Obviously, data management is important. Unfortunately, it is not prioritized in most organizations. Those that effectively manage data perform far better than organizations that don’t. Everyone who needs data to do his/her job must drive change to improve data management.
That was the theme of the recent Enterprise Data World (EDWorld) conference this week. This year’s EDWorld event might be the start of a new vitality and influence for the field, marked by introduction of a Leader’s Data Manifesto.
Over the years, data practitioners struggled for recognition and resources within their organizations. In reaction, they often focused on data “train wrecks” that this neglect causes. This year’s conference was no exception. For example: Continue reading →
I’ve written about groupthink-related quality challenges on Agile projects, and the architect’s role in preventing groupthink from degrading quality. I’ve seen other risks related to the cohesion and potential insularity of successful Agile teams, and the architect is also well positioned to help prevent these: a tendency to neglect setting up and documenting repeatable processes, and a similar tendency not to share of knowledge and lessons learned outside the Agile team. Continue reading →
I believe that early, effective big picture diagrams are key to application development project success. According to the old saw, no project succeeds without a catchy acronym. Maybe so, but I’d say no project succeeds without a good big picture diagram. The question: what constitutes a good one? To me good high-level diagrams have four key characteristics: they are simple, precise, expressive, and correct.
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.
One common theme in recent tectonic shifts in information technology is data management. Analyzing customer responses may require combing through unstructured emails and tweets. Timely analysis of web interactions may demand a big data solution. Deployment of data visualization tools to users may dictate redesign of warehouses and marts. The data architect is a key player in harnessing and capitalizing on new data technologies. Continue reading →
The Atlantic, not typically a technical rag, recently presented an article by business and economics editor Megan McArdle on health care data integration entitled “Paging Dr. Luddite”. The article brings to a mass audience an understanding of both the importance and difficulty of data integration, but the title and general anti-healthcare-professional tone seem counterproductive.
Need uber-guru types who are willing to challenge the existing groupthink on design and architecture, especially on TDD and emergent design and pair programming anti-pattern” – job post at Monster.com 2/9/2010
I stumbled upon that quote following links on the role of the architect on an agile project. Maybe one important role of the architect is to help the team avoid groupthink. Continue reading →
According to Mr. Inmon, virtual data warehousing reminds him of the carnival game called whac-a-mole. He says “just when you think this incredibly inane idea has died and just when someone has delivered what should have been a deathly blow, out it pops again from another hole.” Continue reading →
Many see IT as application of technology to solve business problems.
Of course, this is true but it leaves out the third element, which is to apply the right architectural pattern to solve the problem. For example, when the business problem is that reporting is slow and reports from different departments don’t match, the astute IT professional immediately thinks in terms of a data warehousing pattern employing technologies like databases, extract-transform-load (ETL) tools, and multi-dimensional reporting suites. Continue reading →
Today, the foundation of most of our custom-built systems is a relational dbms. While development frameworks vary, they overwhelmingly access and maintain data in relational tables and columns. As I write I routinely save this post in a MySQL database, and at work I tend SQL Server applications. Millions of others develop, use, and extract analytical data from thousands of SQL Server, DB2, and Oracle applications, on servers and networks maintained in-house by in-house administrators. Continue reading →