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Early proponents of business intelligence focused on specific defined reporting and analysis activities, largely in the strategic arena. This may support senior management needs, but largely ignores the thousands of immediate decisions being made by individuals up and down the organizational chain.
Many CFOs recognize the need to transform the finance department from a group of back-office accountants who focus on cost control to trusted advisors who deliver insights about how to help the organization achieve strategic objectives and goals. Finance is in a unique position to drive strategy because it gathers data from all departments and lines of business.
There is no question that mainframes continue to serve a wide range of organizations by providing a secure, high-performance, and scalable computing platform that’s hard to match on other systems. The issue comes when you attempt to extend mainframe data or applications to participate in new business applications on so-called open systems. Non-relational data, mainframe COBOL programs, and 3270 screen-based applications are difficult to access from open systems, and this inhibits modern data-driven business practices, like 360-degree views, on demand performance management, just-in-time inventory, business intelligence, and so on.
As business conditions change, enterprises need faster time-to-value and shorter deployment cycles for their decision making platforms. Hence the question in IT is shifting from how to build a data warehouse to how to speed delivery of insight and how to meet new requirements without breaking the bank. Although many organizations have collected terabytes of information, most still don’t have a cost-effective infrastructure for transforming this data into actionable insights.
According to a recent TDWI survey, 38% of organizations surveyed are practicing advanced analytics today, whereas 85% say they’ll be practicing it within three years. This dramatic increase in advanced analytics is driven by organizations’ need to understand constantly changing business environments, as well as to discover opportunities for cost reductions and new sales targets. There are different analytic methods users can choose as they move beyond basic OLAP-based methods and into advanced analytics. The majority, however, seem to be choosing SQL-based methods, because they know and trust SQL, plus they can leverage the SQL-based tools and skills they already have.This trend is pushing SQL-based analytics to a new extreme. With “load and go” methods, users quickly load a few terabytes of raw operational data and go at it with ad hoc queries until the data reveals the answers they need. The ad hoc queries get more complex with each iteration by a business analyst or similar power user. This method doesn’t allow time and resources for data transformation, cleansing or remodeling, so users compensate with lots of WHERE clauses, table joins, and temporary tables (when necessary). SQL-based analysis at this advanced level is powerful, but it only succeeds when supported by an analytic database management system that can quickly execute extremely complex SQL statements run against multi-terabyte volumes of raw data, in a schema-neutral fashion that supports “load and go” practices.
Event driven analytics is about real-time response to complex events discerned in high-volume data and message flows. It’s about augmenting BI and predictive analytics programs with Complex Event Processing (CEP), transforming information from data warehouses, data streams, and services linked on an enterprise service bus (ESB) for business activity monitoring (BAM) and automated action. This webinar will introduce data streams and CEP in the enterprise analytics context. It will show how CEP offers new analytics capabilities in conjunction with enterprise BI and data warehousing programs.
This Webinar will explore a host of game-changing technologies that are helping business analysts gain greater insights into the massive volumes of data increasingly at their disposal. These include in-memory analytics, advanced visualization, event-driven and in-database analytics, appliances, BI search, and specialized query and analysis tools. It will also show how many of these technologies are accessible to casual business users, who can now navigate complex data sets without IT assistance. Collectively, these new technologies are making it easier for organizations to leverage information to optimize performance, manage costs and profitability, and gain a competitive advantage.
In today’s world of electronic commerce, virtual storefronts, and online exchange, it is easy to forget that participants in every business transaction are situated in some physical location. While data enhancement is not new, the growth and the quality of geographic data and the capabilities for spatial analytics has democratized the accessibility of geographic information processing. A by-product of the way that e-commerce has simplified business transactions is a need to extract knowledge based on geographic information and exploit it to drive efficiencies and profitability. Since many decisions are driven by geographic information, there is a great opportunity to see how geographic BI can improve business analytics.
What are Business Intelligence strategies and solutions that may help companies not just survive, but thrive during the economic downturn? The current climate has caused a crisis of confidence for all areas of the business – but smart companies and teams are taking their opportunities to gain a new competitive advantage in customer retention and service, maximize revenue opportunities, and reduce risk exposure.