Why out-of-the-box data integration can increase analytics adoption

A critical lesson many organizations have learned when measuring the success of analytics initiatives is that speed of deployment and widespread user adoption are key factors to consider. These are key benefits of analytic applications delivered with or embedded in ERP systems or business applications.

Analytic applications provide pre-built business intelligence (BI) and analytical capabilities that promise to democratize analytics because most of the hard work in transforming data to insights has already been done. However, the historical challenge these applications faced was that they weren’t built with agile cloud technology or modern data architectures that could keep up with the growing complexity, scale, and changing requirements of the line of business (LOB)-driven analytic requirements.

Building these analytic applications on a modern data architecture enables the speed of deployment, which is derived from pre-built industry and role-specific data models, data pipelines, and content that can easily be extended to an organization’s particular requirements.

This modern data architecture addresses one of the most critical challenges to delivering analytics at speed and scale: an inability to handle all the different data sources and use cases to deliver the analytics required. Most data platforms have evolved slowly over a long time and therefore are not designed to handle more modern information sources, such as streaming data or cloud APIs. Legacy infrastructures force you to fit all your data into predefined data structures, even before you’ve decided how or even if you’ll use it. While this might help answer known questions, it prevents the business from innovating.

With out-of-the-box data integration and management, every transaction can automatically flow to a central data lake for subsequent loading into pre-built, industry-specific, dimensional data models. In addition, data governance is assured by using metadata to correctly collect data, knowing how to interpret the data with semantic information, and understanding where the data comes from and how it was processed through data lineage.

The combination of the data lake and industry-specific data warehouse ensures that all analytic use cases are supported—from answering the known questions to innovative data science that can answer the unknown.

To learn more about how to increase analytics adoption across your organization, read our best practice guide today.