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Ensure that your data is correct, consistent and complete by monitoring, analyzing and reporting on information across your organization.
With powerful data profiling, parsing, cleansing, standardization, and matching capabilities, you can have confidence in your data and the decisions you make based on it.
When you make an acquisition, consolidate the data in disparate systems, or upgrade to a new ERP or CRM application, you want to reap the advantages of this strategic business decision quickly. To do this, you need to streamline and integrate your business processes, but it’s even more important to integrate your data and assure its quality. That’s the purpose of data migration.
A data mart is a subset of an organizational data store, usually oriented to a specific purpose or major data subject that may be distributed to support business needs. Data marts are analytical data stores designed to focus on specific business functions for a specific community within an organization. Data marts are often derived from subsets of data in a data warehouse, though in the bottom-up data warehouse design methodology the data warehouse is created from the union of organizational data marts.
Bill Inmon, one of the first authors on the subject of data warehousing, has defined a data warehouse as a centralized repository for the entire enterprise. Inmon is one of the leading proponents of the top-down approach to data warehouse design, in which the data warehouse is designed using a normalized enterprise data model. "Atomic" data, that is, data at the lowest level of detail, are stored in the data warehouse. Dimensional data marts containing data needed for specific business processes or specific departments are created from the data warehouse. In the Inmon vision the data warehouse is at the center of the "Corporate Information Factory" (CIF), which provides a logical framework for delivering business intelligence (BI) and business management capabilities.
In the so-called bottom-up approach data marts are first created to provide reporting and analytical capabilities for specific business processes. Though it is important to note that in Kimball methodology, the bottom-up process is the result of an initial business oriented Top-down analysis of the relevant business processes to be modeled
View and analyze the integrated metadata to get information on data usage, end-to-end change impact analysis, and report-to-source data lineage. Deliver trusted data for compliance requirements, internal controls and improved decision-making and reduce the Re-developing Cost / time & resources / etc.
Prediction of future outcomes and trends: With its power to predict future scenarios by analyzing past behaviors, AI helps Clients predict future outcomes and trends. This helps Clients to identify gray areas, pattern and make customer recommendations. Aligning AI technology with business intelligence helps in building more informed and secured banking processes.
Count on accurate, timely and trusted information with enterprise-class data integration. Get a 360 degree view of your business with tools that allow you to access any type of data (structured and unstructured) from virtually any source; integrate data using flexible approaches through data federation or extraction, transformation and loading (ETL); and deliver reliable data with the ability to clean your data during the ETL process.