Pools of master data present in repositories play a big role in data analytics. For example, data is already re-posited in data warehouses. Example, product data, customer data etc., Big data needs mixed skills. For example - technical skills and some soft skills.
Technical Skills - One being able to administer software frameworks like Hadoop, expertise in databases like noSQL, Cassandra or HBase or in analytics programming languages and facilities like R or Pig.
Soft Kills - Ability of people to think broadly across the organization, to understand the bottom-line needs of the business, to know which analytics questions to pose to get to those bottom lines, and to measure and communicate results. Additional Technical Skills - SAS, Cognos
Mumbai: Three years ago when Shoppers Stop Ltd started its Big Data analytics programme, little did it know that Big Data would lead to big gains. In one of its earliest analytics programmes, the company studied the buying patterns of members of its loyalty programme, First Citizen. Based on the insights, it developed targeted promotions for trousers. This led to around Rs.10 crore worth of additional sales in a three-week period for Shoppers Stop.
After analysing its First Citizen base, the company had observed that not all those who buy shirts also buy trousers. But those who buy both men’s shirts and trousers spend 60% more a year on average than those who buy only shirts, and thrice as much as those who don’t buy men’s shirts at all, said Vinay Bhatia, vice-president, marketing and loyalty, Shoppers Stop.
It integrates data across multiple systems using a high performance parallel framework, and it supports extended metadata management and enterprise connectivity. Powerful, scalable ETL platform—supports the collection, integration and transformation of large volumes of data, with data structures ranging from simple to complex. Support for big data and Hadoop—enables you to directly access big data on a distributed file system, and helps clients more efficiently leverage new data sources by providing JSON support and a new JDBC connector. Near real-time data integration—as well as connectivity between data sources and applications. Workload and business rules management—helps you optimize hardware utilization and prioritize mission-critical tasks. Ease of use—helps improve speed, flexibility and effectiveness to build, deploy, update and manage your data integration infrastructure. Rich support for DB2Z and DB2 for z/OS—including data load optimization for DB2Z and balanced optimization for…
Real estate has traditionally been a game won or lost based on old-fashioned networking and shoe-leather style hard work — deeply dependent on timing, detecting trends and more than a little bit of luck. It may not be that way for much longer, however. Big data is changing the way real-estate professionals, buyers, sellers — and even banks — think about transactions involving property.The following 5 ways Big data helps real estate:Democratizing data for the real-estate consumerBetter understanding communitiesInvestors and banks: Foreclosure and short-sale changesRoles of real estate agentsBoosting pitches The result of Big-data seems "It will become increasingly difficult for individual real estate agents to be visible to consumers, both online and offline, as the consumers follow the larger data sources online," Ref here
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