As organisations face growing complexity of enterprise requirements and demands for real-time reporting and visibility, the cost of "hidden" data increases. As the volume of data continues to grow, the variety and velocity of data sources increases, the speed of data accumulation accelerates and the quality of the data is highly variable.
As a result, the broader user-base struggles to access the right data. This creates strategic performance problems – people are making decisions without the ability to access and analyse the right data at the right time. This gives rise to an increasing need to centralize large volume and different types of data into one consolidated data lake using common terminologies and definitions which cuts across various functional departments within the organization.
In today’s IT landscape, cheap storage, computing and new technologies such as Apache Hadoop, Apache Spark, Kubernetes and Kafka™ streaming allows for a cost-effective, high performance data lake to store and process vast amounts of data. With these new technologies, big data architecture can scale in a robust manner even as data volumes grow exponentially. Data can also be quickly ingested, while deferring labour intensive data manipulation; and allowing for processing and clean-up activities until the organization define a clear business need. Newly introduced technology such as NoSQL also means greater freedom for organizations to integrate data more efficiently and without enforcing rigid metadata schemas.
All these technological advancements mean that organizations can now bring most data; whether structured (transactional legacy, relational data) or unstructured data (human data, machine log, IoT data), into a consolidated data lake. Data ingestion can then be curated and real time analysis done by applying sophisticated analytics, such as predictive analytics, video analytics and sentiments analytics etc, for additional insights. Powerful business outcomes can be achieved to provide maximum value; building 360-degree views of transactions and interactions, measuring brand health across channels, improving real-time fleet logistics, improving risk modeling and fraud detection algorithms, and improving quality control on production lines.
For organization eager to embark on revolutionizing their big data analytics strategy and architecture, these expanding set of new technologies can be confusing. NTT DATA Business Solution's expertise and strong partnerships with leading technology partners enables us to confidently advice and provide recommendations to integrate these technologies to form the foundation of a modern architecture for your big data analytics. Combined with vast industry experience and technical expertise NTT DATA Business Solution can help you built the right foundation to enable you to systematically transform raw big data into fit-for-purpose data sets.