During this episode, I discuss decision management with James Taylor, author of the new book Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics.

James Taylor is the CEO of Decision Management Solutions, and the leading expert in Decision Management Systems, which are active participants in improving business results by applying business rules, predictive analytics, and optimization technologies to address the toughest issues facing businesses today, and changing the way organizations are doing business.

James Taylor has led Decision Management efforts for leading companies in insurance, banking, health management, and telecommunications.  Decision Management Solutions works with clients to improve their business by applying analytics and business rules technology to automate and improve decisions.  Clients range from start-ups and software companies to major North American insurers, a travel company, the health management division of a major healthcare company, one of Europe’s largest banks, and several major decision management technology vendors.

Decision Management Systems

Additional listening options:

• Click here to Download the MP3 file for this OCDQ Radio episode

• Click here to Subscribe to OCDQ Radio via iTunes

• Click here to Subscribe to OCDQ Radio via its RSS feed (non iTunes)

• Click here to Listen to OCDQ Radio on your Mobile with Stitcher SmartRadio

• Click here to Browse the OCDQ Radio Archives and Schedule

Win a copy of the Book

James Taylor wants to give one OCDQ Radio listener a free copy of Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics

Here is how the book contest will work:

(1) Book Contest Question — Name at least one of the four principles of decision management systems that was described by James Taylor during this OCDQ Radio episode.

(2) Book Contest Deadline — By or before February 29, 2012, Email Jim Harris with your answer to the book contest question.

(3) Book Contest Winner — In March 2012, one winner will be randomly selected from the emails containing the correct answer to the contest question, and James Taylor (or his publisher) will email the winner requesting a shipping address for the book.

Related Posts

Dot Collectors and Dot Connectors

Bayesian Data-Driven Decision Making

The Speed of Decision

The Big Data Collider

Decision-Driven Data Management

Satisficing Data Quality

The Data that Supported the Decision

Related OCDQ Radio Episodes

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

• Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt on Data Quality and Business Intelligence

• Big Data and Big Analytics — Special Guests Jill Dyché and Dan Soceanu on big trends in Business Intelligence

• Making EIM Work for Business — Guest John Ladley discusses his book: Making EIM Work for Business

Tweet

Appending the phrase “as a Service” (AAS) to almost every word (e.g., Software, Platform, Infrastructure, Data, Analytics) has become increasing prevalent due to the world-wide-webification of IT by cloud computing and other consumerization trends.

Rick Blaisdell recently blogged about the benefits of the cloud, which include fully featured services, monthly subscription costs, 24/7 support, high availability, and financially-backed service level agreements.  “Look at the cloud,” Blaisdell recommended, “as a logical extension of your IT capabilities, and take advantage of all the benefits of cloud services.”

Judy Redman has blogged about how cloud computing is one of three IT delivery trends (along with agile development and composite applications) that are allowing IT leaders to reduce costs, deliver better applications faster, and provide results that are more aligned with, and more responsive to, the business.

And with more existing applications migrating to the cloud, it is all too easy to ponder whether these services raining down from the cloud forecast the end of the reign of the centralized IT department — and, perhaps by extension, the end of the reign of the traditional IT vendor that remains off-premises-resistant (i.e., vendors continuing to exclusively sell on-premises solutions, which they positively call enterprise-class solutions, but their customers often come to negatively call legacy applications).

However, “cloud (or public cloud at least) is not the only enabler,” Adrian Bridgwater recently blogged, explaining how a converged infrastructure acknowledges that “existing systems need to be consolidated and brought into line in a harmonious, interconnected, and interoperable way.  This is where private clouds (and/or a mix of hybrid clouds) come to the fore and a firm manages its own internal systems in a hyper-efficient manner.  From this point, we see IT infrastructure working to a) save money, b) run parallel with strategic business objectives for profit and growth, and c) become a business enabler in its own right.”

No matter how much of it is cloud-oriented (or public/private clouded), the future of IT is definitely going to be service-oriented.

Now, of course, the role of IT has always been to deliver to the enterprise a fast and agile business-enabling service.  But perhaps what is refreshingly new about the unrelenting “as a Service” trend is that it reminds the IT department of their prime directive, and it enables the enterprise to deliver to the IT industry as a whole a (sometimes sorely needed) Swift Kick in the AAS.

