Hybrid Enterprise Data Lakes Provide Foundation for Disruptive Business Intelligence_161116.cdr

Enterprises are looking beyond traditional data warehousing practices to fulll their business intelligence (BI) requirements. As the need to make accurate and timely decisions increases, enterprises seek real-time access to structured and unstructured data from multiple streams and logs. A growing number of enterprises are exploring cloud and Big Data platforms to address this need. Moreover, since structured data warehouses and relational database management systems (RDBMS) are not enough to process large amounts of varied and unstructured information, businesses are looking to leverage hybrid data lakes, which are a combination of enterprise data warehouses and data lakes. Data lakes combine data existing in silos to improve information use and sharing, while lowering the overheads through reduced server and licensing costs. The Need for a Fresh Perspective on Business Intelligence Real-time decision-making is vital to achieve customercentricity, improve brand image, and gain a distinct competitive advantage. This makes business intelligence (BI) and analytics a priority for C-suite executives across organizations. In today's information economy, enterprises need a scalable environment that helps them efciently manage growing volumes of data, as well as handle diverse types of information needs. The ndings of the TCS Global Trend Study 2015, 'Internet of Things: The Complete Reimaginative Force'1 emphasize that businesses must be able to gather, process, and analyze huge amounts of digital data to realize the true potential of the Internet of Things (IoT). The speed of information retrieval is also critical to effectively leverage Big Data, compelling enterprises to embrace a new perspective on BI and analytics. Adopting Next Gen Business Intelligence and Analytics Data lake is a concept that has gained increased traction in recent times. It breaks down data silos, helping business analysts, data scientists, and engineers gain useful insights from the customer interaction data. The hybrid data lake, which is a combination of data lakes and data warehouses, offers several advantages over traditional data warehouses. Enterprises using the data lake can capture data from multiple customer touch points for in-depth analytics on customer psychographics and demographics. In a data warehouse environment, enterprises derive only descriptive and diagnostic analytics from the structured operational data stored. Further, the development time needed to introduce a new data set to an enterprise data lake is much lesser than that needed for a warehouse . This ensures that the required data is available for analysis and reporting within acceptable timeframes. Data warehouses are also more expensive to maintain than data lakes owing to the high cost of hardware and licenses. Additionally, the hybrid nature of the data lake allows easy and cost-effective analytics since summarized views from various data lakes can be seamlessly uploaded to the cloud platform for anytime-anywhere consumption by business users. WHITE PAPER HiTech