Big data to create big changes, but only with quality measures
Although big data is generating a massive amount of interest among marketing, healthcare and retail professionals, the idea of having too much information is something humans should be accustomed to, according to Uri Friedman, associate editor for Foreign Policy magazine. Friedman explains that as far back as people started recording data, in Ecclesiastes' time of the clay tablet, humans have been troubled by the idea of having no end to the amount of information that is stored for reference and consumption.
Now, in the time of Apple's iPad tablet, companies and governments are looking to take advantage of the compounding data that's being generated online through ecommerce sites and social media networks, as well as through customers' purchases using electronic payment methods. A survey of 502 IT professionals conducted by Freeform Dynamics revealed that most companies are seeing explosive data growth in both structured and unstructured data, but most still rely on the latter because they're not sure how to extract value from the sets that aren't organized, according to The Register.
When planning how to derive the important insight that will help companies achieve their data-drive[n] goals, it's important to keep quality in mind. Starting with a plan to maintain data quality can keep enterprises from running into common issues relating to parsing and standardization, cleansing, matching profiling, monitoring and enrichment later on, reports Gartner.
Now, in the time of Apple's iPad tablet, companies and governments are looking to take advantage of the compounding data that's being generated online through ecommerce sites and social media networks, as well as through customers' purchases using electronic payment methods. A survey of 502 IT professionals conducted by Freeform Dynamics revealed that most companies are seeing explosive data growth in both structured and unstructured data, but most still rely on the latter because they're not sure how to extract value from the sets that aren't organized, according to The Register.
When planning how to derive the important insight that will help companies achieve their data-drive[n] goals, it's important to keep quality in mind. Starting with a plan to maintain data quality can keep enterprises from running into common issues relating to parsing and standardization, cleansing, matching profiling, monitoring and enrichment later on, reports Gartner.
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