Open Framework, Information Management Strategy & Collaborative Governance | Data & Social Methodology - MIKE2.0 Methodology
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Archive for July 19th, 2014

by: Ocdqblog
19  Jul  2014

Micro-Contributions form a Collaboration Macro

Collaboration is often cited as a key success factor in many enterprise information management initiatives, such as metadata managementdata quality improvementmaster data management, and information governance. Yet it’s often difficult to engage individual contributors in these efforts because everyone is busy and time is a zero-sum game. However, a successful collaboration needn’t require a major time commitment from all contributors.

While a small core group of people must be assigned as full-time contributors to enterprise information management initiatives, success hinges on a large extended group of people making what Clive Thompson calls micro-contributions. In his book Smarter Than You Think: How Technology is Changing Our Minds for the Better, he explained that “though each micro-contribution is a small grain of sand, when you get thousands or millions you quickly build a beach. Micro-contributions also diversify the knowledge pool. If anyone who’s interested can briefly help out, almost everyone does, and soon the project is tapping into broad expertise.”

Wikipedia is a great example since anyone can click on the edit tab of an article and become a contributor. “The most common edit on Wikipedia,” Thompson explained, “is someone changing a word or phrase: a teensy contribution, truly a grain of sand. Yet Wikipedia also relies on a small core of heavily involved contributors. Indeed, if you look at the number of really active contributors, the ones who make more than a hundred edits a month, there are not quite thirty-five hundred. If you drill down to the really committed folks—the administrators who deal with vandalism, among other things—there are only six or seven hundred active ones. Wikipedia contributions form a classic long-tail distribution, with a small passionate bunch at one end, followed by a line of grain-of-sand contributors that fades off over the horizon. These hardcore and lightweight contributors form a symbiotic whole. Without the micro-contributors, Wikipedia wouldn’t have grown as quickly, and it would have a much more narrow knowledge base.”

MIKE2.0 is another great example since it’s a collaborative community of information management professionals contributing their knowledge and experience. While MIKE2.0 has a small group of core contributors, micro-contributions improve the breadth and depth of its open source delivery framework for enterprise information management.

The business, data, and technical knowledge about the end-to-end process of how information is being developed and used within your organization is not known by any one individual. It is spread throughout your enterprise. A collaborative effort is needed to make sure that important details are not missed—details that determine the success or failure of your enterprise information management initiative. Therefore, be sure to tap into the distributed knowledge of your enterprise by enabling and encouraging micro-contributions. Micro-contributions form a collaboration macro. Just as a computer macro is comprised of a set of instructions that are used to collectively perform a particular task, think of collaboration as a macro that is comprised of a set of micro-contributions that collectively manage your enterprise information.


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Category: Information Development
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by: Bsomich
19  Jul  2014

Community Update.

Missed what’s been happening in the MIKE2.0 community? Check out our bi-weekly update:



Data Governance: How competent is your organization?

One of the key concepts of the MIKE2.0 Methodology is that of an Organisational Model for Information Development. This is an organisation that provides a dedicated competency for improving how information is accessed, shared, stored and integrated across the environment.

Organisational models need to be adapted as the organisation moves up the 5 Maturity Levels for organisations in relation to the Information Development competencies below:

Level 1 Data Governance Organisation – Aware

  • An Aware Data Governance Organisation knows that the organisation has issues around Data Governance but is doing little to respond to these issues. Awareness has typically come as the result of some major issues that have occurred that have been Data Governance-related. An organisation may also be at the Aware state if they are going through the process of moving to state where they can effectively address issues, but are only in the early stages of the programme.
Level 2 Data Governance Organisation – Reactive
  • Reactive Data Governance Organisation is able to address some of its issues, but not until some time after they have occurred. The organisation is not able to address root causes or predict when they are likely to occur. “Heroes” are often needed to address complex data quality issues and the impact of fixes done on a system-by-system level are often poorly understood.
Level 3 Data Governance Organisation – Proactive
  • Proactive Data Governance Organisation can stop issues before they occur as they are empowered to address root cause problems. At this level, the organisation also conducts ongoing monitoring of data quality to issues that do occur can be resolved quickly.
Level 4 Data Governance Organisation – Managed
Level 5 Data Governance Organisation – Optimal

The MIKE2.0 Solution for the the Centre of Excellence provides an overall approach to improving Data Governance through a Centre of Excellence delivery model for Infrastructure Development and Information Development. We recommend this approach as the most efficient and effective model for building these common set of capabilities across the enterprise environment.

Feel free to check it out when you have a moment and offer any suggestions you may have to improve it.


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This Week’s Blogs for Thought:

Big Data 101

Big data. What exactly is it?

Big data has been hitting the headlines in 2014 more so than any other year. For some people that’s no surprise, and they have a grasp on what big data really is. For others, the two words “big” and “data” don’t conjure up of a meaning of any kind, but instead they stir confusion and many times, misunderstanding.

Read more.

Dinosaurs, Geologists, and the IT-Business Divide

“Like it or not, we live in a tech world, from Apple to Hadoop to Zip files. You can’t ignore the fact that technology touches every facet of our lives. Better to get everything you can, leveraging every byte and every ounce of knowledge IT can bring.”

So write Thomas C. Redman and Bill Sweeney on HBR. Of course, they’re absolutely right. But the tech gap between what organizations can and actually accomplish remains considerable.

Read more.

Data Quality Profiling Considerations

Data profiling is an excellent diagnostic method for gaining additional understanding of the data. Profiling the source data helps inform both business requirements definition and detailed solution designs for data-related project, as well as enabling data issues to be managed ahead of project implementation.

Read more.



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Category: Information Development
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by: Alandduncan
19  Jul  2014

Data Quality Profiling Considerations

Data profiling is an excellent diagnostic method for gaining additional understanding of the data. Profiling the source data helps inform both business requirements definition and detailed solution designs for data-related project, as well as enabling data issues to be managed ahead of project implementation.

Profiling of a data set will be measured with reference to and agreed Data Quality Dimensions (e.g. per those proposed in the recent DAMA white paper).

Profiling may be required at several levels:

• Simple profiling with a single table (e.g. Primary Key constraint violations)
• Medium complexity profiling across two or more interdependent tables (e.g. Foreign Key violations)
• Complex profiling across two or more data sets, with applied business logic (e.g. reconciliation checks)

Note that field-by-field analysis is required to truly understand the data gaps.

Any data profiling analysis must not only identify the issues and underlying root causes, but must also identify the business impact of the data quality problem (measured by effectiveness, efficiency, risk inhibitors). This will help identify any value in remediating the data – great for your data quality Business Case. Root cause analysis also helps identify any process outliers and and drives out requirements for remedial action on managing any identified exceptions.

Be sure to profile your data and take baseline measures before applying any remedial actions – this will enable you to measure the impact of any changes.

I strongly recommend Data Quality Profiling and root-cause analysis to be undertaken as an initiation activity as part of all data warehouse, master data and application migration project phases.

Category: Business Intelligence, Data Quality, Enterprise Data Management, Information Development, Information Governance, Information Management, Information Strategy, Information Value, Metadata
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