I can show you how it will be used in job sectors to give you an example:
Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools. The challenges include capture, curation, storage,[3] search, sharing, analysis,[4] and visualization.
Examples by sector:
General manufacturing
• Predictive maintenance scheduling. Leading manufacturers place sensors around their machines to build an understanding of their usual patterns of operation and then detect frequency changes which indicate that the machine will soon need maintenance. They can then book the machine in when convenient, rather than causing unplanned downtime (which is a big problem) or servicing the machine unnecessarily.
Car makers
• Fault logging and cost predictions. Car makers place hundreds of sensors on components around the car which constantly log data on performance and faults. All of this data can be used to reengineer designs for more efficient products and to predict what the strain of warranty repairs are likely to be on cost and man resource.
Retail and marketing
• Ad retargeting. Online retailers can ‘recapture’ lost sales by retargeting the customer on other websites. When a prospective customer views a particular product on a website but doesn’t go through and make the purchase, a redesigned advert for the product (or one similar) is displayed on other websites that the target goes on to view. This redesign will include techniques to hook the customer that differ from the initial ones that failed to secure the sale, such as different point-of-sale information, pictures or aesthetics.
Finance
• B2B supplier profiling. Finance professionals can use big data to check on the ‘health’ of their suppliers and business partners. They can monitor a variety of indicators including when creditors pay their bills and whether there is any change in the normal patterns, analysed against indicators such as share prices or particular market conditions for the industry sector. They can then use this information to identify which companies might be a credit risk.
• Fraud detection. Companies like Visa are using big data to create fraud detection models which can flag up potential fraudsters. If set patterns are identified (such as spending over $100 at a petrol station followed by taking $200 out of a cash machine after 2am) then the credit card company can immediately notify the customer involved and take the necessary action.
Insurance
• Premium costing. Like the banks, insurance companies use a glut of indicators in determining the cost of their policy premiums. This includes factors relating to the car (the age, performance, security, value etc), the person (age again, driving history, employment etc) and location (where the car is parked, where it is driven, crash rates in the area etc).
Sorry for the long post but hopefully that can give you an idea on what and how it could be used. This is just a few!
---------- Post added at 12:21 PM ---------- Previous post was at 12:01 PM ----------
Ha after reading my post I registered another one! Go figure this is addicting.
Just registered:
BigDataExaminer.com
So now add that to the other two:
BigDataOrganizer.com
BigDataRetrieval.com
