Recently, I posted “Interview with a Data Scientist” at my company’s blog site. In it, my friend and colleague Yan Li answers four questions about being a data scientist and what it takes to become one. In my view Yan’s responses provide a bracing reminder that data science is something truly new, but that it rests on universal principles of application development. Continue reading
As important as it is, data modeling has always had a geeky, faintly impractical tinge to some. I’ve seen application development projects proceed with a suboptimal, “good enough”, model. The resulting systems might otherwise be well-architected, but sometimes strange vulnerabilities emerge that track directly to data design flaws.
Recently I saw an example where a “good enough” data design, similar to the one pictured, enabled a significant application bug.
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 business person knows that item color doesn’t belong in the order table because it describes the item, not the order. Everyone knows that the order isn’t green! Continue reading
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 behind the standards. In general, it seems to me that following standards is trivial for someone who understand the principles, but impossible for someone who doesn’t. But there doesn’t seem to be general understanding of data modeling principles. Continue reading
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 definition and conceptual design. Continue reading
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 starts with a cold war analogy and proceeds to describe how Facebook and Google have made big money as “internet advertising agencies:” offering free services with which users (like us) serve up personal data in return for use of the service, then selling advertising space based on our data (hopefully anonymized).
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 and ensuring that the data structure can be populated by the existing data sources.” On the projects he describes, no one took time before modeling to determine available data sources and identify business entities of interest, relationships among them, and attributes that describe them before database design started, so the data modeler had to do it.
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 informative, presenting the views of a variety of knowledgeable professionals in different situations, and describing successful and sometimes not-so-successful efforts to solve the essential metadata challenge: how to document what information is locked up in databases. Continue reading
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 tools available to requirements analysts to “to describe the concepts relevant to a domain, the relationships between those concepts, and information associated with them.” There’s also Steve Hoberman’s excellent book on the topic, Data Modeling for the Business, an introduction to data modeling aimed at a business audience. Continue reading
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 the initiative to solve the problem. Continue reading