This blog post is sponsored by the Enterprise CIO Forum and HP.

Related Posts

Can Enterprise-Class Solutions Ever Deliver ROI?

Why does the sun never set on legacy applications?

Are Applications the La Brea Tar Pits for Data?

The Cloud Security Paradox

Are Cloud Providers the Bounty Hunters of IT?

The Partly Cloudy CIO

Shadow IT and the New Prometheus

The IT Consumerization Conundrum

The IT Prime Directive of Business First Contact

The IT Pendulum and the Federated Future of IT

Tweet

The recent #GartnerChat on Big Data was an excellent Twitter discussion about what I often refer to as the Seven Letter Tsunami of the data management industry, which as Gartner Research explains, although the term acknowledges the exponential growth, availability, and use of information in today’s data-rich landscape, big data is about more than just data volume.  Data variety (i.e., structured, semi-structured, and unstructured data, as well as other types, such as the sensor data emanating from the Internet of Things), and data velocity (i.e., how fast data is produced and how fast data must be processed to meet demand) are also key characteristics of the big challenges associated with the big buzzword that big data has become over the last year.

Since ours is an industry infatuated with buzzwords, Timo Elliott remarked “new terms arise because of new technology, not new business problems.  Big Data came from a need to name Hadoop [and other technologies now being relentlessly marketed as big data solutions], so anybody using big data to refer to business problems is quickly going to tie themselves in definitional knots.”

To which Mark Troester responded, “the hype of Hadoop is driving pressure on people to keep everything — but they ignore the difficulty in managing it.”  John Haddad then quipped that “big data is a hoarders dream,” which prompted Andy Bitterer to coin the term HoardaByte for measuring big data, and then asking, “Would the real Big Data Lebowski please stand up?”

HoardaBytes

Although it’s probably no surprise that a blogger with obsessive-compulsive in the title of his blog would like Bitterer’s new term, the fact is that whether you choose to measure it in terabytes, petabytes, exabytes, HoardaBytes, or how much reality bitterly bites, our organizations have been compulsively hoarding data for a long time.

And with silos replicating data as well as new data, and new types of data, being created and stored on a daily basis, managing all of the data is not only becoming impractical, but because we are too busy with the activity of trying to manage all of it, we are hoarding countless bytes of data without evaluating data usage, gathering data requirements, or planning for data archival.

The Big Data Lebowski

In The Big Lebowski, Jeff Lebowski (“The Dude”) is, in a classic data quality blunder caused by matching on person name only, mistakenly identified as millionaire Jeffrey Lebowski (“The Big Lebowski”) in an eccentric plot expected from a Coen brothers film, which, since its release in the late 1990s, has become a cult classic and inspired a religious following known as Dudeism.

Historically, a big part of the problem in our industry has been the fact that the word “data” is prevalent in the names we have given industry disciplines and enterprise information initiatives.  For example, data architecture, data quality, data integration, data migration, data warehousing, master data management, and data governance — to name but a few.

However, all this achieved was to perpetuate the mistaken identification of data management as an esoteric technical activity that played little more than a minor, supporting, and often uncredited, role within the business activities of our organizations.

But since the late 1990s, there has been a shift in the perception of data.  The real data deluge has not been the rising volume, variety, and velocity of data, but instead the rising awareness of the big impact that data has on nearly every aspect of our professional and personal lives.  In this brave new data world, companies like Google and Facebook have built business empires mostly out of our own personal data, which is why, like it or not, as individuals, we must accept that we are all data geeks now.

All of the hype about Big Data is missing the point.  The reality is that Data is Big — meaning that data has now so thoroughly pervaded mainstream culture that data has gone beyond being just a cult classic for the data management profession, and is now inspiring an almost religious following that we could call Dataism.

The Data must Abide

“The Dude abides.  I don’t know about you, but I take comfort in that,” remarked The Stranger in The Big Lebowski.

The Data must also abide.  And the Data must abide both the Business and the Individual.  The Data abides the Business if data proves useful to our business activities.  The Data abides the Individual if data protects the privacy of our personal activities.

