Importance of data increases upon the usage of data. It is said that customer relationship management system is only as useful as the quality of the data it contains. Good data or high quality data can provide the most recent picture of a customer. In contrast, bad data i.e. data that are of low quality are incorrect or inconsistent. Good data is important for any company because it provides you with important information and you can use to personalize customer experience, improve customer satisfaction, and gain customer confidence, while bad data interfere with your ability to meet customer needs, therefore creating a divide between you and your customers. They are: decreased confidence; lost opportunities; and missing the big picture. Read more at: http://it.toolbox.com/blogs/insidecrm/3-ways-bad-data-hurt-customer-relationships-69607

According to Darian Shirazi (founder of Radius and a Forbes contributor), marketers measure success based on the dollar value of pipeline they are able to generate in order to increase revenue. The marketers, who can reach their goals, work closely with their sales team who can find appropriate customers. The ones who struggle to reach their goals are not aware of their sales team's ability to close deals. To highlight pipeline, and to find customers, marketers need to understand deeply how to market properly. Hence marketers today, have access to new data sources, superior analytics, and better integrations that allow them to generate high value pipelines. Read more at: http://www.forbes.com/sites/darianshirazi/2014/09/16/marketing-automation-2-0-marketing-intelligence/

According to Jayakanthan Chidambaram (Associate Technical Architect, Aspire Systems), sustaining in the market with a persistent growth is considered to be one of the greatest challenges in any industry. Companies are focusing their investments on business intelligence technologies in order to predict behavior and consequences from patterns found in large volumes of data. Predictive analytics transforms data into important and useful information. Data analysis can be broadly categorized into 4 types - Descriptive, Simple Statistical Summations, Prescriptive and Predictive. Personalization based on location, behavior and preferences are driving how organizations should cater to customer needs and grow their business. When organizations adopted Business Intelligence, they took the first step towards understanding what is currently happening in their business. Now is the time to take that maturity to the next level where the need is not only to know what is happening but also be able to predict what is about to happen. Read more at: http://www.informationweek.in/informationweek/perspective/298003/predictive-analytics-future-business-intelligence?utm_source=referrence_article