The resurgence of analytics is driven by the availability of data in high volume, variety and velocity, coupled with commoditization of sophisticated data processing algorithms. This enterprise-wide disruption is further fueled by ubiquitous access to cheap computing power and the availability of tools and technologies to integrate the results of analytics into complex business processes.
The proof is in the pudding – enterprises that are proactively adopting an analytics-driven approach enjoy a significant competitive advantage. Conversely, companies slow to adopt an analytics-driven approach run the risk of obsolescence.
New and expanding data sources such as social media data, streaming data, machine-to-machine data, and sensor data offer opportunities to generate previously unavailable insights. However, most organizations find it a challenge to extract such insights due to the sheer volume, variety, and complexity associated with Big Data. Traditional analytics tools may provide too little insight, too late to act upon.
Integrating advanced analytics for Big Data with BI systems is critical to uncovering the actionable insights in Big Data. Using advanced analytics techniques such as predictive analytics, data mining, statistics, and natural language processing, organizations can better analyze Big Data, unearth new insights, and apply them to critical situations. While advanced analytics can help examine the granular details of customer interactions and business operations, BI’s rich reporting features, dashboard visualization, and performance management metrics help make the advanced analytics actionable.
Rubric Hadoop as a Service (HaaS) is an intuitive self service environment for quickly discovering business insights from huge volumes of structured and unstructured data (Big Data) from multiple sources. Our proprietary product’s features are primarily a combination of Extract, Transform, and Load (ETL) and Business Intelligence (BI) tools. It starts with extracting data from custom data sources (internal and external), addition of appropriate business logic to interpret the data, and display of results in a graphical format. This entire cycle is completed in a matter of minutes. Our product can be installed on a physical or virtual server and accessed via industry standard web browsers. It is designed to run on single and multiple nodes. The execution speed can be scaled up or down through the number of allocated nodes.