Informatica Data Quality Blog

Posted by Chris Cingrani in: Data Quality > Governance / Stewardship

In my previous posts, I have discussed building the business case for data quality as well as the role that a data quality dashboard plays in supporting this case. As previously noted, these efforts will directly impact your ability to articulate the need to pursue a data quality initiative. The reason for returning to this topic is that I have recently participated in multiple discussions with a variety of companies that were either in the process of forming a data governance council or in the process of building the internal business case to support exploring a data governance initiative. In these discussions two common threads were present – the role of data quality in the data governance initiative and the need to change the culture within the organization if data governance is going to succeed. Although these are only two aspects to consider when pursuing a data governance initiative, they are directly tied to the underlying success or failure of the program.

As it pertains to data quality, the key point I often discuss in these meetings is that without a firm understanding of the types of issues in the underlying data and an action plan to remediate these issues, the governance program will ultimately be flawed. The reason is that any decisions made based upon the data assumes it is correct and that a single version of the truth exists within an organization. The concept of governance is to drive to this common version of the truth where data definitions and processes are standardized at the enterprise level. In each of these scenarios, data quality serves a key role, as it allows an organization to monitor and then remediate issues as they arise. By leveraging data quality software and processes on an ongoing basis, the organization is able to monitor and address data quality on an iterative basis. In a sense, you can consider the data quality scorecard a report on the overall health of your organization’s data. If data issues are not addressed, the data related processes or definitions are likely to have issues as well, which compromises the intent of a data governance initiative.

Although data quality plays an important role in any data governance initiative, another key aspect that should be addressed is the cultural impact within an organization. Although processes can be put in place and data quality software can be licensed and implemented, the ultimate success or failure of the initiative will be determined by the acceptance (or lack thereof) within your organization. To that point, data governance needs executive sponsorship within an organization and should be initiated via a top down approach. Without this executive sponsorship and the willingness to make data governance a strategic initiative to the organization, the program is unlikely to gain the traction needed. The reason is often cultural, as people find it easier to continue to do business as usual. To address this, I often request that the various key stakeholders from across an organization are present when we conduct a data governance workshop, as I want to observe how everyone interacts as well as listen to any concerns, criticisms or objections that might be present, as these are the very issues that must be addressed if a governance program is to succeed.

Just as the title of this blog states, “Rome Wasn’t Built in a Day and Neither is a Data Governance Initiative” - as your organization begins to consider a data governance initiative this is something you should remain cognizant of, as the time to initiate a governance program is going to vary based on the types of issues noted in this post. If you do not have executive buy-in and if significant resistance to governance exists, the time to implement a program is going to be longer, as there is going to be a need for educational workshops to address concerns. If you have this sponsorship in place, you should begin to examine the quality of the data that the processes, policies and standards will be built upon.

Until next time….

Posted by Chris Cingrani at 09:44 AM | Permalink | Comments (0) | TrackBacks (0)

Posted by Tom Golden in: Data Quality > Benefits ; Data Quality > Best Practices ; Data Quality ; Data Quality > Technology

Looking in Webopedia.com recently I came across a definition for CDI. Yes webopedia.com - it bills itself as the #1 online encyclopaedia dedicated to computer technology. You might wonder what I was doing surfing this font of knowledge – well I had time on my hands between delayed flights coming back to Europe from the US. You know what they say “time to spare, travel by air.”

The Webopedia.com CDI definition went: “Short for Customer Data Integration, it is the combination of the technology, processes, and services needed to create and maintain an accurate, timely and complete view of the customer across multiple channels, business lines, and, potentially, enterprises, where there are multiple sources of customer data in multiple application systems and databases.”

A bit long winded perhaps, but the three words that shone out at me through the glare of the florescent lights in San Francisco airport were “accurate, timely and complete”; all data quality issues. Despite this, few if any of the Customer Data Integration (CDI) vendors in the market today have truly addressed the data quality issues in their CDI solutions. And anyone who has gone down the route of developing their own custom-built CDI application will be all too familiar with the data quality demands involved.

CDI or customer data hubs, a subset of the wider master data management field, have become a focus for large organizations struggling to improve customer service and the operational efficiency of all customer interactions. Like all complex IT projects the CDI vision can be limited by the usual constraints of inadequate budgets, tight timelines and capabilities. In the pressure to get the CDI project completed organizations have a tendency to push the data quality issue to the side, and also to take more tactical than strategic views of the CDI implementation.

I would argue that this is a mistake; if you want to create a successful customer hub it is essential to focus squarely on data quality and take as broad a view as possible in terms of CDI strategy – especially in the planning phase.

So even if you don’t have time on your hands, and you are pressed to the pin of your collar to implement the project sooner rather than later, don’t forget the data quality. Defective data quality not only limits return on investment (ROI) from a CDI implementation, but it will ultimately lead to outright failure of the solution. The CDI project will not alleviate the problems that can mean poor relationships with customers, vendors, suppliers, regulators, and other stakeholders which can result in poor decisions and missed business opportunities.

On the other hand when data quality is central to the CDI implementation, the business benefits can be vast: increased customer satisfaction, greater customer loyalty, improved revenue and profit, decreased operational costs, and greater regulatory compliance.

At the end of the day I believe that the ultimate success of CDI hinges as much on good data quality as it does on anything else.

Posted by Tom Golden at 02:13 AM | Permalink | Comments (0) | TrackBacks (0)