In this blog series, I am exploring if it’s wise to crowdsource data improvement, and if the power of the crowd can enable organizations to incorporate better enterprise data quality practices. In Part 1, I provided a high-level definition of crowdsourcing and explained that while it can be applied to a wide range of projects
Tag: data enrichment
The "Internet of Things" is the latest buzzword characterizing the machine-generated big data that has outstripped our ability to derive value from it. Think of UPS delivering 16 million packages every day through various hubs and all the logistics and decisioning that goes into that. But how does an organization
Last time we explored consumption and usability as an alternative approach to data governance. In that framework, data stewards can measure the quality of the data and alert users about potential risks of using the results, but are prevented from changing the data. In this post we can look at
The third part of my data governance primer series addresses data quality analysis. Don’t even start a data quality analysis until you have completed the first two steps of your root cause analysis: investigate and prioritize any potential causative factors, then start your metadata assessment. Otherwise, you may be misled
The second part of my data governance primer series addresses ways to "mind your metadata." I can just hear the collective groans, and perhaps a stifled yawn. Sorry, but metadata collection is one of those necessary evils that may not be fun, but having it available as a resource to
David Loshin (@davidloshin) on naming conventions for naming things.
David Loshin (@davidloshin) discusses the importance of social media analytics.
David Loshin explains how to integrate MDM with your data warehouse.
David Loshin on analyzing sequences of events.
Read David Loshin's latest blog post: Understanding Sequences of Events.
Read David Loshin's latest blog post: "Innovation in Creating Information Products."