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Archive for March, 2013

by: Ocdqblog
28  Mar  2013

A Contrarian’s View of Unstructured Data

“If you analyzed the flow of digital data in 1980,” Stephen Baker wrote in his 2011 book Final Jeopardy: Man vs. Machine and the Quest to Know Everything, “only a smidgen of the world’s information had found its way into computers.”

“Back then, the big mainframes and the new microcomputers housed business records, tax returns, real estate transactions, and mountains of scientific data.  But much of the world’s information existed in the form of words—conversations at the coffee shop, phone calls, books, messages scrawled on Post-its, term papers, the play-by-play of the Super Bowl, the seven o’clock news.  Far more than numbers, words spelled out when humans were thinking, what they knew, what they wanted, whom they loved.  And most of those words, and the data they contained, vanished quickly.  They faded in fallible human memories, they piled up in dumpsters and moldered in damp basements.  Most of these words never reached computers, much less networks.”

However, during the era of big data, things have significantly changed.  “In the last decade,” Baker continued, “as billions of people have migrated their work, mail, reading, phone calls, and webs of friendships to digital networks, a giant new species of data has arisen: unstructured data.”

“It’s the growing heap of sounds and images that we produce, along with trillions of words.  Chaotic by nature, it doesn’t fit neatly into an Excel spreadsheet.  Yet it describes the minute-by-minute goings-on of much of the planet.  This gold mine is doubling in size every year.  Of all the data stored in the world’s computers and coursing through its networks, the vast majority is unstructured.”

One of Melinda Thielbar’s three questions of data science is: “Are these results actionable?”  As Baker explained, unstructured data describes the minute-by-minute goings-on of much of the planet, so the results of analyzing unstructured data must be actionable, right?

Although sentiment analysis of unstructured social media data is often lauded as a great example, late last year Augie Ray wrote a great blog post asking How Powerful Is Social Media Sentiment Really?

My contrarian’s view of unstructured data is that it is, in large part, gigabytes of gossip and yottabytes of yada yada digitized, rumors and hearsay amplified by the illusion-of-truth effect and succumbing to the perception-is-reality effect until the noise amplifies so much that its static solidifies into a signal.

As Roberta Wohlstetter originally defined the terms, signal is the indication of an underlying truth behind a statistical or predictive problem, and noise is the sound produced by competing signals.

The competing signals from unstructured data are competing with other signals in a digital world of seemingly infinite channels broadcasting a cacophony that makes one nostalgic for a luddite’s dream of a world before word of mouth became word of data, and before private thoughts contained within the neural networks of our minds became public thoughts shared within social networks, such as Twitter, Facebook, and LinkedIn.

“While it may seem heretical to say,” Ray explained, “I believe there is ample evidence social media sentiment does not matter equally in every industry to every company in every situation.  Social media sentiment has been elevated to God-like status when really it is more of a minor deity.  In most situations, what others are saying does not trump our own personal experiences.  In addition, while public sentiment may be a factor in our purchase decisions, we weigh it against many other important factors such as price, convenience, perception of quality, etc.”

Social media is not the only source of unstructured data, nor am I suggesting there’s no business value in this category of big data.  However, sometimes a contrarian’s view is necessary to temper unchecked enthusiasm, and a lot of big data is not only unstructured, but enthusiasm for it is often unchecked.

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Category: Information Development
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by: Robert.hillard
24  Mar  2013

Teleworking requires good information sharing

Teleworking has been in the press recently after Yahoo! CEO, Marissa Mayer, banned the practice arguing that innovation and productivity require Yahoo! Employees to be present in the office.  Many HR managers have seized on the reluctance of Yahoo! and some other tech companies such as Google to embrace teleworking to argue that the trend is coming to an end.

Taken to its logical conclusion, opposition to teleworking implies that global operating models also don’t work given that the objection to collaborating electronically must apply equally to employees who are in different offices as it does to those who are working from home.  Clearly this can’t be the case or the modern multinational business could not continue to thrive.

Whether it is a single employee working remotely or a team operating virtually over the globe, there are five principles that are needed to make them successful.  At their core they are about establishing a free flow of information.

Principle #1: Make activity and presence visible

Ensure that there is clear information demonstrating activity.  In person, a manager can judge activity by seeing how many people are sitting at their desks.  In a virtual team, it is important that some sort of live presence be available so that everyone knows who is working and when.

