Data prep considerations for analytics, Part 2

.@philsimon continues his series on data prep and anlytics.

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Data prep considerations for analytics, Part 1

I'm hard-pressed to think of a trendier yet more amorphous term today than analytics. It seems that every organization wants to take advantage of analytics, but few really are doing that – at least to the extent possible. This topic interests me quite a bit, and I hope to explore […]

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Modernization and data-driven culture – Part 2

Modernization is a term used to describe the necessary evolution of information technologies that organizations rely on to remain competitive in today’s constantly changing business world. New technologies – many designed to better leverage big data – challenge existing data infrastructures and business models. This forces enterprises to modernize their approach to data […]

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Data ops: Better way to prepare data for analytics and IoT?

We all find change easier when it starts with something we’re familiar with. That’s why I think sports analytics examples are popular – most of us are sports fans, so we get it more easily. It’s also why automotive examples that illustrate the potential reach of the Internet of Things […]

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Modernization and data-driven culture – Part 1

Modernization is a term used to describe the necessary evolution of information technologies that organizations rely on to remain competitive in today’s constantly changing business world. New technologies – many designed to better leverage big data – challenge existing data infrastructures and business models. This forces enterprises to modernize their approach to data […]

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Data gone awry, Part 1: Will your business data deceive you? 

.@philsimon on whether big data and analytics offer true guarantees.






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Which comes first, data quality or data analytics?

While it’s obvious that chickens hatch from eggs that were laid by other chickens, what’s less obvious is which came first – the chicken or the egg? This classic conundrum has long puzzled non-scientists and scientists alike. There are almost as many people on Team Chicken as there are on Team […]

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How big of a deal is big data quality?

Data quality has always been relative and variable, meaning data quality is relative to a particular business use and can vary by user. Data of sufficient quality for one business use may be insufficient for other business uses, and data considered good by one user may be considered bad by others. […]

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Data governance and analytics

The intersection of data governance and analytics doesn’t seem to get discussed as often as its intersection with data management, where data governance provides the guiding principles and context-specific policies that frame the processes and procedures of data management. The reason for this is not, as some may want to […]

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Analyzing the data lake

In my previous post I used junk drawers as an example of the downside of including more data in our analytics just in case it helps us discover more insights only to end up with more flotsam than findings. In this post I want to float some thoughts about a two-word concept […]

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