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Data is growing at a rapid rate. Data engineers and data analysts are constantly struggling to integrate disparate data for insights delivery and decision-making. In most instances, IT integrators take six to 12 weeks to integrate complex, bi-directional customer data streams, leaving little time to focus on more strategic or high-productivity tasks.

Few will disagree that organizations function better and achieve more of their business priorities when using their data strategically. But with a plethora of sources and formats – think ERPs, mobile, CRMs, flat files, and dozens more, consolidating and making use of that information is a hard row to hoe. 

Big data has undoubtedly transformed the nature of business. However, many data strategies fail to provide reliable benefits, because they have difficulty maintaining data quality.

The ability to extract insights determines the quality of an organization’s decision-making. In other words, actionable insights underpin decision-makers. In fact, it’s the insightful information that not only helps companies make confident decisions but also improves their ease of doing business.

The data integration landscape is under a constant metamorphosis. In the current disruptive times, businesses depend heavily on information in real-time and data analysis techniques to make better business decisions, raising the bar for data integration. 

In the current transformative era, organizations worldwide need to interact, transact, and do business with an overwhelming number of trading partners including customers and partners.

Data mapping plays a central role in helping companies fuel their data-driven processes. When an enterprise fails to formulate a robust data mapping strategy, data transformation logic and filtration errors become obvious which could lead to poor data quality and insights delivery, and ultimately decision-making and revenue generation.

Data mapping underpins a companies’ data-driven processes. Without a robust data mapping strategy, data transformation logic, filtration errors become obvious which leads to poor quality data and ultimately decision-making.

Every organization strives for growth. They make efforts and fight for every chance to attract new customers and business. The strategy they use to do that can be different, however.