Author

David Loshin
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President, Knowledge Integrity, Inc.

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.

Data Management
David Loshin 0
Data modeling for data policy management

Operationalizing data governance means putting processes and tools in place for defining, enforcing and reporting on compliance with data quality and validation standards. There is a life cycle associated with a data policy, which is typically motivated by an externally mandated business policy or expectation, such as regulatory compliance.

Data Management
David Loshin 0
Embedding event stream analytics

In my last two posts, I introduced some opportunities that arise from integrating event stream processing (ESP) within the nodes of a distributed network. We considered one type of deployment that includes the emergent Internet of Things (IoT) model in which there are numerous end nodes that monitor a set of sensors,

Data Management
David Loshin 0
Pushing event analytics to the edge

In my last post, we examined the growing importance of event stream processing to predictive and prescriptive analytics. In the example we discussed, we looked at how all the event streams from point-of-sale systems from multiple retail locations are absorbed at a centralized point for analysis. Yet the beneficiaries of those

Data Management
David Loshin 0
Creating the MDM demo

With our recent client engagements in which the organization is implementing one or more master data management (MDM) projects, I have been advocating that a task to design a demonstration application be added to the early part of the project plan. Many early MDM implementers seem to have taken the

David Loshin 0
What is reference data harmonization?

A few weeks back I noted that one of the objectives on an inventory process for reference data was data harmonization, which meant determining when two reference sets refer to the same conceptual domain and harmonizing the contents into a conformed standard domain. Conceptually it sounds relatively straightforward, but as

David Loshin 0
Integration planning for master data management

A few years ago, I was presenting a morning course on master data management in which I shared some thoughts about some of the barriers to success in transitioning the use of a developed master data management index and repository into production systems. During the coffee break, an attendee mentioned

David Loshin 0
Behavior architecture

In the past few weeks I have presented training sessions on data governance, master data management, data quality and analytics at three different venues. At each one of these events, during one of the breaks a variety of people in my course noted that the technical concepts of implementing programs

David Loshin 0
Behavior modeling

In my last post I introduced the term “behavior architecture,” and this time I would like to explore what that concept means. One approach is to start with the basics: given a business process with a set of decision points and a number of participants, the behavior architecture is the

David Loshin 0
Behavior engineering

Instituting an analytics program in which actionable insight is delivered to a business consumer will be successful if those consumers are aware of what they need to do to improve their processes and reap the benefits. As we have explored over the past few posts, success in the use of

David Loshin 0
Data governance and big data

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

David Loshin 0
Big data and data enrichment

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