Principle #2: Show progress daily

There need to be clear indicators of progress.  For knowledge workers it is hard enough when operating in person to know how much progress has been made to an elusive goal, whether it is a new product or simply evolving the business towards a more efficient way of working.  Operating virtually it is vital that there are very short term objectives that are visible to the whole team.  Ideally this can be done through use of gamification techniques.

Principle #3: Make knowledge sharing a core activity

Without the informal sharing that is possible through accidental encounters, it is even more important that knowledge is encoded and shared.  There is no magic to this, it is simply critical that the loading of new artefacts is a core metric of everyone on the team and its value is recognised.

Principle #4: Build strong informal relationships

Just because the team is virtual doesn’t mean that they can’t develop strong personal bonds.  Social networks have shown how it is possible for people who have never met to become close friends.  Use similar techniques to develop relationships across virtual teams through a range of informal connections.

Principle #5: Encourage good phone conference etiquette

One of the biggest obstacles for teleworking is bad phone conferences.  It is too tempting to have long calls where the majority of participants simply go on mute and do something else.  These meetings wouldn’t be allowed to continue if people were present in the same room.  Similarly, virtual teams fragment when small groups within the team are in the same location and put their phone on mute and compare notes.  The rule should be that everyone is present, is free to challenge the value and that all comments are for all participants.

Category: Information Strategy, Web2.0
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by: Bsomich
23  Mar  2013

Weekly IM Update.

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A Structural Overview of MIKE 2.0

If you’re not already familiar, here is an intro to the structure of the MIKE2.0 methodology and associated content:

  • A New Model for the Enterprise provides an intro rationale for MIKE2.0
  • What is MIKE2.0? is a good basic intro to the methodology with some of the major diagrams and schematics
  • Introduction to MIKE2.0 is a category of other introductory articles
  • Mike 2.0 How To – provides a listing of basic articles of how to work with and understand the MIKE2.0 system and methodology.
  • Alternative Release Methodologies describes current thinking about how the basic structure of MIKE2.0 can itself be modified and evolve. The site presently follows a hierarchical model with governance for major changes, though branching and other models could be contemplated.

We hope you find this of benefit and welcome any suggestions you may have to improve it.

Sincerely,
MIKE2.0 Community

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This Week’s Blogs for Thought:

Developing Communities of Knowledge

In the past few years, the rise of online collaboration software has shifted the conversation of work from the board room to the chat room. Organizations of any size are able to host meetings, forums and other discussion boards that span time and place.

With all this new technology, new conversations are developing. Whether or not the organization is listening, the employees are talking. Behind all the “water cooler talk,” ideas, experiences, and best practices are being shared. A community of expertise is growing, and the smart organization is left wondering how to best untap and manage it.
Read more.

Bigger Data Needs Better Metadata
Information, data, and metadata are three interrelated words we hear a lot in the enterprise information management industry. An example of the difference, and relationship, between data and information is grapes and wine, where data is to grapes as information is to wine, meaning that information is created from data. And metadata is essential to understanding data, information, and the business and technical aspects of the processes that transform data into information.

Read more.

Big Data Analytics: Don’t Forget the Endgame
We’re hearing a great deal these days about Big Data and related terms, one of which is Big Data analytics. There are many definitions of this term and here’s one as good as any.

Read more.

Category: Information Development
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by: Bsomich
21  Mar  2013

Developing Communities of Knowledge: What are the bread crumbs that lead to success?

In the past few years, the rise of online collaboration software has shifted the conversation of work from the board room to the chat room. Organizations of any size are able to host meetings, forums and other discussion boards that span time and place.

With all this new technology, new conversations are developing. Whether or not the organization is listening, the employees are talking. Behind all the “water cooler talk,” ideas, experiences, and best practices are being shared. A community of expertise is growing, and the smart organization is left wondering how to best untap and manage it.

We all have a purpose for sharing our expertise. For some, its exposure. For others, therapy.  On the enterprise level, many of us do it for personal gain. Or out of fear. Learning what motivates your employees is key to understanding what tactics you’ll need to employ to help guide the conversation. Like dropping bread crumbs to find your way home, you need to create a path to steer the discussion toward your end goal.

When it comes to fostering a community of expertise, what tactics or incentives have you had success with? What are your bread crumbs?

