Tag: Alignment
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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…
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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…
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Reengineered Processes Need Business-Defined Data
“Business process reengineering is the act of recreating a core business process with the goal of improving product output, quality, or reducing costs.”* Recently I’ve perused articles on business process reengineering and have been surprised to find that they share a lack of emphasis on data definition. By establishing a shared business vocabulary, identifying and…
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Data Architecture for Improved Dashboard Performance
Sometimes success seems like a data analytics team’s worst enemy. A few successful visualizations packaged up into a dashboard by a small skunkworks team can generate interest such that a year later the team has published scores of mission critical dashboards. As their use spreads throughout the organization, and as features expand to meet the…
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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…
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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…
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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…
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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…
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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…
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Anonymize Data for Better Executive Analytics
Reading articles about data anonymization makes it clear that it is not an entirely effective security measure (here and here), but still part of a robust security capability, and required if your organization is affected by GDPR. (I use “anonymization” as a general term encompassing techniques that de-identify personal data within a given data set.) But there’s a positive…