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Mobile computing offers an unusual opportunity for organizations to innovate with new ways of improving employee productivity, partner and customer relationships, sales and service, and business transactions.
It is a competitive advantage to know more about your customers and to apply this knowledge to marketing, sales, support, and the development of products and services. By gathering together the assortment of big data available to them and applying advanced analytics and data science techniques, organizations can gain a detailed, contextual understanding of customers’ paths to purchase, what types of marketing strategies are most effective, and how customers influence -- and are influenced by -- other customers.
The consistent demand for data quality software and new cloud implementation options indicates that more organizations are considering whether to use the cloud to introduce new data quality software, increase their data quality tool users, save on infrastructure costs, minimize the time to rollout of the tools, and build trust in their enterprise’s information assets—the ultimate goal of data quality efforts.
Data integration and data quality are technical disciplines, but there’s more required than technology. DI/DQ must also coordinate with and align to business processes and goals. The assumption is that businesspeople should be involved in defining quality metrics, standards, and process rules. This report will drill down into the modern best practices associated with emerging data sources, data platforms, and business use cases.
All organizations, no matter how big their budget, must overcome barriers in order to realize value from data faster. Those that have historically experienced centralized, IT-centric BI implementations must transition to flexible environments that embrace the increased use of self-service technologies.
Augmenting the conventional EDW design with Hadoop and Hive can help optimize your EDW by expanding usability, improving performance, improving results, and reducing overall costs.
When numerous diverse data platforms are integrated for multiple use cases, it is called a hybrid data ecosystem (HDE), a concept invented and popularized by Enterprise Management Associates (EMA) in 2012. An HDE provides options for rapidly diversifying data and its business use. Despite the extreme complexity and challenges, users are succeeding with HDEs. This TDWI Checklist Report drills into the data requirements of the HDE with a focus on the role of data virtualization.
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