Category: Enterprise2.0, Information Development
No Comments »

by: Ocdqblog
19  Mar  2013

Bigger Data needs Better Metadata

Information, data, and metadata are three interrelated words we hear a lot in the enterprise information management industry.  An example of the difference, and relationship, between data and information is grapes and wine, where data is to grapes as information is to wine, meaning that information is created from data.  And metadata is essential to understanding data, information, and the business and technical aspects of the processes that transform data into information.

In fact, the importance of metadata adding context all along the journey from data to information can not be overstated.  As David Weinberger explained in his book Too Big to Know, “the atoms of data hook together only because they share metadata.”

Although it has always played an essential role in information developmentmetadata management has an even bigger role play in the era of big data and information overload.

“The solution to the information overload problem,” according to Weinberger, “is to create more information: metadata.  When you put a label on a folder, you’re using metadata so that you can find the papers within it . . . just as a caption helps us make sense of a photo.”

Photos in need of captions and videos in need of categories are great examples of the growing rise of unstructured data, which is deepening our dependence on metadata.  And the semi-structured data of social media (e.g., tweets with hashtags) is another example of how data without the context provided by metadata will never be able to complete its journey to information.

Of course, the journey doesn’t end with information.  In 1988, Russell Ackoff, as Weinberger explained, “sketched a pyramid that has probably been redrawn on a white board somewhere in the world every hour since.  The largest layer at the bottom of the pyramid represents data, followed by successively narrower layers of information, knowledge, understanding, and wisdom.  The drawing makes perfect visual sense: There’s obviously plenty of data in the world, but not a lot of wisdom.  Starting from mere ones and zeroes, up through what they stand for, what they mean, what sense they make, and what insight they provide, each layer acquires value from the ones below it.”  Furthermore, I would argue that metadata provides the footholds allowing us to scale from one layer of the pyramid to the next.

Metadata is our guide on the journey from data to information, enabling us to understand the often complex business and technical contexts surrounding enterprise information management, and allowing us to journey further toward meaningful knowledge and actionable insight.

A lot of the fast-moving large volumes of various data swimming in the big data primordial soup are unstructured or semi-structured.  Without metadata, the amino acids of data won’t combine into the protein chains of information, the building blocks of meaningful knowledge and actionable insight.

Data has always needed metadata, but as you make the business case for big data in your organization, you’d better remember the bigger your data, the better your metadata needs to be.  In other words, bigger data needs better metadata.

Tags: , ,
Category: Information Development, Metadata
2 Comments »

by: Phil Simon
19  Mar  2013

Big Data Analytics: Don’t Forget the Endgame

We’re hearing a great deal these days about Big Data and related terms, one of which is Big Data analytics. There are many definitions of this term and here’s one as good as any:

Big Data analytics is the process of examining large amounts of data of a variety of types (Big Data) to uncover hidden patterns, unknown correlations, and other useful information. Such information can provide competitive advantages over rival organizations and result in business benefits, such as more effective marketing and increased revenue.

You’ll get no argument from me on the importance of defining key terms, be it Big Data, analytics, platforms, etc. Many blown IT projects or corporate initiatives can trace their failures to people not being on the same page from day one.

And this is why I’m a bit skeptical of the term Big Data analytics. Is the focus on Big Data? Analytics? Both?

Where’s the Focus?

I’d actually argue that it should be neither. That is, “BDA” is just a means towards the normal business end. To me, the entire point of capturing, storing, and analyzing any data (Big or Small) is to move the needle. Period. Or, if you like, consider the simple diagram below:

How many of us take the chain to the end? Or do things stop prematurely? I worry that the focus on either analytics or Big Data is misplaced. They are all merely means to the traditional business ends: increasing sales, decreasing expenses, etc.

I’ve written thousands of reports in my consulting career and, lamentably, far too many of my clients would want the report for the sake of wanting the report. I can recall several occasions in which I’ve stumped my clients by asking a simple question like, “What do you do with this information?”

Simon Says: Don’t Forget the Endgame

I have no doubt that the analytics available from unstructured data can augment our understanding of customers, users, employees, and just about everyone else. At the same time, though, data for the sake of data is meaningless. Consider two organizations, A and B. The former effectively utilizes Small Data and routinely makes decisions based on analysis, tested hypothesis, and fact. The latter doesn’t touch the vast troves of data at its disposal–both big and small.

All else equal, I’ll bet on Organization A any day of the week and twice on Sunday.

