Category Archives: App Dev

Secrets of Successful Projects

I’ve had the good fortune to have been involved in many successful application development and analytics efforts (here and here), and a few that were less so (here and here). Recently, I’ve thought about the differences between the successful and the unsuccessful.

As I see it, there are five general characteristics that the successful endeavors share. They: 

  • Closely Align with the Business
  • Share an End State Vision
  • Are Reality Grounded
  • Have a Technically Competent Team
  • Feature Frequent and Open Communications

To level set, this article focuses on business-facing analytics, data acquisition, application development, and related efforts developing internal capabilities for larger organizations. Continue reading

One More Species of Overloaded Data

A while back I wrote the post A Field Guide to Overloaded Data, which publicized the work of Duane Hufford, who examined different types of overloaded data during the 1990s. Over the years his classifications of overloaded data effectively categorized data anomalies I encountered in the wild.

That is until recently, when a colleague encountered an array of file names in a single SQL Server column. This instance didn’t fit into the three categories detailed in the earlier post, so I’m presenting it here. I’ve also added it to the the original post.


Definition: Bundled data is a situation where a single column in a table contains an array of values. Continue reading

Guidelines for Successful Tableau Analytics Rollout

I’ve written previously about development of Tableau analytics capability from single user to multiple teams across an organization. This article is intended for those who may have first installed Tableau Server to enable folks outside their own sphere to interact with their Tableau creations. For the way ahead, it presents a few guidelines for successful development and deployment that data analysts should internalize as their analytics product grows.

The theme is, from the very start, to develop dashboards as if they serve hundreds of users and access millions of data records. If you do that, then as your analytical tools grow in usefulness and popularity, you’ll avoid difficult conversions and retooling later. Continue reading

The Myth of Agile Sign-Off

Although Agile writers and thinkers agree that “there is no sign-off” in Agile methodology, the practice of requiring product owners and business customers to sign off on requirements and delivered work products persists in Agile settings. I’ve seen it most when an agile team faces delivery challenges and leaders perceive the problem is scope creep or failure of the UAT process before delivery. In those situations, adding a formal sign-off provides an illusion of a stronger process but does nothing to resolve the underlying issue.

Sure, sometimes sign-off is necessary, especially when two or more separate organizations work together on a project. For example, consulting contracts often require sign-off on interim and final work products. However, addition of a sign-off step is common within organizations in hopes of a remedy for delivery or quality challenges.

The commenter David on this post says that “the purpose of a sign-off (or whatever you wanna call it) is a confirmation from a product owner that artifact A is fine for the time being, and can be used as basis for work on artifact B.” That’s all well and good, but in a well-run Agile context sign-off is a meaningless formality that’s dispensed with because it’s unnecessary.

How could that be? Others have written, often emphatically, on why sign-off is unnecessary in an agile context, including here and here. This quick video explains how “definition of done” and a fully committed, reliable team work together together make sign-off irrelevant. Continue reading

Two key interview questions for SQL developer candidates

Frequently in my career I’ve selected or helped select ETL and reporting professionals who need SQL skills. For some of those opportunities, placement firms returned resumes with interminable, and nearly identical, lists of technical achievements with excruciating unnecessary detail (paraphrasing: “Wrote SELECT statements using GROUP BY”, “Applied both inner and outer joins”). Before interviewing we typically ranked candidates in order of preference based on resumes. Candidates’ interview success bore little relation to resume-based rankings.

With some candidates I’ve encountered consistent pauses after fact questions, sometimes accompanied by keyboard clicks. They obviously used “think time” to look up answers on the internet. As a result, “fact” questions didn’t distinguish one candidate from another.

On the other hand, open ended questions worked well. I’ve written before that interviews should ask opinion rather than fact questions. Open ended questions or thought exercise, as opposed to fact questions, assess SQL skill level, are hard to quickly look up on the web, and have the added benefit of demonstrating a candidate’s reasoning and communication skills. Here are two of my favorite examples: Continue reading

Two Design Principles for Tableau Data Sources

It’s not unusual for talented teams of business analysts to find themselves maintaining significant inventories of Tableau dashboards. In addition to sound development practices, following two key principles in data source design help these teams spend less time in maintenance and focus more on building new visualizations: publishing Tableau data sources separately from workbooks and waiting until the last opportunity to join dimension and fact data.

