Open Framework, Information Management Strategy & Collaborative Governance | Data & Social Methodology - MIKE2.0 Methodology
Collapse Expand Close

To join, please contact us.

Improve MIKE 2.0
Collapse Expand Close
Need somewhere to start? How about the most wanted pages; or the pages we know need more work; or even the stub that somebody else has started, but hasn't been able to finish. Or create a ticket for any issues you have found.

Archive for February, 2012

by: Bsomich
23  Feb  2012

Profile Spotlight: Jans Aasman

Jans Aasman

Dr. Jans Aasman is CEO and President of Franz, a semantic web and enterprise technology solution provider.  He started his career as an experimental and cognitive psychologist, earning his PhD in cognitive science with a detailed model of car driver behavior using Lisp and Soar.

Aasman has spent most of his professional life in telecommunications research, specializing in intelligent user interfaces and applied artificial intelligence projects. From 1995 to 2004, he was also a part-time professor in the Industrial Design department of the Technical University of Delft. Jans is currently the CEO of Franz Inc., the leading supplier of commercial, persistent, and scalable RDF database products that provide the storage layer for powerful reasoning and ontology modeling capabilities for Semantic Web applications.

Dr. Aasman has gained notoriety as a conference speaker at such events as Semantic Technologies Conference, International Semantic Web Conference, Java One, Linked Data Planet, INSA, GeoWeb, ICSC, RuleML and DEBS.  He is also one of 15 CEOs interviewed in a new book, “Startup Best Practices”.

Connect with Jans.

Category: Information Development
No Comments »

by: Phil Simon
22  Feb  2012

Permission and Forgiveness

All too often on information management (IM) projects, the best laid plans go awry. Despite copiously followed methodologies, extensive planning sessions, and a bevy of high-priced consultants, results frequently do not meet expectations.


Well, there are many reasons, but ultimately interpersonal communication is sorely lacking. In this post, I’ll discuss how these communication issues can cause a data nightmare to remember.

A Case in Point

Two years ago, I was managing data conversions for a large IM project at a regional hospital in New Jersey. I had built a Microsoft Access database with a number of involved automated routines that imported legacy data, mapped it new values, and generated upload files for the new ERP application.

The database took months to develop and, because of the complexity and number of different legacy data sources, it wasn’t exactly intuitive to the layperson. Complicated ETL programs are that way because the data is often, well, complicated. I had created a number of temp tables, queries, and macros that would properly format employee and vendor data, taking into account myriad undocumented rules.

Well, the ETL tool worked, until it didn’t.

One Monday morning, I loaded tens of thousands of what should have been accurate records into the test environment. A few hours later, a few people came to me with questions. They were seeing errant data and wanted to know what I had done wrong. Since I wasn’t senile yet, I had the same question. After all, my ETL program worked fine when I left on Friday.

I did some digging, asking a few people in my general work area if they had noodled with anything in my database. It turns out that one user (Kathy – not her real name) had in fact tweaked the Access database, changing an important date field from which other dates were derived.


I asked Kathy why she did this. Her response was priceless, “Why? Is it a big deal?”

The short answer was yes. It was a big deal. A really big deal. All of the data loaded into the test environment had to be purged and testing (already well behind schedule) was delayed even more. Fortunately, the team and I were able to undo her changes and resume testing a few days later. I then locked the database down to prevent future tampering.

Simon Says

It’s always better to ask permission rather than forgiveness on IM projects, especially when making changes that can affect hundred thousands of records. Adding an individual vendor or pay check or sale is one thing; loading five years’ worth of data is another.

These days, everyone is reachable. Pick up the phone and ask someone if you have a question before potentially affecting everyone’s data.


What say you?

Category: Information Management

by: Robert.hillard
19  Feb  2012

Technology gardening

There is little that is guaranteed to soothe the stressed mind as much as a well-structured garden.  It brings together order and nature in a magic combination.  From a distance, the garden follows a clear plan that has probably been laid out by a landscape architect.  Up close, each garden bed logically leads to the next.  A good garden tells a story, describes its own purpose and combines aesthetics with function.

While the information technology profession often uses the building metaphor for its projects perhaps gardens might be more appropriate.  Where a building architect is operating to a clear project plan with a beginning, middle and end, the landscape architect may have a vision for an end but it will be many years before it can be fully realised with a long evolution along the way.  Before realising the landscape architect’s vision, the goals of the garden are almost certain to have changed in some way and the gardeners who are charged with planting, pruning and maintaining the garden will morph the plans as they learn more about what thrives in different part s of the garden and observe the preferences of the users of the garden (the public or the individual owners).

