An occasional series in which a review of recent posts on SmartData Collective reveals the following nuggets:
Thinking macro vs. micro on social media
While I couldn’t think of any good ones off the top of my head, a thought about marketing on social media crossed my mind. The ability to allow your brand to take an occasional hit on the micro level, in order that it will have more success on the macro level, is exactly what companies need to do on social media.
—Tom H.C. Anderson: “Allowing Your Brand to Look Bad So It Can Look Better”
Tracking the green
Given that the BI team understands data management and reporting best, they can step up and offer to help develop the supporting metrics for these green IT projects. They can even help consolidate the reporting of multiple projects into the company’s first sustainability report card, where data can be sliced and diced by product, business unit, or geography, and tracked over time.
—Terri Rylander: “BI Team is Integral Part of Green IT Initiatives”
It’s one of those guys
Fortunately for Page, he is already assured of a solid entry in the history books. Because Page’s Law sounds suspiciously like Wirth’s law, pronounced by computer science titan Niklaus Wirth in 1995: “Software is getting slower more rapidly than hardware becomes faster.” In fact, the more precise version cited by Page is known as Gates’s law – though I don’t think Bill Gates wants to take credit for it.
—Daniel Tunkelang: "Page’s Law? Try Wirth’s Law. Or Gate’s”
Those pesky silos
Data silos result in poor quality data that is often inconsistent; wastes resources and time on overlapping and redundant projects; costs more money to build and maintain; and ultimately results in the business not getting information in a consistent, comprehensive and current manner. Data silos also encourage the creation of data shadow systems or spreadmarts, further exacerbating the problems.
—Rick Sherman: “People, Process and Politics - We All Hate Data Silos, So Why do Th...”
The one most likely to
Which of our employees will be the next most likely to resign and take a job with another company? By examining the traits and characteristics of employees who have voluntarily left (e.g., age, time period between salary raises, percent wage raise, years with the organization, etc.), business analytics can layer these patterns on the existing work force. The result is a rank order listing of employees most likely to leave and the reasons why. This allows managements’ selective intervention.
—Gary Cokins: “Fill in the Blanks: Why X is Most Likely to X?”
Don’t ignore your gut
Your knowledge, your understanding of the data will guide you and your intuitive feeling to determine what is right. There is no substitute for it. Intuition will give you that sixth sense and you will be able to differentiate true data issues from false data issues when the tools and processes set in place cannot make that differentiation. Use it when the tools just don't cut it.
—Daniel Gent: “DQ is 1/3 Process Knowledge + 1/3 Business Knowledge + 1/3 Intuition”
It’s in there
But the list of potential applications does not end here: Using a technique called Association Rule Learning (or Association Discovery) we can extract [from Twitter text mining] emotions or thoughts that appear to co-exist and also emotions that seem to be associated with specific events. Classification analysis can also play an important part.
—Themos Kalafatis: “Twitter Analytics: Cluster Analysis reveals similar Twitter users”