Data Management Analytics - Introduction to Data Mashups and Data Blending
Over time, we have seen how increasingly important it is for organizations to bring data from disparate sources and data types together in an analytics-ready fashion. A recent IDC survey shows the increasing complexity of data management: by 2020, over 80% of organizations will be integrating ten data sources (or types), up from (on average) six today.1 This need to blend data to create value will only increase as new types and sources of data and information continue to emerge.
Without a business goal to achieve, working with big data may be a “science experiment.” How do businesses drive results?
Increasingly, enterprise businesses can best address their challenges with a blended data approach. For instance, a telecom company might blend semi-structured network data with customer service data to understand the relationship between dropped calls and customer behavior across geographies. Wherever you’re at, it’s important to understand the benefits and restrictions of different types of data architecture.
The most powerful insights come from blending data on demand and at the source of the data.