Enabling Self-Service Analytics with Intelligent Data Integration
TDWI Speaker: Philip Russom, TDWI Research Director
Date: Wednesday, October 5, 2016
Time: 9:00 a.m. PT, 12:00 p.m. ET
One of the strongest trends in information technology (IT) today is self service, which puts the power of creating data-driven solutions in the hands of the business user. This way, IT organizations are offloaded; they needn’t create unique datasets, reports, and analyses per user, which frees up IT’s time for other tasks. Furthermore, a broad range of end-users – mostly mildly technical business people – needn’t wait for help from IT, thereby giving them greater agility and creativity, while reducing the time to value and allowing them to apply their business expertise to a well-targeted solution. Therefore, self service is a win-win situation – but only if key pieces of technology are in place.
For example, the hardest part of self-service analytics, for the business end user, is data integration, due to the complexity of data sources and structures, plus the challenges of unifying diverse data into a single dataset for analytics. To get full value from self-service analytics, organizations need a data integration platform that can ingest any data type, from any source, of any quality. The platform should operate in real time and at scale, even with large and complex data. However, a leading-edge platform will also have machine intelligence in the form of algorithms and methods that automatically detect, model, ingest, cleanse, reconcile, transform, match, correlate, analyze, and present data – thereby taking these complex tasks off the plate of the business end user, so he/she can focus on creating effective analytics in an autonomous, self-service fashion.
Many of today’s data integration approaches are designed for an older time, with older data and time frames, whereas modern business users need alternative approaches, if they are to achieve self service, agility, and impact via analytics. Join Philip Russom, senior director of research at TDWI, and Andrew Miller, senior sales engineer at Bit Stew Systems for an in-depth look at how data integration drives advanced analytics to bring greater intelligence for your organization.
What You Will Learn:
- New data integration best practices for self-service analytics
- What features to look for in next generation analytics and data integration platforms
- How an integrated analytics and data integration platform with machine intelligence can make successful self-service analytics a reality for many users
Philip Russom, Ph.D.