketing communications, and we haven’t yet seen the integration of social media into other business processes.”

“Midsize businesses understand,” Handly Cameron remarked, “how important it is to get into social media, but they’re usually so focused on daily operations that they think that a social business is simply one that uses social media, and therefore they cite the facts that they created Twitter and Facebook accounts as proof that they are a social business, but again, they are focusing on external uses of social media and not internal uses such as improving employee collaboration.”

Collaboration was a common theme throughout the panel discussion.  Brill said a social business is one that has undergone the cultural transformation required to embrace the fact that it is a good idea to share knowledge.  McCabe remarked that the leadership of a social business rewards employees for sharing knowledge, not for hoarding knowledge.  She also emphasized the importance of culture before tools since simply giving individuals social tools will not automatically create a collaborative culture.

Cameron also noted how the widespread adoption of cloud computing and mobile devices is helping to drive the adoption of social tools for collaboration, and helping to break down a lot of the traditional boundaries to knowledge sharing, especially as more organizations are becoming less bounded by the physical proximity of their employees, partners, and customers.

From my perspective, even though marketing might have been how social media got in the front door of many organizations, social media has always been about knowledge sharing and collaboration.  And with mobile, cloud, and social technologies so integrated into our personal and professional lives, life and business are both more social and collaborative than ever before.  So, even if collaboration isn’t in the genes of your organization, it’s no longer possible to put the collaboration genie back in the bottle.

 

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.

 

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Social Media Marketing: From Monologues to Dialogues

“With social media analytics,” Ed Abrams and Jay Hakami recently blogged, midsize businesses “can calculate the ROI and assess the effectiveness of their social media marketing campaigns by tracking both the actions of consumers and the influence of their top commenters and re-tweeters providing a level of insight on the individual never seen before . . . allowing them to make informed business decisions on how to best leverage their online presence.”

But perhaps the most challenging aspect for businesses trying to best leverage their online presence by using social media in their marketing campaigns is that it involves the presence of voices that they don’t have control over — their customers’ voices.

Social media has transformed word of mouth into word of data.  And, as more companies are being forced to acknowledge, the digital mouths of customers speak volumes.  Social media is empowering customers to add their voice to marketing messages.

“Everyone loves to talk about customers engaging with brands,” Rick Robinson recently blogged.  “But in the process, customers are also taking over brands.  The message for midsize firms is that they can no longer count on shaping the conversation.”

“Social media offers,” Dan Berthiaume recently blogged, “the opportunity to directly engage with customers for real-time feedback.  Social media marketing at its core is a relatively inexpensive and fast way of conducting marketing.”  True, however as Paul Gillin explained during our recent podcast discussion about social media for midsize businesses, the fundamental difference between traditional marketing and social media marketing is that the former is one-way, whereas the latter is two-way.

In other words, marketing has not historically looked to engage with customers to receive feedback.  Marketing has traditionally broadcasted messages at customers — and marketing’s early use of social media has been as just another broadcast channel.

However, “social media is a process of continual conversation,” Gillin explained.  “It’s a very different way to go about marketing, but the natural tendency for people when they see something new is to apply the old metaphors to it.  What you’ve seen during the first five years of social media’s popularity is a lot of use of these platforms as essentially the same old marketing channels.”

“We see more companies every year that are getting the idea that social media is a two-way conversation,” Gillin continued, “but it’s a difficult skill to develop.  Marketers are not taught in school or at work to converse — they’re taught to deliver messages.  So that’s turning around a pretty big battleship trying to convince and teach all these people the skills of two-way engagement.”

Marketing has long been accustomed to controlling a conversation that was never really a conversation — since marketers did all the talking, and customers could only listen or ignore them.  Social media is evolving marketing from monologues to dialogues.

Is your midsize business ready and, more importantly, willing to engage customers in an actual conversation?

 

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.

 

Social Media for Midsize Businesses

OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, Paul Gillin and I discuss social media for midsize businesses, including how the less marketing you do, the more effective you will be with social media marketing, the war of generosity, where the more you give, the more you get, and the importance of the trust equation, which means the more people trust you, the more they will want to do business with you.