Perhaps the most frustrated members of the technology team are the architects.  They set standards and try to impose discipline across the enterprise.  They often feel like they have made a breakthrough with everyone agreeing to very sensible principles at the start of projects, such as adopting just one set of business intelligence tools, adhering to integration standards or consolidating all online activity through a single platform.  Unfortunately real world complexity conspires to cause the project team to rapidly break these agreements.

There is no doubt that information technology is the foundation of modern business.  With something so critical, compromising quality should not be an option.  It might be expected that senior executives would be prepared to invest and plan ahead.  Similarly, project managers are engaged with a goal in mind and yet are often forced to abandon the plans laid out by the very same information technology architects that they themselves engaged.

Increasingly organisations are looking to adopt more evolutionary approaches to technology projects.  The methods often encourage a high level outline of the project’s goals and then experiment or test different approaches in a series of structured mini-projects or “sprints”.  Perhaps adopting the gardening metaphor will lead information technology strategists to evolve the entire technology landscape across the enterprise leveraging both the techniques of agile methods combined with the principles of developing a good garden.

An enterprise approach to technology which adopts landscape gardening principles might think of vendor choices not in terms of a single standard but rather what will suit the needs of one area while retaining the aesthetic or integration needs of the whole landscape.  Decisions about system priorities might be expected to change over time as the business moves through its natural cycles, just as gardeners change their focus as the environment moves through good and bad seasons.

More than anything else, though, an organisation adopting the garden metaphor would embrace rather than fear user empowerment.  These organisations would seek to plant many technology seeds to find out what grows best and then be willing to prune or remove some of newly grown systems to keep the overall landscape in line with the vision.  In this model, users focused on their own little patch will create fertile or barren ground without putting the enterprise goals at risk.  Not everyone needs to understand the garden as a whole in order to be able to meaningfully contribute to an individual plant or tree.

Tags: ,
Category: Enterprise2.0

by: Bsomich
17  Feb  2012

Weekly IM Update.


What is an Open Methodology Framework?

An Open Methodology Framework is a collaborative environment for building methods to solve complex issues impacting business, technology, and society.  The best methodologies provide repeatable approaches on how to do things well based on established techniques. MIKE2.0′s Open Methodology Framework goes beyond the standards, techniques and best practices common to most methodologies with three objectives:

  • To Encourage Collaborative User Engagement
  • To Provide a Framework for Innovation
  • To Balance Release Stability with Continuous Improvement

We believe that this approach provides a successful framework accomplishing things in a better and collaborative fashion. What’s more, this approach allows for concurrent focus on both method and detailed technology artifacts. The emphasis is on emerging areas in which current methods and technologies lack maturity.

The Open Methodology Framework will be extended over time to include other projects. Another example of an open methodology, is open-sustainability which applies many of these concepts to the area of sustainable development. Suggestions for other Open Methodology projects can be initiated on this article’s talk page.

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


MIKE2.0 Community

Popular Content

Did you know that the following wiki articles are most popular on Google? Check them out, and feel free to edit or expand them!

What is MIKE2.0?
Deliverable Templates
The 5 Phases of MIKE2.0
Overall Task List
Business Assessment Blueprint
SAFE Architecture
Information Governance Solution

Contribute to MIKE:

Start a new article, help with articles under construction or look for other ways to contribute.

Update your personal profile to advertise yourself to the community and interact with other members.

Useful Links:
Home Page
Content Model
MIKE2.0 Governance

Join Us on

Follow Us on
43 copy.jpg

 Join Us on images.jpg



This Week’s Blogs for Thought:

On Google, Privacy and Data Management

Google’s new privacy policy is causing quite the stir. In a nutshell, effective March 1st of this year, the company is going to be integrating user information from its different products and services much more extensively than before. For instance, a YouTube video watched on Manchester United might cause an add for soccer jerseys to appear in Gmail.

Read more.

Talking Data Democratization

I spoke with Elissa Fink, the Chief Marketing Officer of Tableau Software about the democratization of business intelligence and the rise of well-performing, independent tools like Tableau. Here are some highlights of that conversation.

We both remember the days when the end user had no choice but to wait weeks, months or forever for IT to deliver every small report and every change to every small report. The end user was simply not empowered whatsoever. By the time the report came, it was far less valuable than when it was asked for. Even then, there was much further processing to be done on the data as the next step was to pull the data into Excel, where the real work would begin, including data cleansing. IT would go willfully blind on this and consequently users were inconsistently, and redundantly, recreating the work of their fellow users. Information was not a weapon. It was an afterthought.

Read more.

Kahneman and Data Management

Let’s try a test.

