Author Archives: Bob Lambert

Resources for Working From Home: Tips and Gear

In our current “social distancing” situation, many are working remotely in a serious way for the first time. As one who’s worked full time from home for the past four years, and frequently before that, I thought I should share some tips based on experience. Below are my top three tips and then some of the gear that I’ve used to set up a comfortable workspace.

But first to sum it all up, WFH works well for me. On a team of folks mostly working from home, we are engaged and productive, and have developed what I hope are lasting relationships with each other, although we rarely see each other in person.

Here is what has worked for me:

  • Stick to a routine
  • Be available, productive, and communicative
  • Cultivate a non-work social life
  • Set up a comfortable workspace

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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

How to be a good client


I recently listened to Brian O’Neill’s excellent interview with Tom Davenport, headlined “Why on a scale of 1-10, the field of analytics has only gone from a one to about a two in ten years time.”

The conversation covered a lot of ground as Mr O’Neill and Mr Davenport explored the reasons why. Highlights included general lack of technical literacy and lack of an organizational data driven culture. But to their credit, they took responsibility on behalf of analytics professionals, emphasizing how we in the field could change in order to make more analytics efforts successful. Rather than focusing on providing technology-centered solutions, they recommended that data and AI professionals seek first to understand and empathize with their clients or internal customers, enabling data and AI pros to develop more effective analytics capabilities in light of that understanding.

I agree that analytics professionals can improve their game. However, as a former consultant who’s switched over to the client side, I think there’s room for improvement all around. To me, clients who work proactively to prepare for an analytics project position themselves for better outcomes. Continue reading

Prioritize data initiatives with the new Data Management Maturity Index

In my experience, data management is both a mission critical and an undervalued capability. Perhaps recent customer data losses and regulatory initiatives like GDPR tend to raise the stock of data maturity efforts, but it remains undervalued. For example, any Fortune 1000 firm building end-to-end processes finds that much of the cost goes to translating data from different systems that integrate into the process.

Today we have available stage models like CMMI’s Data Management Maturity Model (DMMM) which, as I’ve written, help organizations assess an organization’s maturity level. However, the DMM model aims to assess data maturity at a single agency. It lacks mechanisms to compare multiple agencies or business functions, and therefore can be difficult to translate to prioritized plans for improvement.

Recently I participated, with Manoj Thomas, Joseph Cipolla, and Lemuria Carter, in a study introducing techniques for assessing relative data management maturity of different organizations, and different data management capabilities, within a larger enterprise. Continue reading

Data Governance Meets Procurement

Why pay good money for bad data?

Of course no one would do that on purpose, but I as a consultant over many years I’ve often seen it. A vendor fulfills a contract to the letter, which unfortunately allows them to deliver required reports in various, sometimes changing, formats with suspect data quality. The customer company absorbs these costs, leaning on the data analyst to update PowerPoint decks on schedule before the next monthly management meeting in spite of the extra programming work.

These contracts have been for various goods and services, but almost every business contract today is also a contract for data. If a regional gas company hires a vendor to inspect residential lines, then I suspect it wants reports showing inspections conducted and results; a healthcare firm that sends nurses on house calls needs data detailing call schedules and results; and so on.

Companies that supply goods or provide services often don’t feature data management as a core competency, and the quality of their reporting often doesn’t match the quality of their goods or services. Someone in the customer organization has to code around every addition or omission of an expected Excel column, every “N/A” in a numeric field, and every unexpected change from imperial to metric units. 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

Leadership Must Prioritize Data Quality

Data quality improvements follow specific, clear leadership from the top. Project leaders count data quality among project goals when senior management encourages them to do so with unequivocal incentives, a common business vocabulary, shared understanding of data quality principles, and general agreement on the objects of interest to the business and their key characteristics.

Poor data quality costs businesses about “$15 million per year in losses, according to Gartner.” As Tendü Yoğurtçu puts it, “artificial intelligence (AI) and machine learning algorithms are only as effective as the data they use.” Data scientists understand the difficulties well, as they spend over 70% of their time in data prep.

Recent studies report that data entry typos are the largest source of poor data quality (here and here). My experience says otherwise. From what I’ve seen, operational data is generally good, and data errors only appear when data changes context. In this post I’ll detail why data quality is management’s responsibility, and why data quality will remain poor until leadership makes it a priority. Continue reading

Leader’s Data Manifesto at #EDW19: Building a Foundation for Data Science

It’s been a truism that data is a resource, but to prove it you just have to follow the money. As the illustration shows, the vast majority of corporate market value draws from intangible assets. Just as money is an abstraction that represents wealth, data is an abstraction that represents these intangible assets.

It’s year three after initial rollout of the Leader’s Data Manifesto (LDM). Since then, many widely publicized events have highlighted the value of data and metadata, and the importance of sound data management (here, here, and here). Recently at Enterprise Data World, John Ladley, Danette McGilvray, James Price, and Tom Redman presented this year’s LDM update. They reintroduced the Manifesto, recounted events of the past year, discussed strategy for the coming year, and issued a call to action for data professionals. Continue reading

Enterprise Data Prep for Analytics: Two Principles

Data scientists spend most of their time doing data integration rather than gathering insights. In my interview with data scientist Yan Li, she said that data collection and prep takes at least 70% of her time. Obviously, there’s a lot of integration work to do on data that’s new to analytics efforts, but not every analysis uses brand new data. Organizations can improve analytics efficiency by staging commonly used data pre-integrated for data science.

For years, large organizations have supported data warehouses, but prevailing data warehousing practices often fail when faced with “big data” volume and velocity. Still, warehousing teams in large organizations can pre-prep frequently-used internal data. Examples include reference and master data, production and sales records, and so on.    Continue reading