Tuesday, January 22, 2013

Business Data Analytics Trends



 Business Data Analytics Trends

“To innovate, users need to understand the factors that affect efficiency, success and failure- they can’t improve what they don’t understand.”

Over the years business analytics is used in strategic decision making. But today, there is a major change in business data analytics trends. It is now used to make day to day business decisions and manage day to day business operations. 

Areas where Business Analytics led to better business decisions:
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·         Real time Fraud Detection
·         Web Display advertising
·         Call center optimization
·         Intelligent traffic management
·         Smart power grids
·         Sustainability
·         Bio-informatics

In past, business analytics was performed mainly on structured data i.e. the data in warehouses but now with the advancement in internet, social media and social networking, companies are interested in mining unstructured data from web, blogs , wikis etc. to see how people feel about their product and services.

Few Business Analytics Tech Trends

Big Data:

Big Data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. 

If we talk about 1960s, gigabyte of data was handled. In 1970s and 80s data was in terabytes and relational technology was used to support universal systems like ATM, phone etc. But now the data is in petabytes. To handle such large amount unstructured data there has to be some new ways. One of the most talked about Big Data technologies is Hadoop, an open-source distributed data processing platform originally created for tasks such as compiling web search indexes. It's one of several so-called "NoSQL" technologies (others include CouchDB and MongoDB) that have emerged to organize web-scale data in novel ways.[2]

Smarter Analytics and Faster decisions:

Today, the business analytics is not just used to react to business events but also to predict them as well. More data is analyzed and it requires more advanced type of processing. Faster decisions are required in areas like real time fraud detection and in stock markets.

The capacity of today’s computers to process much more data in memory allows faster results than when searching through data on disk. Columnar database servers, which invert the traditional row-and-column organization of relational databases, address another category of performance requirements. Instead of reading entire records and pulling out selected columns, a query can access only the columns of interest-dramatically improving performance for applications that group or measure a few key columns.

Budget:

Cost is a very important factor for any organization while planning its strategies. The prices of memory and storage are falling every year. Apart from this the analytics are benefitting from the open source software that provides an alternative to commercial products and puts competitive pressure on pricing.

Ternent is an open-source evangelist. Prior to joining Island One, he was vice president of engineering for Pentaho, an open-source business intelligence company, and worked as a consultant focusing on BI and open source. "To me, open source levels the playing field," he says, because a mid-sized company such as Island One can use R, an open-source application, instead of SAS for statistical analysis. Once, open-source tools were available only for basic reporting, he says, but now they offer the most advanced predictive analytics.[2]

Although the price of storage is dropping every year but the consumption is growing at a much faster rate. Some methods must be applied in order to reduce the cost pressure on Information Technology. Techniques like Agile PI, Self Service PI should be used to make business users more self-sufficient to reduce the load on IT so that analytics can be deployed faster at a lower cost.

Social Media:

Almost everyone has an account in many social networking sites, like facebook, twitter etc. Companies want to analyze data generated by these social networking sites. To support statistical techniques new analytical applications like natural language processing, sentiment analysis and network analysis have emerged. Many social media tools are also available. One of the prominent tools is Raidan6, recently purchased by salesforce.com. It presents a dashboard of brand mention- tagged positive, negative, or neutral- based on Twitter feeds, public Facebook posts, posts and comments on blogs and discussion board conversations.


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