The term “Big Data” is generally used to refer to data sets that have become so large and complex that they have become difficult to process using traditional database management tools. Capturing, storing and mining multiple large data sources for actionable business intelligence that can improve operations is a major challenge.
Data is the fuel that powers today’s successful businesses. The global volume of data is skyrocketing, with new sources being added all the time. And it is not just a matter of conventional business applications. Mobile communications, social networks, and machines and sensors are generating data of unprecedented quantity and quality.
Against this background, Big Data analytics has become the focus of attention. Processing large amounts of complex data (85-90 percent of all bits and bytes are unstructured) from diverse sources—and extracting the relevant information—is the key to turning raw data into genuine competitive advantage. One framework for understanding Big Data is to consider it in terms of the four Vs:
- Volume: The quantity of data to be captured continues to grow exponentially.
- Velocity: Bits and bytes must be up to date and have to be processed quickly.
- Variety: Data comes in many formats and from diverse sources.
- Value: Data needs to be converted into meaningful insights.
A firm grasp of Big Data analytics enables businesses to recognize patterns in structured and unstructured data and gain in-depth insights. This allows decision makers to better understand market developments, trends and business performance.
In order to draw benefits from Big Data and create sustainable value from Big Data investments, companies need to confront the management of an almost unimaginable volume of unstructured data. This would allow organizations to respond quickly to market changes, utilize the latest information on trends and customer demand to develop entirely new services and secure their competitiveness in the long run.
To find out more about tangible ways that companies are leveraging Big Data to create value, visit the T-Systems Big Data Use Cases page for examples ranging from energy providers to the automotive industry.
You can learn more about how to make better use of data by visiting the T-Systems Big Data/Business Intelligence page. If you’re interested in implementing optimized analysis solutions for SAP HANA, learn more about High-Performance Business Intelligence, and if you’d like to learn more about accessing and utilizing large amounts of data go to Emerging Technologies: Hadoop and NoSQL.