Category: Analysis
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Lessons from the puppy poster
In some presentations, I assert that top-down data modeling should result in not only a business-consistent model but also a pretty well normalized model. One of the basic concepts behind normalization is functional dependency. In layperson’s terms, functional dependency means separating entities from each other and putting attributes into the obviously correct entity. For example, a…
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Selected data modeling best practices
Recently I was in a conversation about data modeling standards. I confess that I’m not really the standards type. I understand the value of standards and especially how important it is to follow them so others can interpret and use work products. It is just that I prefer to focus on understanding of the principles…
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Big Data opportunities and NoSQL challenges
As a relational database professional I couldn’t help but feel like something would be lost with the emergence of the new Big Data/NoSQL database management systems (DBMS). After about two years of buzz around the topic, I’m really excited about the emerging possibilities. However, I’m pretty sure we’ll miss the relational model’s strengths in requirements…
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Abstracting and recombining all the way to the bank
In the past I’ve never understood what people really mean they say “think outside the box” but Jim Harris, in a recent OCDQ blog post, helped me figure it out. Mr. Harris ends with this provocative line: “the bottom line is Google and Facebook have socialized data in order to capitalize data as a true corporate asset.” The post…
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Get an early start for on-time data modeling
I’m a data modeler, so I enjoyed Jonathon Geiger’s recent article entitled “Why Does Data Modeling Take So Long”. But why does he say it like it’s a bad thing? Mr. Geiger’s bottom line is exactly right: “Most of the time spent developing data models is consumed developing or clarifying the requirements and business rules…
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Metadata goals, ROI, and point solutions
Recently there has been a long, and very interesting, discussion of do-it-yourself versus third-party metadata tools on LinkedIn’s TDWI BI and DW discussion forum (membership required to follow the link). I have followed but haven’t commented, but I suppose I contributed when Information Management kindly published my article on DIY metadata. The discussion is extremely…
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Use conceptual data modeling in requirements definition
I’ve often thought that conceptual data modeling was an underused tool in the arsenal available to requirements analysts, and in a recent conversation I found that many were surprised that it would be used in the requirements phase at all. Checking the Business Analysis Body of Knowledge (BABOK) I found data modeling listed among the…
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IT should own the misalignment problem
In a new post at Insurance Networking News Ara Trembly provides a balanced perspective on IT/business misalignment (Business/IT Misalignment: Whose Responsibility?). He describes the problem as cultural, more amenable to relational than management solutions. His conclusion sums it up: “Take a geek/suit to lunch today!” To me (speaking as an IT professional) IT should take…
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No business value in nulls
It seems I’m frequently in conversations about using null to represent a business value. To paraphrase, say there are credit and cash customers, and there’s a suggestion to set “Customer_Type” to “C” for credit and null for cash. To data and database professionals this is obviously a bad idea, but it’s not obvious from a…
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A pretty good requirements analysis checklist
Recently I was asked for a high level requirements plan for a large IT conversion. I googled around a little for something standard. I found some good references (see links at the bottom of this post), but not exactly what I was looking for: a simple, method-agnostic layout of the high level steps and checkpoints…