The Data abides.  I don’t know about you, but I would take more comfort in that than in any solutions The Stranger Salesperson wants to sell me that utilize an eccentric sales pitch involving HoardaBytes and the Big Data Lebowski.

Related Posts

Big Data el Memorioso

Dot Collectors and Dot Connectors

OCDQ Radio - Big Data and Big Analytics

DQ-View: Data Is as Data Does

OCDQ Radio - So Long 2011, and Thanks for All the . . .

Neither the I Nor the T is Magic

Information Overload Revisited

The Big Data Collider

The Speed of Decision

The Data-Decision Symphony

A Decision Needle in a Data Haystack

OCDQ Radio - Good-Enough Data for Fast-Enough Decisions

Tweet

The Johari Window is a term from psychology for a technique used to help people better understand their personality and behavior by combining a self assessment with assessments from their peers.  In relation to data, the Johari Window is a metaphor for 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.

During this episode, I discuss the Johari Window of Data Quality with Martin Doyle.  Our discussion, inspired by our blog comment banter on my post There is No Such Thing as a Root Cause, includes root cause analysis, the pursuit of data perfection, metadata, communication, Business-IT collaboration, change management, defect prevention, and continuous improvement.

Martin Doyle is a Data Quality Improvement Evangelist and the CEO of DQ Global, which is a UK-based data quality software and services vendor providing data cleansing, international address and email verification, data deduplication, and data matching solutions for Customer Relationship Management, Single Customer View, and Master Data Management.  DQ Global has worked with over 500 businesses worldwide on a variety of projects, providing their clients with improved data quality, making their data fit for business use, and enabling them to trust their data and make decisions based on a foundation of fact.

The Johari Window of Data Quality

Additional listening options:

• Click here to Download the MP3 file for this OCDQ Radio episode

• Click here to Subscribe to OCDQ Radio via iTunes

• Click here to Subscribe to OCDQ Radio via its RSS feed (non iTunes)

• Click here to Listen to OCDQ Radio on your Mobile with Stitcher SmartRadio

• Click here to Browse the OCDQ Radio Archives and Schedule

Related Posts

There is No Such Thing as a Root Cause

The Dichotomy Paradox, Data Quality and Zero Defects

The Asymptote of Data Quality

To Our Data Perfectionists

DQ-View: The Cassandra Effect

The Data Quality Wager

DQ-View: Data Is as Data Does

Selling the Business Benefits of Data Quality

Related OCDQ Radio Episodes

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

• Redefining Data Quality — Guest Peter Perera discusses his proposed redefinition of data quality

• The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside

• Studying Data Quality — Guest Gordon Hamilton on the key concepts from recommended Data Quality books

• Organizing for Data Quality — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality best practices

Tweet

If you are having trouble viewing this video, then you can watch it on Vimeo by clicking on this link: DQ-View on Vimeo

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

Related Posts

You Say Potato and I Say Tater Tot

The Metadata Continuum

The Metadata Crisis

Listen to an OCDQ Radio episode about how we are now living in a world where everyone is a Data Geek

What’s the Meta with your Data?

Let’s Meta a Data

Listen to Peter Benson discuss Metadata, Data, and Information on the Knights of the Data Roundtable

Data Quality Music (DQ-Songs)

In other words, the following links are to lyrical data quality blog posts inspired by music:

Data Quality Mondegreens

Council Data Governance

Over the Data Governance Rainbow

I’m Gonna Data Profile (500 Records)

A Record Named Duplicate

New Time Human Business

People

You Can’t Always Get the Data You Want

A spoonful of sugar helps the number of data defects go down

Data Quality is such a Rush

I’m Bringing DQ Sexy Back

Imagining the Future of Data Quality

The Very Model of a Modern DQ General

Tweet

Funes el memorioso is a short story by Jorge Luis Borges, which describes a young man named Ireneo Funes who, as a result of a horseback riding accident, has lost his ability to forget.  Although Funes has a tremendous memory, he is so lost in the details of everything he knows that he is unable to convert the information into knowledge and unable, as a result, to achieve wisdom.

