The increasing complexity of data is putting pressure on organizations to rethink traditional methods of preparing data for reporting, analysis, sharing, and use in business processes. Preparing, blending, integrating, cleansing, and governing multiple sources of data, including big data stores like Hadoop, has traditionally been an IT responsibility. However, broadening interest in data science and analytics has begun to bring non-IT personnel into these activities. If you’re trying to automate and scale your manual data preparation processes, this TDWI Best Practices Report is a must-read. It is designed to help you put in place the right strategy, processes, and technologies to solve data preparation woes. It will also examine the burgeoning field of self-service data preparation, including how the technology fits with visual analytics, data discovery, and BI tools. And, because a big driver behind next-gen data preparation is to enable data scientists as well as business analysts alike to get value out of data in Hadoop, it will take a special look at data preparation in the Hadoop ecosystem. In this data preparation whitepaper you will learn: How organizations can tap innovations in technology and best practices to establish smarter, more scalable, and more coordinated data preparation processes How data preparation supports self-service and reduces the burden on IT, while ensuring governance is part of the process Approaches to handling current data troubles and preparing for future challenges brought on by new data and user requirements Ways to get the data you need faster and increase the level of confidence in data to support strategic, operational, and financial decisions How data prep works in both traditional environments like data warehouses and newer big data frameworks like Hadoop
TDWI Best Practices Report Improving Data Preparation for Business Analytics
