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New technologies often change market dynamics, making it possible for organizations to address business needs in new and creative ways. Today, open source software, analytic databases, and other new technologies are enabling BI teams to deliver new applications that previously weren't possible in a cost-effective way.
What is analytics? What can it do for my company? Who performs analytics? These are some of the questions we’ll address in this Webinar to provide the context for understanding analytical tools, technologies, and processes. We’ll then take a deeper dive into exploratory tools and techniques that organizations are using to better understand and communicate patterns and trends within data and take appropriate business action.
This Webinar will review the fundamentals of analytics addressed in part 1 and types of exploratory tools that have formed the backbone of analytics for many years. This Webinar will take the next step and introduce more advanced analytical tools, including text mining, predictive analytics, and event-streaming, that promise to deliver oversized business benefits by generating actionable insights that can give companies a competitive advantage. The session will also discuss how to ramp up an analytical practice and the challenges organizations face when implementing predictive analytics and mining capabilities.
Collaborative data integration is a collection of user best practices and software tool functions that foster collaboration among the growing number of technical and business people involved in data integration projects and initiatives.
Usage rates for BI tools have nudged up from 18 percent three years ago to 24 percent today, according to TDWI Research. This abysmally low percentage accounts for most of an organization’s power users and a handful of very determined casual users. What can you do to make BI more pervasive?
New technologies often change the rules of the game, making it possible for BI teams to address business needs in new and creative ways. BI teams that understand how to harness the power of the cloud, open source, virtualization, and high-performance analytical databases can create new opportunities to serve the business while saving money and time.
A data model can communicate Business Intelligence (BI) requirements at different levels of detail, depending on the needs of the project team member. Developers require more detail than business users, for example. Building data models which connect business need to business solution and then to technical solution dramatically increase the likelihood of a successful BI program. This webinar explains how data modeling can lead to better BI by capturing analytical requirements at the logical, structural, and physical levels of detail.
Putting data into a database and getting it back out are surprisingly different operations, despite the fact that both rely heavily on the capabilities of a vendor’s database management system (DBMS). Because these are two distinct “database workloads,” the common approach for many years has been to provide separate DBMS instances and server/storage hardware for application databases and data warehousing, each instance modeled and optimized for its primary workload. Yet, there are good reasons why some user organizations should consider consolidating the two database workloads onto a single database platform.
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