Organization ABC has deployed top-tier enterprise software. It has hired an army of expensive consultants who advise that people should follow specific business practices designed to maximize data quality.

Contrast ABC with organization XYZ. The latter’s management never upgraded its mainframe, bought “modern” apps and, to be frank, some of its business processes are antiquated.

Based on this information, which organization manages its data better?

Read more.

Category: Information Development
No Comments »

by: Phil Simon
15  Feb  2012

On Google, Privacy, and Data Management

Google’s new privacy policy is causing quite the stir. In a nutshell, effective March 1st of this year, the company is going to be integrating user information from its different products and services much more extensively than before. For instance, a YouTube video watched on Manchester United might cause an add for soccer jerseys to appear in Gmail.

In The Age of the Platform, I write about how all of these products and services are in fact just planks on the Google platform.

Some people have a problem with this level of integration. I am not one of them, as I wrote recently. In today’s post, I’d like to discuss my non-objections from a data management perspective.

The privacy extremists question whether Google is being true to its stated corporate credo, Don’t be evil. They worry about what will happen to the information that Google collects on its users. To me, Google is merely doing what all companies should do–i.e., practice solid data management.

Google and Data Silos

Consider the following questions:

  • Would it be better for Google not to collect and integrate as much user information as possible?
  • Should Google maintain a bunch of disparate data silos?
  • Should Google Maps not talk to Google Docs or Google Music?
  • Should they all be different departments within Google?

If you’re Mark Zuckerberg or Tim Cook, then the answers to these questions are yes. You don’t want Google to succeed. If you’re a Google investor, employee, or vendor, though, the answer is an unequivocal no. Google is Google because of many reasons including brilliant employees, a cool corporate culture, and remarkable technology. But all of that wouldn’t matter if Google didn’t manage its data well.


Understanding the Hate

Many Google detractors simply envy what Google does so well–and so much better than other organizations. Yes, I’m talking about data management. Don’t think for a minute that many of these same people aren’t trying to figure out how to do exactly what Google is doing–or learn from Larry and Sergey.

No doubt that this envy extends to technology. As a web-native company, Google probably doesn’t have a great deal of spaghetti architecture running behind the scenes. 1960s mainframes? Please. Google played leapfrog and embraced the cloud long before many CIOs even heard the term.

Simon Says

If Yahoo! had the ability to manage its data as well as Google, then perhaps the dot-com leftover might be doing better that it has been over the last five years.

If you truly dislike what Google is doing as a consumer, then use Bing, Yahoo! Mail, MapQuest, and other alternatives to Google products. That’s your choice as an individual. As a company, however, it’s hard to argue with Google’s results. Dislike the company if you must, but you can’t help but admire how the company manages its data.


What say you?


Category: Information Development
1 Comment »

by: Phil Simon
10  Feb  2012

Kahneman and Data Management

Let’s try a test.

Organization ABC has deployed top-tier enterprise software. It has hired an army of expensive consultants who advise that people should follow specific business practices designed to maximize data quality.

Contrast ABC with organization XYZ. The latter’s management never upgraded its mainframe, bought “modern” apps and, to be frank, some of its business processes are antiquated.

Based on this information, which organization manages its data better?

You’d probably guess ABC, right? Why? The answer can be found in Daniel Kahneman’s new book Thinking, Fast and Slow (affiliate link). He writes about how the human brain is broken into two systems. From the book’s Amazon page:

System 1 is fast, intuitive, and emotional; System 2 is slower, more deliberative, and more logical. Kahneman exposes the extraordinary capabilities—and also the faults and biases—of fast thinking, and reveals the pervasive influence of intuitive impressions on our thoughts and behavior. The impact of loss aversion and overconfidence on corporate strategies, the difficulties of predicting what will make us happy in the future, the challenges of properly framing risks at work and at home, the profound effect of cognitive biases on everything from playing the stock market to planning the next vacation—each of these can be understood only by knowing how the two systems work together to shape our judgments and decisions.

When you read the start of this post, you were invoking System 1.

Kahneman has taken some flak from academics because he has ostensibly simplified years of research. Pay them no heed. Few people are going to read books written like dense theses rife with citations.

This notion of two systems is essential in understanding how we interpret–or fail to interpret data. In Chapter 19 of the book, he writes about how intelligence on 9/11 gathered a few months before that awful day was not reported directly to George W. Bush. Rather, that information went to Condoleeza Rice, then National Security Advisor.

Of course, hindsight is 20/20. It’s easy to point fingers because we know now what we didn’t know then. But how often is that the case?

