When is the BEST time for a Data Quality Review? | Roshan Joseph (via LinkedIn)

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My comment

While position 5 (NOW!) is the "correct" answer, I like to add "Merger & Acquisition" as a triggering event (variation / combination of pos. 1 to 4).

With an upcoming M&A transaction, a data quality review prepares for the audit that is an indispensable part of the due diligence. Both (all) involved organizations should undergo a data quality review to especially know about the mergability of the parties' data before taking the final decision.

Tool to Track Which Databases Keep Customer Data | LinkedIn Group: Master Data Management Pros

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I suggest you to use a professional data and process modeling tool suite.

The data modeling tool component will allow you to have an inventory of the data, i.e. which fields (particularly: customer data) reside in which database. Typical use of the data modeling tool could be:

• Reverse engineer each database, i.e. automatic transfer of the database structure to a graphical/textual representation in the data modeling tool.
• (Since even (semantically) same fields will have different physical names in different databases...) Link synonyms to a common business name, e.g. "cust_name" and "cli_nam" could both represent "customer name". 
• Add/modify any other crucial description that may be missing/incorrect.
• Integrate the database models into subject areas (A subject area will give you the synchronized business view of how e.g. a customer is - and perspectively should be - described in your organization, e.g. by customer first-name, customer family-name, customer date-of-birth, etc.)

The process modeling tool component will provide you a graphical/textual representation how the database fields "flow" through your organization, i.e. which fields are included in the "input" and/or "output" data flow(s) of a process (program, module, dialog,...).

Ideally, the tool suite will be integrated, i.e. database fields that are captured in the database reverse engineering step (using the data modeling tool) can be linked to the fields found in the analysis of the data flows (using the process modeling tool) and vice versa.

How you apply the modeling tool suite in detail will certainly depend on the mid and long-term goals of your organization, e.g. merging/replacing application systems, evaluating new software packages, changing platforms, going mobile etc.

Considering any of these targets combined with your initial question, I recommend you to check out the SILVERRUN Professional & Enterprise Series at www.silverrun.com . (In the spirit of full disclosure: I represent Grandite, the maker of the SILVERRUN tools.)

Please do not hesitate to contact me for further information directly, you will find my coordinates in "Contact Info" of my LinkedIn profile.

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 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.