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Category: Information Value
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by: Phil Simon
09  Mar  2013

A Key Data Management Lesson from Amazon

Few companies do data management better than Amazon–and I’m not just talking about their internal practices. Regardless of how well the company’s analytic systems generate über-accurate recommendations, it’s not perfect. Nor, for that matter, is it a substitute for human intuition.

To the Amazon’s credit, it recognizes the inherent limitations of relying exclusively upon its sophisticated algorithms and machines. Why not let customers refine, customize, and even remove their own recommendations, à la Netflix? In fact, Amazon does just that. Just look at the image below:

When perusing books, Amazon lets each customer override its own algorithm-generated recommendations. In this case, I can tell Amazon that I have no desire to read Guy Kawasaki’s book. (This was random. I have no bone to pick with Apple’s former chief evangelizer.)

It’s evident to me that machines can spawn remarkable recommendations. Collaborative filtering is nothing short of amazing–and I’m more than willing to consider Netflix gentle suggestions. But organizations adept at Big Data realize the inherent limitations of a computer- or data-only method to data management. In fact, there are typically legitimate reasons to ignore the results of even very accurate algorithms. Brass tacks: they’re not always right.

Even mighty Google–another Big Data stalwart–isn’t batting 1.000 vis-à-vis algorithm accuracy. Consider the recent Nature.com story on Google Flu that “drastically overestimated peak flu levels.”

The Limits of Democratized Data

No one is saying that all data should be democratic. I can’t imagine allowing employees to update their own pay rates or companies letting vendors tweak their own invoices. (In fact, ERP self-service tools have been with us for more than a decade, although many organizations refuse to use them for a cauldron of reasons).

Still, it’s hard to see the downside of Amazon’s move here. After all, don’t customers know what they like better than some machine? What’s the real harm in allowing them to remove items from their search or browsing history that they have no intention of buying? I’d argue that the benefits of this type of move far exceed their costs.

Simon Says

Organizations ought to learn from the examples set by Big Data leaders such as Amazon, Apple, Facebook, and Google. No CTO, CIO, or individual employee should be so enamored with his or her algorithm or technology that common sense is ignored. As a starting point, yes, emerging technologies and fancy algorithms can do amazing things and tap into heretofore unknown insights. By the same token, though, a user-override can often improve a good but imperfect result.

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Tags: , ,
Category: Information Management
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by: Bsomich
08  Mar  2013

Weekly IM Update.

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Book Release Announcement: “Information Development Using MIKE2.0”

Have you heard? Our new book, “Information Development Using MIKE2.0” is now available for pre-order.
MIKE2.0, Method for an Integrated Knowledge Environment, is an open source delivery framework for Enterprise Information Management. It provides a comprehensive methodology (with 871 significant articles so far) that can be applied across a number of different projects within the Information Management space. While initially focused around structured data, the goal of MIKE2.0 is to provide a comprehensive methodology for any type of Information Development.

The vision for Information Development and the MIKE2.0 Methodology have been available in a collaborative, online fashion since 2006, and are now made available in print publication to a wider audience, highlighting key wiki articles, blog posts, case studies and user applications of the methodology.

Authors for the book include Andreas Rindler, Sean McClowry, Robert Hillard, and Sven Mueller, with additional credit due to Deloitte, BearingPoint and over 7,000 members and key contributors of the MIKE2.0 community. The book has been published in paperback as well as all major e-book publishing platforms.

Get Involved: To get your copy of the book, visit our order page on Amazon.com. For more information on MIKE2.0 or how to get involved with our online community, please visit www.openmethodology.org.
Reviews Welcome! If you are interested in writing a review of the book, we would be happy to provide you with a free copy. Please contact mike2@openmethodology.org.
Sincerely,

MIKE2.0 Community 

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This Week’s Blogs for Thought:

Big Data: Intuition and Analysis
More than most people, I wonder about our relationship with data and technology. More and more of us are almost always connected, generating and consuming ever-increasing amounts of data. But do we use this data as much as we should? And when should we shut off intuition and start analyzing?

Read more.
The “Open MIKE” Podcast: Episode 12: Information Development Using MIKE2.0

We’ve just released the 12th episode of our Open MIKE Podcast series!

Episode 12: “Information Development Using MIKE2.0” features content from our new book and key aspects of the following MIKE2.0 articles:

Check it out.