Imagine a business team — let’s call it Marketing Analytics — with read-only access to a Hadoop store or an enterprise data warehouse. They gain approval for Tableau licenses and Tableau Server publication rights for five tech-savvy data analysts. After a few initial successes with some impactful visualizations, the team gathers steam. After a while the team finds itself supporting scores of published workbooks serving a few hundred managers and executives. In spite of generally sound practices, Marketing Analytics struggles to maintain consistency from one Tableau workbook to another.

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More on “Select Failed. [2646] No more spool space”

Also see the previous related post Escaping Teradata Purgatory (Select Failed. [2646] No more spool space)

Not too long ago I posted on how to avoid the dreaded “No more spool space” error in Teradata SQL. That post recounted approaches to restructuring SQL queries so that they would avoid being cancelled for using inordinate amounts of Teradata resources. Teradata is an immensely powerful, even if aging, database engine but it does little to help one not steeped in knowledge of its structure to use its resources efficiently.

But what if, as sometimes happens, your DB admin team further tightens the screws by  reducing spool space, or imposing new execution time or CPU usage limits? Then, you’ll have to go further to make queries efficient, as happened on one team that I was a part of. Beyond the steps previously recommended, here’s what we did: Continue reading

Toward a Values-Based Approach to Auditing Agile Projects

By now Agile has taken over waterfall as the dominant app dev project pattern*. In many large organizations, the traditional waterfall PMO also owns Agile projects. One aspect of PMO oversight that can work against Agile culture is the project audit. If the goal of an audit is to ensure the project reflects Agile values, it can help ensure working software and a satisfied customer. If not, an Agile project audit can reinforce process, documentation, and other values that don’t directly promote project success.

In this post I’ll briefly review the Agile Manifesto, recount some prominent advice for auditors of Agile projects, and offer suggestions for auditors who want to reinforce rather than suppress Agile values. Continue reading

Meaningful Requirements Start Successful Data Projects

To me, development projects fail or succeed in the first few weeks. Once a project starts off in the wrong direction, momentum and expectations tend to prevent a return to the proper path. With today’s wealth of database options each addressing exciting new possibilities, the right choice for the application’s data foundation plays a large part in steering a project to success.

At this year’s Enterprise Data World conference, William Brooks showed the relations among different data modeling approaches, in effect detailing how to derive nine different model types from a detailed conceptual entity relationship model. Mr Brooks’ presentation hinted at a way to correctly frame up your data direction early on in a project, setting the stage for success.

According to his presentation, called “Symmetry in Modeling Approaches“, the different model types — relational, graph, dimensional, JSON, XML, and so on — all represent different perspectives on the same data relationships. Each suits a different application, like dimensional for reporting applications, data vault for data warehouses, graph databases for multi-layered search, and so on. However, if properly constructed they all map back in predictable and specific ways to a normalized entity-relationship model.

I and others write that ER modeling should be integral to requirements definition, but Mr. Brooks’ presentation implies that ER modeling can also serve as the basis for application architecture as well. Continue reading

Values and Behaviors of the Successful Agilist

Of course, any discussion of Agile values starts with the Agile Manifesto. The first sentence declares that Agile development is about seeking better ways and helping others. Then, as if espousing self-evident truths, the founders present four relative value statements. Finally, they emphasize appropriate balance, saying that the relatively less valued items aren’t worthless: implying that they are to be maintained inasmuch as they support the relatively more valued items.

While there is value in the four relative value statements, I believe most successful Agilists value the first and last statements more. So to me, the core Agile values are continuous improvement, helping others, and balance.

There’s a lot written about Agile behaviors, but as I read most is geared toward scrummasters or managers, and most is about transitioning from the waterfall world. Starting from the premise that Agile methods are established, focusing on participants rather than managers, and based on the assumption that behaviors are grounded in values, this post details the values and behaviors I’ve observed of those who succeed as Agile team members.

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