On Demand
What is a “customer” or a “product,” how are these data concepts defined, and how many places are these data concepts inadvertently replicated across the enterprise? Most organizations have many different applications supporting the functional requirements of specific operational processes.
The organically grown application landscape is rife with independent business processes, potentially working at cross-purposes. Aligning functional departmental systems with an enterprise information management strategy exposes opportunities for data sharing and information reuse, as well as improved collaboration reliant on a coherent centralized enterprise information asset. However, despite the approaches used for data extraction and data warehouse population, real time operational activities continue to create, modify, or retire data, leading to increasing inconsistency between data warehouse refreshes.
Sponsored By IBM
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.
Partnering companies have long exchanged data associated with supply chains and financial routing networks, and more recently with online trade exchanges, e-commerce, and business process outsourcing. Many large companies sync data across business units in a similar fashion. Applications for business-to-business (B2B) data exchange have been around for years, and many have been modernized by interoperating with platforms for enterprise application integration (EAI) and business process management (BPM). And they have begun incorporating the tools and techniques of data integration (DI). That’s so the applications can cope with the numerous data standards that are common in B2B data exchange, plus versions and variants of these. DI also gives B2B data exchange the BI, data quality, stewardship, and remediation functions it has lacked. When DI is used in this context, it’s called B2B data integration.
At one end of the spectrum, many organizations initiate data governance programs because of pressing compliance issues that impact data usage. At the other end of the spectrum, some organizations begin by governing data that’s shared broadly through a variety of data integration and application integration technologies. In the middle, transformation is a goal unto itself, as well as an enabler for the goals of compliance and integration. When pulled together, compliance, integration, and transformation are the three pillars that support the average data governance program, as well as represent its primary impetus and goals.
One of the most insidious problems in data warehousing is the proliferation of renegade spreadmarts and data marts. These data shadow systems undermine information consistency, crippling the business and escalating costs thanks to redundant data, platforms, and staffing. TDWI research shows that the average business analyst spends two days a week collecting and formatting data, something that IT professionals are trained and paid to do. This Webinar will explain various strategies for reining in these renegade systems while liberating business analysts to do what they do best: analyze data, create plans, and deliver deep insights.
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.
Data integration has traditionally been associated with the creation of a data warehouse to support strategic decisions. In today’s economy, as companies are striving for right-time information to support tactical decisions and operational performance improvement, having a sound data integration and quality strategy is becoming increasingly important. This view requires an understanding of the business meaning of the data, an understanding of the quality of the data source data, a commitment to addressing data quality issues as close as possible to the point of entry, and quickly gaining concurrence on the data transformation and integration rules. This session enumerates the major issues that need to be addressed from both a business and technical perspective and includes case studies that demonstrate the benefits.