David Loshin, president of Knowledge Integrity, Inc., is a recognized thought leader and expert consultant in the areas of data quality, master data management and business intelligence. David is a prolific author regarding data management best practices, via the expert channel at b-eye-network.com and numerous books, white papers, and web seminars on a variety of data management best practices. His book, Business Intelligence: The Savvy Manager’s Guide (June 2003) has been hailed as a resource allowing readers to “gain an understanding of business intelligence, business management disciplines, data warehousing and how all of the pieces work together.” His book, Master Data Management, has been endorsed by data management industry leaders, and his valuable MDM insights can be reviewed at mdmbook.com . David is also the author of The Practitioner’s Guide to Data Quality Improvement. He can be reached at loshin@knowledge-integrity.com.
The data quality and data governance community has a somewhat disconcerting habit to want to append the word “quality” to every phrase that has the word “data” in it. So it is no surprise that the growing use of the phrase “big data” has been duly followed by claims of
In my last post, we started to look at some of the issues with the concept of “big data governance,” especially when a large part of governance is intended to prevent the introduction of errors into data sets. Many big data analytics applications focus on the intake of numerous varied
In my last post, I noted two key issues where there is the desire to impose governance over large-scale data sets imported from outside the organization: the absence of control and the absence of semantics. Of course, we cannot just throw up our hands and say that the data is
Last time we explored consumption and usability as an alternative approach to data governance. In that framework, data stewards can measure the quality of the data and alert users about potential risks of using the results, but are prevented from changing the data. In this post we can look at
The conventional approach to data organization within a business is largely correlated to the original operational intent. Whether the data is collected or created as a result of executing a business transaction or as a result of managing operational activities, in most cases the information is effectively a byproduct of
Almost by definition, a customer-centric strategy demands identification of each unique customer within the customer community. Creating a representative model of the customer is a necessary prelude to developing customer profile models and analyzing any characteristics and behaviors for classification. That model must, at the very least, incorporate these aspects:
In my last post, I suggested that there is a difference between data attributes used for unique identification and those used for attribution to facilitate customer segmentation and classification. An example of some attributes used for segmentation are those associated with location, such as home address or package delivery address.
If a key concept of customer centricity is understanding relationship networks and any individual’s sphere of influence, it is critical that the organization of the data incorporate two different aspects of these networks. The first is the concept of a relationship, which can bind a customer to some other entity
In my last three posts, we walked through a thought experiment about the decision-making process, with the conclusion that a method for ensuring the quality of report data used to make decisions will highlight the value of those individuals whose instincts and experience allow them to generally make good decisions.
In my last two posts, we looked at a simple model of a decision-making process, and I drew the conclusion that when an individual is faced with a decision and may not trust his/her own decision-making capabilities, a way to deflect accountability for a poor decision is to call the