In Spanish, the word memorioso means “having a vast memory.”  Without question, Big Data has a vast memory comprised of fast-moving large volumes of varying data seemingly providing details about everything your organization could ever want to know about our increasingly digitized and pixelated world.  But what if Big Data is the Ireneo Funes of the Information Age?

What if Big Data el Memorioso is the not-so-short story in which your organization becomes so lost in the details of everything big data delivers that you’re unable to connect enough of the dots to convert the information into knowledge and unable, as a result, to achieve the wisdom necessary to satisfice specific business needs?

Adrian Bridgwater recently compared this challenge to “trying to balance a stack of papers on a moving walkway, in a breeze, without knowing the full length or speed of the walkway itself.  If you want to extend the metaphor one step further — there are other passengers on our walkway and they could bump into us and/or add papers to our stack.  Oh, did I mention that the pieces of paper might not even all be the same size, shape, or color — and some may have tattered edges and coffee stains?”

In other words, as Bridgwater went on to explain, “our information optimization goals will typically include the need to manage information and assess its quantitative and qualitative values.  We will also need to analyze streams of both structured and unstructured data, the latter including video, emails, and other less ‘straight edged’ data.”

While examining some of the technology options that can assist with this challenge, Paul Muller recently remarked “whether it be structured, unstructured, big, small, real-time, or historical — data of all kinds are top-of-mind for business executives.  It may already feel like you’re drowning in data, but it’s important to get to grips with the changing technology landscape to ensure you’re not drowning in an incoherent mess of information management architectures too.”

Edd Dumbill recently wrote an introduction to the big data landscape, which concluded that “big data is no panacea.  You can find patterns and clues in your data, but then what?”  As Dumbill recommends, you need to know where you want to go.  You need to know what problem you want to solve, i.e., you need to pick a real business problem to guide your implementation.

Without this implementation guide, big data will have, as Borges said of Funes, “a certain stammering greatness,” but amount to, as William Shakespeare said in The Tragedy of Macbeth, “a tale told by an idiot, full of sound and fury, signifying nothing.”

This blog post is sponsored by the Enterprise CIO Forum and HP.

Related Posts

Neither the I Nor the T is Magic

Information Overload Revisited

The Speed of Decision

The Data-Decision Symphony

A Decision Needle in a Data Haystack

The Big Data Collider

Dot Collectors and Dot Connectors

DQ-View: Data Is as Data Does

Data, Information, and Knowledge Management

Is your data complete and accurate, but useless to your business?

The Real Data Value is Business Insight

Data, data everywhere, but where is data quality?

Tweet

The attention blindness inherent in the digital age often leads to a debate about multitasking, which many claim impairs our ability to solve complex problems.  Therefore, we often hear that we need to adopt monotasking, i.e., we need to eliminate all possible distractions and focus our attention on only one task at a time.

However, during the recent Harvard Business Review podcast The Myth of Monotasking, Cathy Davidson, author of the new book Now You See It: How the Brain Science of Attention Will Transform the Way We Live, Work, and Learn, explained how “the moment that you start not paying attention fully to the task at hand, you actually start seeing other things that your attention would have missed.”  Although Davidson acknowledges that attention blindness is a serious problem, she explained that there really is no such thing as monotasking.  Modern neuroscience research has revealed that the human brain is, in fact, always multitasking.  Furthermore, she explained how multitasking can be extremely useful for a new and expansive form of attention.

“We all see selectively, but we don’t select the same things to see,” Davidson explained.  “So if we can learn to work together, we can actually account for, and productively work around, our own individual attention blindness by seeing collaboratively in a way that compensates for that blindness.”

During the podcast, an analogy was made that focusing attention on specific tasks can result in a lot of time spent collecting dots without spending enough time connecting those dots.  This point caused me to ponder the division of organizational labor that has historically existed between the dot collection of data management, which focuses on aspects such as data integrity and data quality, and the dot connection of business intelligence, which focuses on aspects such as data analysis and data visualization.

I think most data management professionals are dot collectors since it often seems like they spend a lot of their time, money, and attention on collecting (and profiling, modeling, cleansing, transforming, matching, and otherwise managing) data dots.

