CLOUD COMPUTING

Mining Social Data, the Cloud and a Fisherman's Tale

Salvatore Salamone | Date: 06-29-11 | Comments

IBM hosted New York-area editors at a luncheon June 24 to discuss its IT blueprint for smarter computing. Here are some highlights from the discussions with the invited speakers.

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Mining Social Data, the Cloud and a Fisherman's Tale
Smarter computing can take on many forms, and that was evident at the IBM press luncheon last week in New York. Editors got a chance to sit down with industry experts and IBM customers to hear about their smart computing efforts in a variety of application areas.

Similar to a speed dating event, editors moved from table to table after each course of the meal to chat with the speakers. Here are some tidbits gleaned from the event:

Issues with Mining Social Data: At my first table, Harvey Koeppel, executive director, Center of CIO Leadership, talked about issues faced by his organization�s members. He covered many topics including the changing role of the CIO, new challenges CIOs must address and dealing with Big Data.

During the chat, he noted that more organizations are data mining Tweets and other social networking content. One of the challenges with this type of data mining is that it must be done in a very different way from previous data mining efforts.

In the past, data mining was something done after the fact. Orders would be processed, accounts paid, products delivered and the data associated with these activities would be reviewed at the end of the week, month or year.

The problem with mining social networking content is that the half life of the information is getting narrower. Glowing praise for a product one day is quickly replaced by scorn the next when a problem arises. The implication here is that to get the best value from this wealth of information, the data mining must be done in real time.

A second point raised about mining social networking content is that competitive insights are no longer private. In the past, unhappy customers would flood a company�s call centers with complaints. Competitors would not know about these calls or the problem. Today, many consumers are broadcasting product flaws in Tweets and Facebook posts. So a competitor�s data mining efforts can quickly spot a company�s misfortune and use it to their advantage. Naturally, this works both ways. So companies need to mine data with an eye toward their competitors� products, too.

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