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Developing a single customer view and efectively identifying users has become a hot topic in the analytics community for two main reasons :
Users demand excellent user experiences on digital platforms , increasingly expecting experiences to be personalized , or expecting marketing and recommendations to be highly relevant . personalized recommendations or tracking behavior to identify top-performing products .
The efectiveness of individual sources of data pales in comparison to a robust data architecture that merges multiple sources to create a single customer view .
The key factor in building a data-driven organization is a clear and thought-through data strategy , led by a data champion with insight into the overarching goals and operation processes of the business .
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This increases the need for efective user identifcation across platforms and over time .
However , analytics operations need to be carefully managed to ensure that they are delivering maximum value for the business as a whole .
When properly managed and structured , data analysis can be a signifcant revenue driver for departments across a wide range of business functions . Data can be monetized directly or it can be used to reduce costs and fuel revenue growth throughout other business units – such as by improving per-user revenue by delivering
Company-wide dashboards and business intelligence tools allow diferent functions to make use of the full scope of the organization ’ s data without individual teams having to buy in multiple separate third-party tools .
As the architects of the engines of digital transformation , heads of data have a big responsibility to deliver digestible , actionable intelligence to the rest of the business , driven by a system that must adapt to the rapidly-changing demands of the modern enterprise .
However , these data champions also have a prized role as the enablers of knowledge , understanding , and
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