What does represent for you Enterprise Information Map? | LinkedIn Group: MDM - Master Data Management

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My comment 

Your approach perfectly makes sense.

Call it "Enterprise Data Architecture", call it "Enterprise Information Map" - it is indispensable to get ready for the future. In any medium and large organization, (almost) all operational units create, update, use and interpret data for the major part of their daily business duties (even where tangible goods are produced using machines, the latter ones are data-driven.) Consequently, medium and large organizations are first and foremost in "information business" (whereas the underlying data model may vary depending on the industry).

An Enterprise Information Map is therefore not only helping the CIO to develop a road map from a siloed to an integrated application landscape, but should primarily serve as a blueprint for the CEO (with "E" as in "Executive") to pursue the alignment of the operational business units with the "new reality" of being an information business. The CEO should assume leadership in this alignment process, nominate responsible parties and monitor progress and results closely.

In a nutshell, the alignment includes (but is not limited to) business (not IT!) activities such as:

• Design the Master Data model as the core piece of the Enterprise Information Map
• Assign ownership of information entities to business units 
• Identify "central" information entities without "natural" owner (such as master entities Party and Location as well as reference data) to a (new) central unit responsible to conceive mechanisms for management and governance of "central" information entities and to license the above mechanisms for reuse in decentral business units
• Nominate data stewards in decentral business units that are responsible to reuse central mechanisms and, based on entity ownership, to conceive decentral measures for data governance
• Reorganize business processes based on the above mechanisms as well as to integrate data governance measures
• Restructure existing business units to support the reorganized processes
• Train managers and staff how to support the "new" culture.

The above is only a primer to answer your initial question within the given limitations of this medium. Therefore, please feel free to follow up or contact me directly.

I'd like to emphasize here, though, the importance of the CEO's commitment to make sure that the investment into an Enterprise Information Map pays off and will not only remain a sandbox game.

Where to store data quality standards? | LinkedIn Group: Data Governance & Data Quality

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The metadata repository is the right place to store data quality standards: those that can be automatically transformed into database constraints such as referential integrity, data types, data nullability, data domains etc. as well as, more importantly, those business rules that require human interaction.

The hardest part is the perseverance and discipline necessary to maintain the data quality standards, but also to instruct and monitor users that standards are consequently applied.

My additional comment

To answer the question .., related to my comment "The hardest part is the perseverance and discipline necessary to maintain the data quality standards, but also to instruct and monitor users that standards are consequently applied.":

The weakest element in the integrated system of people - processes - tools is undoubtedly the human factor. Users that enter data do not only need to be trained and monitored in their doing, but the organization has to create a cultural climate that rewards high quality of data.

Example: If people that enter data are paid by number of correctly and completely created/updated objects (persons, addresses, products, orders etc.)), the resulting data quality will naturally be higher than if those people are paid by time.

In general, there needs to be a system of incentives that make it attractive for users to contribute to data quality. A simple, but important factor to increase their motivation is also to ask users on a regular basis for their feedback about difficulties and possible improvements of the process.

Data Governance as a part of the SDLC | LinkedIn Group: Data Governance & Stewardship

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I agree with most of what has already been said in preceding comments:

Standardization of the SDLC and the related artifacts such as data models, process models and data flow diagrams definitely contribute to transparency which is a basic demand of any Data Governance endeavor, regardless of industry-specific compliance requirements.

However, Data Governance primarily demands traceability of the production data itself, i.e. transparent data lineage is a major prerequisite so that consuming applications / users can judge the reliability and trustability of data.

Consequently, the SDLC of applications that create, update or delete governance-sensitive data will need to include logbook tables into the application data models and subsequently into the application databases. Such logbook tables comprise e.g. the following columns (and their related trigger functions) and record for each modification event of an application database row (and possibly even of an application database row column):

• Timestamp
• Actor (e.g. staff member, batch process, third-party)
• Physical source (e.g. third-party self-service (Web) application form, postal code verification from external reference, MDM hub, migrated database, merger / acquisition database)
• Status (e.g. active, inactive because customer passed away, inactive as being a duplicate entry)
• Quality indicator, i.e flagged if incomplete and/or incorrect (NOT NULL columns empty, filled with semantically incorrect values or meaningless defaults); flagged if referential integrity is violated (e.g. not every customer has an address)).

Data extraction mechanisms for data warehouses / BI purpose will need to have the ability of filtering data based on its logbook information (and of tracing the data lineage backwards) to make sure that only reliable data contributes to a decision process (or the user is accordingly warned about the related risk).

Data Governance Management. Is it a Program or a Project? | LinkedIn Group: Data Governance & Data Quality

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In a nutshell: Data Governance Management starts as a Project with the purpose to set up roles / responsibilities, procedures and technology to ensure regulatory compliance, data quality and data security. 

It turns into a Program where operational units practice - as agreed in the initial Data Governance Project - their responsibilities and use the defined procedures and technology on a daily basis. Operational units should report issues with the Program to a Data Governance Committee which may trigger follow-up Projects to adjust responsibilities, procedures and technology to improve the existing Program. 

It is the task of the Internal Audit to check on a regular (and/or random) basis that operational units follow their obligations as defined in the Program.