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SmartData Collective > Big Data > Data Mining > #13: Here’s a thought…
Data MiningPredictive Analytics

#13: Here’s a thought…

brianfarnan1
brianfarnan1
6 Min Read
#13: Here's a thought...
Illustration generated with FLUX.2 [klein 4B] via Cloudflare Workers AI.
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An occasional series in which a review of recent posts on SmartData Collective reveals the following nuggets:

Cloudy days ahead
The question is not, will cloud computing happen, but rather, how will this tendency unfold, and how will organizations, regulators, and other actors respond? Until the rhetoric and more important the base of experience moves beyond the current state of pilots and vaporware, the range of potential outcomes is too vast to bet on with any serious money.

—John Jordan: “May 2009 Early Indications: Clouded Over”

Back to the drawing board
But I can’t say I’d be thrilled. I’ve only had a short time to play with Bing, but I’m not overwhelmed. In fact, I’m quite disappointed, given their big talk about delivering a “decision engine,” I expected at least a little bit of innovation in the user experience. No such luck, The focus is still on the ranked list, and their ranking is, at least to my taste, perceptibly inferior to Google’s. I could live with that small difference if the interface offered real opportunities for interaction. But there isn’t anything new there.

—Daniel Tunkelang: “Banging on Bing: A Bummer”

It’s always the customer
Customer service will remain the priority area for IT investments during the coming year. This is for good reason, as evidence mounts from several sectors that customer loyalty is eroding and customer churn increasing. Information management will also be high on the priority list, especially when it comes to projects designed to improve firms’ understanding of customer behavior.

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The Dark Matter of Data
—Timo Elliott: “Economist Research: Decision-Making in Turbulent Times”


The growing world of analytics
Risk management is one of the largest component of the analytics industry today, and it is the pioneer component, too. The market is huge in the U.S. and Europe; Asia Pacific is coming up fast and it is yet to get full swing in China and South Asia, including Nepal.

—Bhupendra Khanal (in a Romakanta Irungbam post): “Analytics: Reality and the Growing Interest”

Sort it out
In Master Data terms, IT understands the data architecture and the interdependencies. They know all the transactions required to enter data into the system, and what security roles are in place to limit access to those transactions. IT also has tools and knowledge on how to extract data from the database and batch import data en masse. IT knows the what, when, and how of Master Data.

—James MacLennan: “Who Owns Master Data in Your Company?”

Help management lead
My suggestion to middle managers who have become passionate advocates of applying business analytics is to start educating your leadership team. Help them lead. Leadership is their primary role and responsibility. Executives lead by both communicating their vision to employees and inspiring employees. My advice to middle managers to influence your executives is to be frank and open. Do not fear that executives will reject your ideas. Create pilots and test experiments that demonstrate the power of business analytics.

—Gary Cokins: “Discovering Analytics: A Revelation or Slow Investigation?”

Make those filters accessible
Given the importance of filters to most information applications, it is surprising how often the interface makes them hard to find. As I mentioned in an earlier post, the failure of many analytical and reporting applications is that “they assume users know precisely what they need before they’ve begun the analysis.” Filtering shouldn’t be a one shot deal; the functionality should always be accessible.

—Juice Analytics: “Five Features of Effective Filters”

Do the smart thing
Smart means applications that handle lots of data, use personalized data, focus on predictive analytics and enable time critical decisions. Today they see problems with software choking on large data volumes, applications being hampered by unclean/incomplete data, limits to accuracy and simulation, and days of processing time for complex problems.

—James Taylor: “First Look – Pervasive DataRush”

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