Paul Gillin is a veteran technology journalist and a thought leader in new media.  Since 2005, he has advised marketers and business executives on strategies to optimize their use of social media and online channels to reach buyers cost-effectively.  He is a popular speaker who is known for his ability to simplify complex concepts using plain talk, anecdotes, and humor.

Paul Gillin is the author of four books about social marketing: The New Influencers (2007), Secrets of Social Media Marketing (2008), Social Marketing to the Business Customer (2011), co-authored with Eric Schwartzman, and the forthcoming book Attack of the Customers (2012), co-authored with Greg Gianforte.

Paul Gillin was previously the founding editor of TechTarget and editor-in-chief of Computerworld.  He writes a monthly column for BtoB magazine and is an active blogger and media commentator.  He has appeared as an expert commentator on CNN, PBS, Fox News, MSNBC, and other television outlets.  He has also been quoted or interviewed for hundreds of news and radio reports in outlets such as The Wall Street Journal, The New York Times, NPR, and the BBC.  Paul Gillin is a Senior Research Fellow and member of the board of directors at the Society for New Communications Research.

 

Social Media for Midsize Businesses

Additional listening options:

 

This podcast was sponsored by 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.

 

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Demystifying Social Media

In this eight-minute video, I attempt to demystify social media, which is often over-identified with the technology that enables it, when, in fact, we have always been social, and we have always used media, because social media is about human communication, about humans communicating in the same ways they have always communicated, by sharing images, memories, stories, words, and more often nowadays, we are communicating by sharing photographs, videos, and messages via social media status updates.

This video briefly discusses the three social media services used by my local Toastmasters clubPinterest, Vimeo, and Twitter — and concludes with an analogy inspired by The Emerald City and The Yellow Brick Road from The Wizard of Oz:

If you are having trouble viewing this video, then you can watch it on Vimeo by clicking on this link: Demystifying Social Media

You can also watch a regularly updated page of my videos by clicking on this link: OCDQ Videos

 

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Exercise Better Data Management

Recently on Twitter, Daragh O Brien and I discussed his proposed concept.  “After Big Data,” Daragh tweeted, “we will inevitably begin to see the rise of MOData as organizations seek to grab larger chunks of data and digest it.  What is MOData?  It’s MO’Data, as in MOre Data. Or Morbidly Obese Data.  Only good data quality and data governance will determine which.”

Daragh asked if MO’Data will be the Big Data Killer.  I said only if MO’Data doesn’t include MO’BusinessInsight, MO’DataQuality, and MO’DataPrivacy (i.e., more business insight, more data quality, and more data privacy).

“But MO’Data is about more than just More Data,” Daragh replied.  “It’s about avoiding Morbidly Obese Data that clogs data insight and data quality, etc.”

I responded that More Data becomes Morbidly Obese Data only if we don’t exercise better data management practices.

Agreeing with that point, Daragh replied, “Bring on MOData and the Pilates of Data Quality and Data Governance.”

To slightly paraphrase lines from one of my favorite movies — Airplane! — the Cloud is getting thicker and the Data is getting laaaaarrrrrger.  Surely I know well that growing data volumes is a serious issue — but don’t call me Shirley.

Whether you choose to measure it in terabytes, petabytes, exabytes, HoardaBytes, or how much reality bites, the truth is we were consuming way more than our recommended daily allowance of data long before the data management industry took a tip from McDonald’s and put the word “big” in front of its signature sandwich.  (Oh great . . . now I’m actually hungry for a Big Mac.)

But nowadays with silos replicating data, as well as new data, and new types of data, being created and stored on a daily basis, our data is resembling the size of Bob Parr in retirement, making it seem like not even Mr. Incredible in his prime possessed the super strength needed to manage all of our data.  Those were references to the movie The Incredibles, where Mr. Incredible was a superhero who, after retiring into civilian life under the alias of Bob Parr, elicits the observation from this superhero costume tailor: “My God, you’ve gotten fat.”  Yes, I admit not even Helen Parr (aka Elastigirl) could stretch that far for a big data joke.

