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Challenges of exploring dark data
While you may recognize the immense potential value in Big Data , you also may find it particularly challenging to explore certain forms of semi-structured data such as social media data,Web logs, sensor data, and ot her dark data sources. These new critically important data types traditionally have required a lengthy process to load into legacy analytic platforms and data warehouses before they deliver value. You want to explore, analyze, and shine a light on this dar k data to understand its potential.
Some of your challenges to managing this data include:
- Extreme volumes — Since data volumes are so large in modern enterprise solutions, data should be stored on a scalable architecture that is easy to manage and minimizes data transformation and movement.
- Dynamic schemas — Since the speed of innovation is accelerated today versus legacy systems, modern data structures have data formats that change more often than traditional enterprise data systems. Modern architecture needs to handle today’s dynamic data.
- Licensing costs — Legacy architectures often have licensing models that challenge the cost effectiveness of Big Data analytics, especially when it comes to data whose value is not yet proven. Modern architectures should offer value based on realistic data volumes of today.
- Value of data for analytics — Dark data value is often unknown. Modern analytics platforms should allow users to explore dark data freely to understand its value, without necessarily committing massive resources to it up front.