False Causality

Systems 1 dominates most of the time, fueled by our need to understand the world as quickly as possible. Case in point: We like simple stories with tactical, repeatable instructions. If I only do these ten things, then my company will be the next Wal-Mart or Apple. Books like The Halo Effect point out the facile nature of most management texts.

(Side note: I am not being hypocritical here. One of the things of which I am most proud in my most recent book, The Age of the Platform: How Amazon, Apple, Facebook, and Google Have Redefined Business, is that I don’t provide a ten-point plan on how to be the next Google. I’m just not that smart. In fact, if launched today, I’d argue that these four companies wouldn’t be the companies they are right now. Luck and timing are huge.)

Are companies successful because their CEO practices certain management techniques? Or is the chain reversed? Ultimately, this is impossible to tell absent some experiment.

Simon Says

Many organizations mistakenly follow a me-too approach to data management. That is, they model their data, buy applications, and/or follow “best practices” like they were scripture because “successful” companies are doing the same. But successful data management is more art than science; there are only necessary conditions. Those looking for recipes are probably going to be disappointed with the results.


What say you?

Category: Information Management
No Comments »

by: Bsomich
09  Feb  2012

Feedback Request: Do you have a MIKE2.0 success story to share?

For the past few years, our team at MIKE2.0 has been actively soliciting, compiling and promoting best practices for enterprise information development. And this year, we want to hear from you!

Have you successfully used or applied MIKE2.0 concepts  in a business or educational setting? This could include using any of our open source methodologies, how-to guides, open source solutions, supporting assets, or blog advice in an effort to improve information management. 

If so, please share your experience with us in the comment section below, or email us at Community respondents will have a chance to be featured as a case study in future MIKE2.0 knowledge publications, so this is a great opportunity for exposure and to help make an impact improving enterprise information management across the globe.

Category: Information Development
1 Comment »

by: Phil Simon
01  Feb  2012

Understanding the Paradox of the Middle

Back when MySpace, AOL, and Yahoo! ruled the world, people online were not always who they appeared to be. Yes, the Internet was still shaking out, but these erstwhile titans did not exactly take pains to authenticate their users. The dot-com era rewarded eyeballs, clicks, and page views–not authenticity.

A New Era

Fast forward ten years. Those three companies are shells of their former selves. Screen names like TennisFan_69 have given way to real names at companies that understand the importance of validating user identifies. While forgeries are nearly possible to completely prevent, current tech bellwethers like Twitter, Google (via Plus), LinkedIn, and Facebook make great efforts to ensure that people are who they claim to be. (By extension, sites that use tools like Facebook Connect benefit from these authentication steps.)

The point is that millions of people can effectively manage their own identities, their own data, much better than a centralized entity or a customer service department.

This is one end of the spectrum: the democratization of data. As Clive Thompson writes in Wired, we’ve seen this era of increased transparency play out among our very eyes over the last five years, although sites like eBay and Amazon have long enabled this type of data self-service. Thanks to Google, it’s harder than ever to pretend that you’re someone else. Ask Scott Adams–or at least one of his pseudonyms.

Let’s switch gears.

Contrast the “all hands on deck approach to data management” with what many small business owners have to face. Their data tends to be extremely accurate because so few hands are touching it. While far from perfect, at least errors tend to be consistently made. Rarely in my experience are 20 different people at a company entering hours, invoices, or purchase orders in 20 different ways. Mistakes can typically be rectified in a relatively short period of time after someone understands what was done.

To sum, when millions of people touch the data, the result tends to be the same: reasonably good data.

The Middle

The problem for most organizations lies somewhere in between these two extremes. When 50 or 200 or 1,000 people touch the data, things often go awry (absent some type of data quality tool, culture of data governance, routine audits, and the like). Data is often, incomplete, inaccurate, dated, and/or duplicated.

Employees in big companies rarely make errors in consistent ways–and business rules of enterprise applications can only do so much. Yes, I can prevent someone from adding an employee with the same social number, but does the busy data entry clerk really care about data integrity when making minimum wage?

Adding to the mess is the fact that too often organizations fail to appropriately train employees. On-the-job training is, at least in my experience, sadly the norm.

Simon Says

You may allow vendors, customers, and even employees to manage their own information–or at least some of it. Of course, you can restrict access to editable data to only employees who have been properly trained and understand the consequences of their actions–and inactions.

Perhaps most important, however, understand that “the middle” represents a danger zone, a potential netherworld in which your data faces serious risk of being compromised.


What say you?


Tags: , , , ,
Category: Data Quality
No Comments »

Collapse Expand Close
TODAY: Fri, March 22, 2019
Collapse Expand Close
Recent Comments
Collapse Expand Close