A Healthier Approach to Big Data

Although Daragh’s concerns about morbidly obese data are valid, no superpowers (or other miracle exceptions) are needed to manage all of our data.  In fact, it’s precisely when we are so busy trying to manage all of our data that we hoard countless bytes of data without evaluating data usage, gathering data requirements, or planning for data archival.  It’s like we are trying to lose weight by eating more and exercising less, i.e., consuming more data and exercising less data quality and data governance.  As Daragh said, only good data quality and data governance will determine whether we get more data or morbidly obese data.

Losing weight requires a healthy approach to both diet and exercise.  A healthy approach to diet includes carefully choosing the food you consume and carefully controlling your portion size.  A healthy approach to exercise includes a commitment to exercise on a regular basis at a sufficient intensity level without going overboard by spending several hours a day, every day, at the gym.

Swimming is a great form of exercise, but swimming in big data without having a clear business objective before you jump into the pool is like telling your boss that you didn’t get any work done because you decided to spend all day working out at the gym.

Carefully choosing the data you consume and carefully controlling your data portion size is becoming increasingly important since big data is forcing us to revisit information overload.  However, the main reason that traditional data management practices often become overwhelmed by big data is because traditional data management practices are not always the right approach.

We need to acknowledge that some big data use cases differ considerably from traditional ones.  Data modeling is still important and data quality still matters, but how much data modeling and data quality is needed before big data can be effectively used for business purposes will vary.  In order to move the big data discussion forward, we have to stop fiercely defending our traditional perspectives about structure and quality.  We also have to stop fiercely defending our traditional perspectives about analytics, since there will be some big data use cases where depth and detailed analysis may not be necessary to provide business insight.

Better than Big or More

Jim Ericson explained that your data is big enough.  Rich Murnane explained that bigger isn’t better, better is better.  Although big data may indeed be followed by more data that doesn’t necessarily mean we require more data management in order to prevent more data from becoming morbidly obese data.  I think that we just need to exercise better data management.

 

Related Posts

Commendable Comments (Part 13)

Welcome to the 400th Obsessive-Compulsive Data Quality (OCDQ) blog post!  I am commemorating this milestone with the 13th entry in my ongoing series for expressing gratitude to my readers for their truly commendable comments on my blog posts.

 

Commendable Comments

On Will Big Data be Blinded by Data Science?, Meta Brown commented:

“Your concern is well-founded. Knowing how few businesses make really good use of the small data they’ve had around all along, it’s easy to imagine that they won’t do any better with bigger data sets.

I wrote some hints for those wallowing into the big data mire in my post, Better than Brute Force: Big Data Analytics Tips. But the truth is that many organizations won’t take advantage of the ideas that you are presenting, or my tips, especially as the datasets grow larger. That’s partly because they have no history in scientific methods, and partly because the data science movement is driving employers to search for individuals with heroically large skill sets.

Since few, if any, people truly meet these expectations, those hired will have real human limitations, and most often they will be people who know much more about data storage and manipulation than data analysis and applications.”

On Will Big Data be Blinded by Data Science?, Mike Urbonas commented:

“The comparison between scientific inquiry and business decision making is a very interesting and important one. Successfully serving a customer and boosting competitiveness and revenue does require some (hopefully unique) insights into customer needs. Where do those insights come from?

Additionally, scientists also never stop questioning and improving upon fundamental truths, which I also interpret as not accepting conventional wisdom — obviously an important trait of business managers.

I recently read commentary that gave high praise to the manager utilizing the scientific method in his or her decision-making process. The author was not a technologist, but rather none other than Peter Drucker, in writings from decades ago.

I blogged about Drucker’s commentary, data science, the scientific method vs. business decision making, and I’d value your and others’ input: Business Managers Can Learn a Lot from Data Scientists.”