But since data’s value comes from data’s usefulness, merely collecting data dots doesn’t mean anything if you cannot connect those dots into meaningful patterns that enable your organization to take action or otherwise support your business activities.

So I think most business intelligence professionals are dot connectors since it often seems like they spend a lot of their time, money, and attention on connecting (and querying, aggregating, reporting, visualizing, and otherwise analyzing) data dots.

However, the attention blindness of data management and business intelligence professionals means that they see selectively, often intentionally selecting to not see the same things.  But as more of our personal and professional lives become digitized and pixelated, the big picture of the business world is inundated with the multifaceted challenges of big data, where the fast-moving large volumes of varying data are transforming the way we have to view traditional data management and business intelligence.

We need to replace our perspective of data management and business intelligence as separate monotasking activities with an expansive form of organizational multitasking where the dot collectors and dot connectors work together more collaboratively.

Related Posts

Channeling My Inner Beagle: The Case for Hyperactivity

Mind the Gap

The Wisdom of the Social Media Crowd

No Datum is an Island of Serendip

DQ-View: Data Is as Data Does

The Real Data Value is Business Insight

Information Overload Revisited

Neither the I Nor the T is Magic

The Big Data Collider

OCDQ Radio - Big Data and Big Analytics

OCDQ Radio - So Long 2011, and Thanks for All the . . .

The Interconnected User Interface

Tweet

During this episode, recorded on the first of three occurrences of Friday the 13th in 2012, I discuss scary calendar effects.

In other words, I discuss how schedules, deadlines, and other date-related aspects can negatively affect enterprise initiatives such as data quality, master data management, and data governance.

Please Beware: This episode concludes with the OCDQ Radio Theater production of Data Quality and Friday the 13th.

Scary Calendar Effects

Additional listening options:

• Click here to Download the MP3 file for this OCDQ Radio episode

• Click here to Subscribe to OCDQ Radio via iTunes

• Click here to Subscribe to OCDQ Radio via its RSS feed (non iTunes)

• Click here to Listen to OCDQ Radio on your Mobile with Stitcher SmartRadio

• Click here to Browse the OCDQ Radio Archives and Schedule

Related Posts

Data Quality and #FollowFriday the 13th

The Moirae, Deadlines and Working within Limits

The Fiscal Calendar Effect

Eternal September and Tacit Knowledge

“What is is the was of what shall be”

Popular OCDQ Radio Episodes

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

• So Long 2011, and Thanks for All the . . . — Don’t Panic! This is the OCDQ Radio 2011 Year in Review episode

• The Fall Back Recap Show — Celebrating the autumnal equinox with a look back at the Best of OCDQ Radio

• The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside

• Redefining Data Quality — Guest Peter Perera discusses his proposed redefinition of data quality

• Studying Data Quality — Guest Gordon Hamilton on the key concepts from recommended Data Quality books

Tweet

In her recent Data Quality Pro Interview, Jill Dyché explained a common misconception, namely that a data governance framework is not a strategy.  “Unlike other strategic initiatives that involve IT,” Jill explained, “data governance needs to be designed.  The cultural factors, the workflow factors, the organizational structure, the ownership, the political factors, all need to be accounted for when you are designing a data governance roadmap.”

“People need a mental model, that is why everybody loves frameworks,” Jill continued.  “But they are not enough and I think the mistake that people make is that once they see a framework, rather than understanding its relevance to their organization, they will just adapt it and plaster it up on the whiteboard and show executives without any kind of context.  So they are already defeating the purpose of data governance, which is to make it work within the context of your business problems, not just have some kind of mental model that everybody can agree on, but is not really the basis for execution.”

“So it’s a really, really dangerous trend,” Jill cautioned, “that we see where people equate strategy with framework because strategy is really a series of collected actions that result in some execution — and that is exactly what data governance is.”

And in her excellent article Data Governance Next Practices: The 5 + 2 Model, Jill explained that data governance requires a deliberate design so that the entire organization can buy into a realistic execution plan, not just a sound bite.  As usual, I agree with Jill, since, in my experience, many people expect a data governance framework to provide eureka-like moments of insight.

