The Challenge Unlocking the Value of Big Data

Big data is here, and it’s transforming the very nature of commerce, enabling new insights, and accelerating the generation of business insights. While the concept of big data isn’t new, its potential is just now being realized as powerful tools to organize, manage, and analyze, immense volumes of enterprise-generated and third-party data finally become available for mainstream use.

However, for many organizations, it’s not so easy to unlock the value in this data. While data volume (the amount of data) and velocity (speed that data is generated) is in part what makes it so valuable, volume and velocity also present significant challenges. Still more daunting is the broad variation in the types and sources of data (variety), including highly structured files, semi-structured text, and unstructured video and audio feeds.

The following picture shows what are the biggest problems that organizations struggle with.


In a recent Gartner study, 49% of organizations reported that they struggled the most with the variety of big data compared to 35% citing volume as their most significant problem, and 16% of organizations claimed velocity was the largest problem relating to big data. Contending with data from multiple databases and systems has always been a challenge but now with increasingly different types of data, the task has become overwhelming. In addition, with data distributed across disparate systems, sources, and silos, it can be a seemingly impossible challenge to obtain a unified, enterprise-wide view of the information available for analysis.

For companies attempting to integrate this onslaught of data in the same manner as was popular 20 years ago—with traditional data warehouse approaches—it is indeed impossible, or close to it. To extract real value from data organizations must ingest and process data from both internal and external sources and perform near real-time analysis—not an easy task. Faced with these challenges, traditional data warehouse solutions cannot keep up with rapidly changing data ecosystems.

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