On Word of Mouth has become Word of Data, Vish Agashe commented:

“I would argue that listening to not only customers but also business partners is very important (and not only in retail but in any business). I always say that, even if as an organization you are not active in the social world, assume that your customers, suppliers, employees, competitors are active in the social world and they will talk about you (as a company), your people, products, etc.

So it is extremely important to tune in to those conversations and evaluate its impact on your business. A dear friend of mine ventured into the restaurant business a few years back. He experienced a little bit of a slowdown in his business after a great start. He started surveying his customers, brought in food critiques to evaluate if the food was a problem, but he could not figure out what was going on. I accidentally stumbled upon Yelp.com and noticed that his restaurant’s rating had dropped and there were some complaints recently about services and cleanliness (nothing major though).

This happened because he had turnover in his front desk staff. He was able to address those issues and was able to reach out to customers who had bad experience (some of them were frequent visitors). They were able to go back and comment and give newer ratings to his business. This helped him with turning the corner and helped with the situation.

This was a big learning moment for me about the power of social media and the need for monitoring it.”

On Data Quality and the Bystander Effect, Jill Wanless commented:

“Our organization is starting to develop data governance processes and one of the processes we have deliberately designed is to get to the root cause of data quality issues.

We’ve designed it so that the errors that are reported also include the userid and the system where the data was generated. Errors are then filtered by function and the business steward responsible for that function is the one who is responsible for determining and addressing the root cause (which of course may require escalation to solve).

The business steward for the functional area has the most at stake in the data and is typically the most knowledgeable as to the process or system that may be triggering the error. We have yet to test this as we are currently in the process of deploying a pilot stewardship program.

However, we are very confident that it will help us uncover many of the causes of the data quality problems and with lots of PLAN, DO, CHECK, and ACT, our goal is to continuously improve so that our need for stewardship eventually (many years away no doubt) is reduced.”

On The Return of the Dumb Terminal, Prashanta Chandramohan commented:

“I can’t even imagine what it’s like to use this iPad I own now if I am out of network for an hour. Supposedly the coolest thing to own and a breakthrough innovation of this decade as some put it, it’s nothing but a dumb terminal if I do not have 3G or Wi-Fi connectivity.

Putting most of my documents, notes, to-do’s, and bookmarked blogs for reading later (e.g., Instapaper) in the cloud, I am sure to avoid duplicating data and eliminate installing redundant applications.

(Oops! I mean the apps! :) )

With cloud-based MDM and Data Quality tools starting to linger, I can’t wait to explore and utilize the advantages these return of dumb terminals bring to our enterprise information management field.”

On Big Data Lessons from Orbitz, Dylan Jones commented:

“The fact is that companies have always done predictive marketing, they’re just getting smarter at it.

I remember living as a student in a fairly downtrodden area that because of post code analytics meant I was bombarded with letterbox mail advertising crisis loans to consolidate debts and so on. When I got my first job and moved to a new area all of a sudden I was getting loans to buy a bigger car. The companies were clearly analyzing my wealth based on post code lifestyle data.

Fast forward and companies can do way more as you say.

Teresa Cottam (Global Telecoms Analyst) has cited the big telcos as a major driver in all this, they now consider themselves data companies so will start to offer more services to vendors to track our engagement across the entire communications infrastructure (Read more here: http://bit.ly/xKkuX6).

I’ve just picked up a shiny new Mac this weekend after retiring my long suffering relationship with Windows so it will be interesting to see what ads I get served!”

And please check out all of the commendable comments received on the blog post: Data Quality and Chicken Little Syndrome.

 

Thank You for Your Comments and Your Readership

You are Awesome — which is why receiving your comments has been the most rewarding aspect of my blogging experience over the last 400 posts.  Even if you have never posted a comment, you are still awesome — feel free to tell everyone I said so.

This entry in the series highlighted commendable comments on blog posts published between April 2012 and June 2012.

Since there have been so many commendable comments, please don’t be offended if one of your comments wasn’t featured.

Please continue commenting and stay tuned for future entries in the series.

Thank you for reading the Obsessive-Compulsive Data Quality blog.  Your readership is deeply appreciated.

 

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Commendable Comments (Part 2)

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