TRILLIUM SOFTWARE INSIGHTS

Embed Data Governance into your Enterprise Processes Early

By Michael Stiffler, Strategic Consultant, Data Governance, Harte-HanksTrillium Software

One activity that may help create a credible and successful Data Governance program – and achieve early executive buy-in – is embedding Data Governance policies and processes into your corporate infrastructure. This allows for seamless integration of Data Governance into existing processes your company is already relying on.

An example of where you can do this is in your software/system development life cycle (SDLC). Now, of course, if you don’t take this approach, violations of these data governance policies will be seen downstream after the project's completion (ideally earlier if you have robust test and development cycles). But to keep rework to a minimum, be proactive about Data Governance and embed your DG processes into the development lifecycle in the planning stages and through the formal project management organization.
 
Gathering business metadata, documentation, approvals, accountable parties, and data owners is usually much easier when you incorporate these elements up front, than after the completion of the project.  That makes sense, right?

And creating new processes for data quality mitigation or establishing escalation processes is less  efficient when it needs to done as a fire drill on the back end.

Additionally, Data Governance will be a part of the project timeline and you won't have to reset expectations or risk losing resources (e.g., subject matter experts) that are not already allocated and approved for the Data Governance effort.

Among other things, Data Governance should help bridge the gap between IT and the business.  As part of this partnership, these two organizations would have already agreed to follow a standard process for application/system/database development (from collecting requirements to production release), so Data Governance policies will fit right in.

Piggy-backing off existing lifecycle project agreements and embedding Data Governance processes into your SDLC is a logical extension and should make enforcement of Data Governance policies a little easier.

Have you done this before?  How did it work out for you?

QR Codes: Next Generation Data Quality Challenges

By Nelson Ruiz, Senior Product Manager, Harte-Hanks Trillium Software

Quick Response Codes, or QR Codes, are the next generation of barcodes. Wikipedia defines them as “a specific matrix barcode that is readable by dedicated QR barcode readers and camera telephones.  The code consists of black modules arranged in a square pattern on a white background.”  They are popular in Japan, South Korea and the Netherlands.

So, these barcodes contain data, and since I have been working in the data quality space for years, I, of course asked myself, “I wonder when we will read about the first big manufacturing mistake due to a QR code error?”

And that is when I learned about QC Codes Error Correction (http://www.denso-wave.com/qrcode/qrgene2-e.html). QR Code Error Correction is like a “backup system” within the QR Code that will allow the retrieval of the correct data from the code in case the QR Code is damaged in some way.

Now the “error code” methodology makes use of data redundancy and it takes double the space to store a retrievable word. Which means not all data on the QR Code can be “Error Corrected.”  In such cases, the business driver or analyst needs to actually decide which information on this QR Code is of the highest importance in order to include it in the Error Correction of the QR Code.  When deploying data quality in your enterprise, you may need to take the same approach.  And you create business rules that tell the business and the system what information, data, and processes are most critical – and what the business response should be to drive positive result. 

Business rules are created to automate the method of maintaining data quality. In recent years, people have been crazed about business rules for measuring data quality. In order to ensure enterprise data quality, companies are proactively building data quality business rules as a best practice for ensuring higher quality data, with the objective of using this EDQ solution to monitor and manage data while approaching higher data quality standards.

This approach will not only create better data quality, but ensure higher levels of ROI of your EDQ implementation and Master Data Management practice. This approach will allow you to centralize your EDQ efforts while providing cross-platform and cross-business-application support on a single DQ solution.

For example, when we have a centralized DQ rule for what is a valid product-code, we can ensure that the business rules that define an accurate product-code are consistently correct across the enterprise. If the creation and maintenance of business rules can be a challenge for a large enterprise, just think of how much it will slow down your business if you had multiple copies of these rules. For the same reason that companies deploy Single Customer View Solutions, you want to use an EDQ solution that allows you to deploy the same business rules across multiple applications and environments.

When you create your next RFP regarding EDQ solutions, ensure good data quality “Error Correction” capabilities. Ask yourself, Can it be deployed across applications and platforms supporting the same business rules? Does it support business-rules for business drivers and business rules for data quality? 

Your data stewards and data users will be eternally grateful to you for doing so.

Care to comment on your Error Correction processes for data quality?  Or, send me a Tweet at @_nelson_ruiz