For social networks, volume is the enemy of value

Information is a valuable asset and companies increasingly place great store in identifying new sources of data about their products and customers.

Individually, we are also quickly assembling a mass of personal information through our social networks. Professionally, the most popular social network is LinkedIn.

When LinkedIn launched a decade ago we enthusiastically started to build our portfolio of contacts. Each contact has a value to us in our career. While that value is intangible it motivates us to maintain the contact as a relationship that enhances our network.

However the volume of our contacts is starting to become overwhelming. In many cases the accepted invitations can be counted in the thousands. Projecting forward a decade, it is easy to see that many of us could be facing a set of contacts in the tens of thousands.
Read more.

Category: Information Development
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by: Bsomich
02  Mar  2013

The “Open MIKE” Podcast: Episode 12 – Information Development Using MIKE2.0

We’ve just released the 12th episode of our Open MIKE Podcast series!

Episode 12: “Information Development Using MIKE2.0” features content from our new book and key aspects of the following MIKE2.0 articles:

Check it out:



Pre-order your copy of ”Information Development Using MIKE2.0online today.

Want to get involved? Step up to the “MIKE”

We kindly invite any existing MIKE contributors to contact us if they’d like to contribute any audio or video segments for future episodes.

On Twitter? Contribute and follow the discussion via the #MIKEPodcast hashtag.

You can also find the videos and blog post summaries for every episode of the Open MIKE Podcast at: ocdqblog.com/MIKE

 

Category: Information Development
1 Comment »

by: Phil Simon
02  Mar  2013

Big Data: Intuition and Analysis

More than most people, I wonder about our relationship with data and technology. More and more of us are almost always connected, generating and consuming ever-increasing amounts of data. But do we use this data as much as we should? And when should we shut off intuition and start analyzing?

Those questions were on my mind as I read this recent Forbes’ article. From the piece:

Recently, the Corporate Executive Board developed a tool it calls Insight IQ and used it to assess the tendency of managers to rely on intuition versus analysis.  They found that 19% of over 5,000 managers in major global corporations are “Visceral decision makers” that rely almost exclusively on intuition.  (I suspect this figure is actually too low, based on other research and questions I have about the validity of the test.  But let’s not get hung up on this point; there is far more to be gleaned from this field of inquiry.)  Insight IQ proceeds to split roughly in half the remaining managers between “Unquestioning empiricists” who rely entirely on analysis and “Informed skeptics” (clearly the right answer to the test) who find some way to balance intuition and analysis.

Fellow MIKE2.0 blogger Jim Harris made a similar point. Most learned folks know (I’d hope, at least) that there are times to use both intuition and data. That is, you can’t get away with relying exclusively on your gut–nor is data the answer to all problems.

The Benefits of Intuition

Intuition is great as a starting point for solving big problems. For instance, in my years consulting on large-scale IT projects, often business issues would manifest themselves. Sure, sometimes the resolution involved merely the click of a box or the reversal of a journal entry. More often than not, though, thornier issues required a starting point–aka, a working theory or an initial hypothesis.

Equipped with that working theory, I would take the logical next step: to test it. At this point, data and analysis became invaluable. How else would you prove (or disprove) your hypothesis? Test environments were especially useful since enterprise systems rife with business rules (and software bugs) meant that isolating cause and effect could take time.

Without intuition, many problems would not have been solved. I can think of one woman with whom I worked who lacked knowledge of the system we were implementing. When a problem arose, she didn’t know where to start. Her intentions were benign, but she just couldn’t help. (It didn’t help that she lacked data analysis skills to boot.)

The Limits of Intuition

A few years later, I worked on an extremely complex data issue for a large healthcare organization. (The details of this project aren’t terribly relevant here. Suffice it to say that intuition could only get me so far.) Very quickly, I realized that the problem would require 90 percent analysis, but that ten percent for intuition served me well. Some very expensive bigwig consultants on the project had neither the data nor the intuitive skills to assist the client, and they wound up doing more harm than good.

Simon Says: It’s Not a Binary

Data–especially the big kind–can complement our understanding of events and trends, especially when used right. However, make no mistake: data does in no way supplant the need for intuition.

Maybe in 20 years a Terminator-like device will make us obsolete, but were a long way from that. In the meantime, ensure that your employees understand the benefits of Big Data, the limitations of intuition, and when to use each.

Feedback

What say you?

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Category: Information Development
1 Comment »

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