![data mashup data mashup](https://slidetodoc.com/presentation_image/cfc2aee9e836c2006369dc78e203d257/image-3.jpg)
Data mashup full#
This goes well beyond the mere act of visualizing or reporting on a data set.īlending Data on Demand and at the sourceĪn enterprise-grade data mashup strategy requires an architected and trusted approach, designed with full knowledge of underlying systems and constraints to utilize the most efficient point of processing. It takes a well-architected, trusted process with IT and business collaboration underlying it to put data blending into production in an analytics-ready format. The most powerful insights come from blending data on demand and at the source of the data. Wherever you’re at, it’s important to understand the benefits and restrictions of different types of data architecture. 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. Yet, without a business goal to achieve, working with big data may just be a “science experiment.” How do businesses drive results? Increasingly, enterprise business issues are best addressed with a blended data approach. This need to blend data to create value will only increase as new types and sources of data and information continue to emerge.
![data mashup data mashup](https://metacart.com/images/mashup-performance.png)
Highlighting the importance of emerging data to the mashup trend, two thirds of companies surveyed are using unstructured data from sources like social media, 65% from Internet of Things or device/sensor data, and 58% from consumer mobile device data such as geolocation and wearables. Indeed, the majority of organizations (52%) surveyed in a 2015 Forrester Consulting study are, on average, blending 50 or more data sources - and 12% are blending over 1,000 sources.