Data Mining Guest Post: Eric Greenwood
Today, Eric Greenwood, expert in data storage, is our guest blogger on Data Mining Research. Thanks for his post and feel free to comment about his input.
Data Mining, Refining, and Storage
As companies are able to more effectively store and retrieve information via online storage services, the relevance of data mining as the crucial first step towards intelligent business decisions will become an important tool that was previously unavailable for small and medium-sized businesses and may usher in a new era of success for those who can afford to make the change.
Online Storage: Addition by Subtraction
The effect on large and small companies of using services that provide online storage should be evaluated by looking at what the process takes away rather than what it necessarily adds. More and more companies are finding themselves practically bombarded with the exponential growth of data that is available about their customers, provided in no small part by the increasing number of internet-connected sensors that people own—smart phones, tablets, laptops, to name just a few.
Just consider this snippet taken from a white paper produced by Globalknowledge, a leader in IT and business skills training:
“According to International Data Corporation (IDC), ‘The proliferation of devices, compliance, improved systems performance, online commerce and increased replication to secondary or backup sites is contributing to an annual doubling of the amount of information transmitted over the Internet.’”
Indeed, a veritable ocean of information has been created, and its unexplored depths contain the potential for game-changing discoveries. Unfortunately for medium-sized businesses, or even larger businesses that find themselves underprepared, the challenges of collecting their relevant data efficiently and storing it securely are enough to keep them too busy to participate in this feeding frenzy of information. That’s where the developing industry of online storage comes in.
Online storage as a service is a young industry, and as with any young industry there are still some very real customer concerns that need to be addressed before it can be widely accepted as a solution. Specifically, those who wish to provide online storage to customers at a large scale will need to bridge the security gaps that are still associated with it; however, as those issues are resolved with time, purveyors of online storage services that position themselves to accommodate this massive influx of data from small to medium-sized businesses may find that they have customers knocking down their doors.
The introduction of online storage as a service to small and medium-sized businesses that are having difficulty managing their data has a more profound effect on their bottom lines than it might initially seem. That’s because rather than spending too much time trying to manage data, companies can shift their primary focus toward understanding it, which is a monumental task unto itself.
If the process of local storage and management was a difficult burden, then the process of knowledge discovery required to understand it is a seemingly insurmountable one—how can any person possibly hope to make sense of petabytes of information? The answer to this question, and the key first step toward vital business intelligence for curious companies, is data mining. As an automated process, data mining can help companies identify patterns in their data that can be analyzed in detail and used in predictive or product-oriented capacities.
In a dark room, data mining is like emergency lights that come on and provide much needed guidance and direction. Without the benefit of online storage to relieve the mounting pressure from data overload, though, small and medium-sized businesses are in danger of being too busy to see the light.
Eric Greenwood is a technophile whose interests span the range of data management, online storage, business intelligence and much more – Read more of his work at the blog Online Storage!
Data Mining Book Review: Decision Management Systems
I recently read the last book from James Taylor, Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics. As a data miner, I was interested by the subtitle of the book. Although, the book is really well written, I'm a bit disappointed regarding the content for someone in analytics. I was expecting real methodologies and examples… Continue reading... | 6 CommentsNew data mining blogs
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Data Mining Research in 2011
I would like to thanks readers, subscribers and advertisers on Data Mining Research for 2011. Just a little bit of blog statistics for Data Mining Research in 2011:
- More than 67'000 visits
- Around 110'000 pageviews
- Top 10 challenging problems in data mining
- List of blogs
- Top five articles in data mining
- Standardization versus normalization
- Data miners
Data mining readings
Here are a few interesting readings I have come across recently:- Where to Begin with Predictive Analytics: James Taylor explains that a single successful project may be enough to justify the need for analytics in a company. In his article he explains that the best place to start analytics in a company is with operational decisions.
- Can Machines Think?: Robert Wright wrote this article 15 years ago and it's very
BAQMaR 2011: Feedback
I presented at BAQMaR 2011 Creative Ways Conference in Ghent (Belgium) last week. The conference was about marketing research and marketing analytics. My talk was in the data mining track: Personalized Online Advertising using Data Mining. I presented the work I did when I was a consultant at FinScore. The conference was a clear success! Very dynamic audience. Also very easy to interact with keynote speakers and BAQMaR organizing committee compared… Continue reading...Data sampling for association rule mining
In certain situations, the data miner has to perform sampling on the dataset before applying any algorithm. The main reason being too many data to mine. In such a case, a possible technique is random sampling. If classes are uniformly distributed, one may use random sampling before supervised learning. But what about association rule mining? If you use random sampling before an association rule algorithm, you may end up finding no… Continue reading... | 7 CommentsData Mining Research
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