Next-gen 360° view of customers

For a long time, master data management (MDM) practitioners boasted about their ability to build a 360° view of customers by aggregating and proactively managing information coming from various business applications such as CRM systems, ERP applications, and other operational systems.MDM_BigData

But was it really a 360° view? What about transactional and historical data? What about external data sources like social media? What about unstructured content such as emails or call records? Read More »

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Cracking the code to successful conversions: Logical data models  

I have a question --- do we need a logical data model for a conversion?  Here are my thoughts.  I believe the answer is yes if the conversion has any of the following characteristics:

  1. The target application is created in-house. This application will more than likely be enhanced in the future, so good definitions and understanding of the relationships would be required.
  2. If the target application is purchased, BUT we are adding extensions. Again, this application will more than likely be enhanced in the future, BUT what is more important is that it will be upgraded at some point in time. In addition, we will need to understand the changes that will need to take place in the upgrade.

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Master Data Access Use Case #2: The Composite Record

In the last post we looked at the use case for master data in which the consuming application expected a single unique representative record for each unique entity. This would be valuable in situations for batch accesses like SQL queries where aggregates are associated with one and only one entity record. This week, we look at a second use case that might be more common in an interactive environment where the users desire access to all the data associated with a particular entity, such as customer service or fraud investigation. Read More »

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When data isn't sticky enough to keep customers

A few years ago, at the urging of my accountant, I switched from a single-person LLC to an S corp. Sure, I'd have to do my own payroll from that point forward, but the tax benefits easily justified the move. Every quarter, I would now process payroll for all Simon, Inc. employees—and, by that, I mean yours truly.
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Data steward is a tough role to play

In my previous post I explained that even if your organization does not have anyone with data steward as their official job title, data stewardship plays a crucial role in data governance and data quality.

Let’s assume that this has inspired you to formally make data steward an official job title. How should you go about finding good candidates for such an important role? You could take inspiration from some of the examples noted in the bestselling book Rework by Jason Fried and David Heinemeier Hansson. Read More »

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Master data access use case #1: The unique record

Last time I suggested that there are some typical use cases for master data, and this week we will examine the desire for accessibility to a presumed “golden” record that represents “the single source of truth” for a specific entity. I put both of those terms in quotes because I think they are both mistaken. A record that is cobbled together by pulling data values from an assortment of sources to create a record that is inconsistent with almost all of the sources could hardly be called “golden.” The interpretation of what is (or is not) “truth” is based on the use of the data, and it would be presumptuous for an IT-project to dictate what is to be considered the truth.

That being said, the desire for a unique representation of a master entity remains, and there are reasonable expectations that any application’s search for an entity’s information will return one and only one record. This is particularly true when the master domain is employed as part of a reporting or analytics activity in which queries are aggregating values associated with each unique entity. Read More »

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Cracking the code to successful conversions: Prototype or not!

To perform a successful data conversion, you have to know a number of things. In this series, we have uncovered the following about our conversion:

  1. Scope of the conversion
  2. Infrastructure for the conversion
  3. Source of the conversion
  4. Target for the conversion
  5. Management for the conversion
  6. Testing and Quality Assurance for the conversion
  7. Governance and stewardship requirements
  8. Data management standards and guidelines
  9. Technology for the conversion
  10. Target security requirements
  11. Data requirements

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The causal link between education and data quality

Here on the Data Roundtable we've discussed many topics such as root-cause analysis, continual improvement and defect prevention. Every organization must focus on these disciplines to create long-term value from data quality improvement instead of some fleeting benefit.

Nowhere is this more important than the need for an appropriate education strategy, both in relation to data quality and the underlying systems and local policies. It is an area that often gets ignored in the quest for technological advances, so I wanted to recount a simple story that outlines the importance of factoring education into your root-cause discovery. Read More »

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