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
Over the past year I’ve reviewed what seem like countless plans for enterprise data warehouses. The plans address real problems in the organizations involved: the organization needs better data to recognize trends and react faster to opportunities and challenges; business measures and analyses are unavailable because data in source systems is inconsistent, incomplete, erroneous, or contains current values but no history; and so on.
The plans detail source system data and its integration into a central data hub. But the ones I’m referring to don’t tell how the data will be delivered, or portray a specific vision of how the data is to drive business value. Instead, their business case rests on what I’ll call the “railroad hypothesis”. No one could have predicted how the railroads enabled development of the West, so the improved data infrastructure will create order of magnitude improvements in ability to access, share, and utilize data, from which order of magnitude business benefits will follow.* All too often these plans just build bridges to nowhere. Continue reading
There’s an unfortunate and rather rude saying about assumptions that I’ve found popular among IT folks I’ve worked with. I say unfortunate because, to me, assumptions that are recognized early and handled the right way are a key to successful projects. Technical players who use assumptions well can help set projects on the right path long before they go astray.
Sometimes on waterfall and hybrid projects technical players are asked to estimate work early, before requirements are complete. My instinctive reaction is not to provide an ungrounded estimate, but that’s not helpful. The way to handle this uncomfortable uncertainty is to fill out the unknowns with assumptions: detailed, realistic statements that provide grounding for your estimate. Continue reading
Yesterday I had the pleasure of presenting “The Business End of Data Modeling” for the Lynchburg SQL Server User’s Group. It was a great time, thanks for having me out!
I’ve linked the presentation below, please comment here or shoot me an email if you have comments or questions.
Logical data modeling is one of my tools of choice in business analysis and requirements definition. That’s not particularly unusual – the BABOK (Business Analysis Body of Knowledge) recognizes the Entity-Relationship Diagram (ERD) as a business analysis tool, and for many organizations it’s a non-optional part of requirements document templates.
In practice, however, data models in requirements packages often include many-to-many relationships. I’ve heard experienced data modelers advocate this practice, and it unfortunately seems consistent with the “just enough, just in time” approach associated with agile culture.
In my experience unresolved M:M relationships indicate equally unresolved business questions. The result: schedule delays and budget overruns as missed requirements are built back in to the design, or the familiar “that’s not what we wanted” reaction during User Acceptance Testing (UAT). Continue reading
Asking fact questions in technical interviews is like eating a donut, feels great at the time but not so satisfying later.
Let’s say the interview consists of facts like this “softball question”: “What is the default port number for SQL Server?” The linked list of questions is a really good high level study guide for a SQL Server student. If a SQL Server developer candidate answers all correctly, then the interviewer can be confident that the candidate knows a lot about SQL Server.
However, few development jobs require only technical fact knowledge. Typically, developers must apply creativity when working with unclear or poorly expressed requirements under tight schedules. They must be versatile so that they can take on unforeseen roles in case of resignations or transfers of team members. If you make an investment in an individual by hiring her or him, you’ll look for a return in the form of professional development as the individual grows their skills.
So how do you test creativity, versatility, and ability to learn, while still gauging raw technical talent? My method is to ask opinion rather than fact questions. Continue reading
How does this sound as advice for an app dev manager leading his or her team from waterfall to Agile?
- Clearly articulate a compelling end-state vision
- Work from a position of authority
- Weather the storms
- Reward creativity while fostering improvement
A post at scrumsource.com lists leadership, organizational culture, and people as three of the five key factors in making the transition. Another at the Scrum Alliance site describes the transition as a migration from externally-organized to self-organizing teams. In my experience the transition requires leadership by a strong advocate who shows the way to willing, empowered team members.
The US men’s national soccer team (USMNT) is playing out a strikingly similar transition. Continue reading
Out of curiosity I recently reviewed articles critical of Agile Methodologies. I had expected agile-versus-waterfall arguments and attacks from vendors selling new alternatives, but even given the reputation that advocates have for flaming well-intentioned critics, I wasn’t prepared for the level of emotion I found.
My opening position was that Agile techniques are great, but like any other tool there are limits and prerequisites. The critical articles I read strengthened that view. Let’s review three examples that stood out, in reverse order: 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.
Recently the BBC posted this video. On first view it is just funny, but watching those dogs learn to drive really reminded me of personal experiences with IT teams making big learning transitions. To represent those real situations let’s consider a fictional team of SQL developers facing the daunting task of deploying a functional Hadoop-based analytics prototype in two months. The video parallels their critical learning success factors: (1) set audacious goals, (2) learn bit by bit, and (3) know your limits.