Modern data architectures, by enabling data analytics insights, promise to drive order of magnitude value gains across many business sectors (here, here, and here). Not so long ago, big data presented a daunting challenge. Although tools were plentiful, we struggled to conceptualize the architecture and organization within which to capitalize on those tools. Now solid frameworks have emerged. This post reviews two promising models for modern data architecture, and discusses two key cultural values critical to their successful adoption: drive to solve business challenges and drive for universal data correctness. Continue reading
I recently found myself in a series of conversations in which I needed to make a case for dimensional data modeling. The discussions involved a group of highly skilled data architects who were surely familiar with dimensional techniques but didn’t see them as the best solution in the case at hand.
I thought it would be easy to find a quick, jargon free summary of best reporting database design principles aimed at a technical audience. There were a number of good summaries (cited at the end of this post), but none pitched just right for this highly-technical-but-outside-the-data-warehouse-world crowd.
I wanted to raise the dimensional model because, for most business reporting scenarios, it not only delivers on reporting needs, but also helps report developers handle changes to those needs as a side effect of the design.
So these are the notes I prepared for the conversation. They helped us all get on the same page, hopefully they will be useful to others: Continue reading
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
Information Management recently sent around their pick of best IM blog articles of 2009. Among them was Forrester’s James Kobelius’s reaction to Bill Inmon’s “incineration of a straw man concept that he refers to as ‘virtual data warehousing (DW).’”
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