IMPROVING DATA PREPARATION FOR BUSINESS ANALYTICS

January 15, 2019

Business users want the power of analytics—but analytics can only be as good as the data. The biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation. The increasing volume, variety, and velocity of data is putting pressure on organizations to rethink traditional methods of preparing data for reporting, analysis, and sharing. In the recent TDWI Best Practices Report: Improving Data Preparation for Business Analytics, we take a look at experiences with data preparation, discuss goals and objectives, and uncover how to take advantage of the rapidly evolving technology and self-service capabilities that allow business users to realize insights from data faster. Here are several of the key survey results.

Spotlight

Kloutix Solutions Pvt. Ltd.

Never before in the history of consumerism, has the power of decision making shifted so radically into the hands of the consumer. With social media and mobile becoming mainstream, gone are the days when any business could work solely on the traditional models of business management. Today agility and consumer responsiveness has to be high and every aspect of the business has to be constantly improved significantly. This leads to an expansion in the role of analytics across functions, business units, departments etc. making it an organization wide decision making discipline. A large part of intuitive and experiential decision making has to be replaced with scientific methods of outcome prediction.

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Spotlight

Kloutix Solutions Pvt. Ltd.

Never before in the history of consumerism, has the power of decision making shifted so radically into the hands of the consumer. With social media and mobile becoming mainstream, gone are the days when any business could work solely on the traditional models of business management. Today agility and consumer responsiveness has to be high and every aspect of the business has to be constantly improved significantly. This leads to an expansion in the role of analytics across functions, business units, departments etc. making it an organization wide decision making discipline. A large part of intuitive and experiential decision making has to be replaced with scientific methods of outcome prediction.

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