Cracking the code to successful conversions: Physical data models

The physical data model should represent exactly the way the tables and columns are designed in the in the database management system.  I recommend keeping storage, partitioning, indexing and other physical characteristics in the data model if at all possible.  This will make upkeep and comparison with the development, test or production database much much easier. Read More »

Post a Comment

Thoughts on dynamic data provenance

We've explored data provenance and the importance of data lineage before on the Data Roundtable (see here). If you are working in a regulated sector such as banking, insurance or healthcare, it is especially important right now and one of the essential elements of data quality that they look for in their assessments.

Data lineage is vital to data quality management because we need to know where data originates from in order to build data quality rules to measure, monitor and improve it, ideally at source. Read More »

Post a Comment

Can big data and bad administration coexist?

Around the time of the publication of Too Big to Ignore, I spoke at a large conference. The conference sponsor—a very large company that we'll call ABC here—was struggling to find speakers. (I suppose that there weren't too many "big data experts" back then.) What unfolded over the next two months convinced me that, at an organizational level, ABC was incapable of acting with the agility that big data demands.

Read More »

Post a Comment

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 »

Post a Comment

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.

Read More »

Post a Comment

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 »

Post a Comment

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.
Read More »

Post a Comment

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 »

Post a Comment