In The Myths of Innovation, Scott Berkun debunked the myth of the eureka moment using the metaphor of a jigsaw puzzle.

“When you put the last piece into place, is there anything special about that last piece or what you were wearing when you put it in?” Berkun asked.  “The only reason that last piece is significant is because of the other pieces you’d already put into place.  If you jumbled up the pieces a second time, any one of them could turn out to be the last, magical piece.”

“The magic feeling at the moment of insight, when the last piece falls into place,” Berkun explained, “is the reward for many hours (or years) of investment coming together.  In comparison to the simple action of fitting the puzzle piece into place, we feel the larger collective payoff of hundreds of pieces’ worth of work.”

Perhaps the myth of the data governance framework could also be debunked using the metaphor of a jigsaw puzzle.

Data governance requires the coordination of a complex combination of a myriad of factors, including executive sponsorship, funding, decision rights, arbitration of conflicting priorities, policy definition, policy implementation, data quality remediation, data stewardship, business process optimization, technology enablement, change management — and many other puzzle pieces.

How could a data governance framework possibly predict how you will assemble the puzzle pieces?  Or how the puzzle pieces will fit together within your unique corporate culture?  Or which of the many aspects of data governance will turn out to be the last (or even the first) piece of the puzzle to fall into place in your organization?  And, of course, there is truly no last piece of the puzzle, since data governance is an ongoing program because the business world constantly gets jumbled up by change.

So, data governance frameworks are useful, but only if you realize that data governance frameworks are like jigsaw puzzles.

Related Posts

Listen to Jill Dyché discuss Data Governance on the Knights of the Data Roundtable

The Three Most Important Letters in Data Governance

Data Governance and the Adjacent Possible

Aristotle, Data Governance, and Lead Rulers

Data Governance Star Wars: Balancing Bureaucracy And Agility

OCDQ Radio - Data Governance Star Wars

The Stakeholder’s Dilemma

The Collaborative Culture of Data Governance

The Big Data Collider

No Datum is an Island of Serendip

Are your Best Practices R.I.P.?

The Dumb and Dumber Guide to Data Quality

Tweet

If you are having trouble viewing this video, then you can watch it on Vimeo by clicking on this link: DQ-View on Vimeo

The following list contains the books shown in the video, simply listed in the order they appeared on my book shelf:

• Customer Data Integration: Reaching a Single Version of the Truth by Jill Dyché and Evan Levy

• Master Data Management by David Loshin

• Master Data Management in Practice: Achieving True Customer MDM by Dalton Cervo and Mark Allen, who discussed their book on OCDQ Radio: Master Data Management in Practice

• Master Data Management and Data Governance (Second Edition) by Alex Berson and Larry Dubov

• The Data Governance Imperative by Steve Sarsfield

• The Data Asset: How Smart Companies Govern Their Data for Business Success by Tony Fisher

• Making EIM Work for Business by John Ladley, who discussed his book on OCDQ Radio: Making EIM Work for Business

• Data Driven: Profiting from Your Most Important Business Asset by Thomas Redman

• Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information by Danette McGilvray

• The Practitioner’s Guide to Data Quality Improvement by David Loshin

• Data Quality and Record Linkage Techniques by Thomas Herzog, Fritz Scheuren, and William Winkler

• Entity Resolution and Information Quality by John Talburt

• 101 Lightbulb Moments in Data Management: Tales from the Data Roundtable by Jill Dyché, Jim Harris, Dylan Jones, David Loshin, Joyce Norris-Montanari, Rich Murnane, and Phil Simon

Previous DQ-View Videos

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

DQ-View: Baseball and Data Quality

DQ-View: Occam’s Razor Burn

DQ-View: Roman Ruts on the Road to Data Governance

DQ-View: Talking about Data

DQ-View: The Poor Data Quality Blizzard

DQ-View: New Data Resolutions

DQ-View: From Data to Decision

DQ View: Achieving Data Quality Happiness

Data Quality is not a Magic Trick

DQ-View: The Cassandra Effect

DQ-View: Is Data Quality the Sun?

DQ-View: Designated Asker of Stupid Questions

Video: Oh, the Data You’ll Show!

Tweet