(Business Intelligence), (Analysis and Reporting),  (Data Management)

Business Intelligence    Business intelligence (BI) uses knowledge management, data warehouse, data mining and business analysis to identify, track and improve key processes and data, as well as identify and monitor trends in corporate, competitor and market performance.  Analysis and Reporting    Business intelligence reporting and monitoring includes ad hoc and standardized reports, dashboards, triggers and alerts. Business analytics include trend analysis, predictive forecasting, pattern analysis, optimization, guided decision-making and experiment design. Data Management      Data management ensures data integrity and availability through methodologies such as data warehousing, cleansing, profiling, stewardship, modeling and definition. Effective business decisions rely on data accuracy and reliability. Knowledge Management Knowledge Management methodologies record and disseminate both explicit and tacit process and performance strategies and actions to identify best practices and innovative techniques and ideas.

Getting CRM right means integrating processes both within and across business functions to drive more effective customer interactions and unlock greater customer value. More mature areas such as campaign management, sales force automation, contact center and ecommerce are adding advanced capabilities through analytics, business process management and knowledge management tools. Newer areas such as Field Service, Marketing Resource Management, and Sales Asset Management are broadening departmental capabilities and enabling CRM to reach new heights. Customer data integration (CDI), Customer Interaction Hubs and Customer Experience Management make the relationship visible and customer interactions cohesive throughout the organization. Customer value analysis and customer data mining enable more insightful customer interactions within the context of the interaction.

Master data consists of facts that define a business entity, facts that may be used to model one or more definitions or views of an entity. Entity definitions based on master data provide business consistency and data integrity when multiple IT systems across an organization (or beyond) identify the same entity differently.
In an Internet-based survey that TDWI ran in mid-2006, the business entity most often defined in master data is the customer (74%), followed by products (54%) and financials (56%). Other entities include business partners (49%), employees (45%), locations (41%), sales contacts (25%), and physical assets (21%).
Depending on where and how it’s practiced, MDM solutions fall into three broad categories. Operational MDM is built into and/or used to integrate operational applications for ERP, CRM, financials, and so on. Analytic MDM is prominent in data warehousing, because of the balance between tracking data lineage (to ensure you have the right data) and repurposing data to create new structures (like aggregates and time series). Enterprise MDM is far broader in scope than operational and analytic MDM and—as a discrete infrastructure—may encompass them.
MDM has long been practiced as part of a larger application, as seen in analytic MDM (usually for a data warehouse) and operational MDM (usually for an ERP system). The current trend is to take MDM out of its isolated silos and make it a separate solution, so it can achieve a broader enterprise scope that integrates master data and related definitions across more systems. Today, few organizations practice MDM as a separate solution (20%), although most of those embracing the practice have done so with enterprise scope (76%).
In TDWI’s MDM survey, 83% of respondents reported that their organizations have suffered problems due to poor master data, and 54% claimed to have derived benefits from good master data. Data warehousing and BI issues are deeply affected, with reporting and other BI functions either suffering (81%) or succeeding (54%) based on the quality of master data. For example, when compliance involves reporting, MDM helps to populate reports accurately (to avoid an audit) and to answer questions about data’s lineage (in the event of an audit). But master data also affects other business functions, like customer service, marketing, purchasing, product introductions, and the supply chain. And it assists with business integration issues like mergers, acquisitions, and reorganizations.
A first step in designing a software solution for MDM is deciding whether business entities and their storage should follow a hierarchical, multidimensional, object-oriented, relational, or flat data model. A common struggle early in MDM practice is to get beyond reacting to master data problems (like out-of-sync systems) and start proactively searching for opportunities for improvement (like including more systems in the MDM grid).
As a key success factor, most organizations need business people to be involved in the creation of business entity definitions, if the definitions are to be valid and useful. Likewise, for master data to achieve its goal—consensus-driven definitions applied consistently—it must be shared ruthlessly, which in turn demands a central organizational structure with an executive mandate, like a data governance committee or data stewardship program. These much-needed corrections to how master data is managed have deep ramifications for organizational structures and staffing.

Master data management is about defining shared business entities, like customer, product, and financials.
MDM practices tend to be operational or analytic, but can be both when the scope is enterprisewide.
MDM is cross-functional by nature, so it benefits from a governance organization that fosters collaboration between business and IT.

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(Activity Based Management)

Activity-Based Management   Activity-based management (ABM) is a cost accounting tool applying cost analysis, target costing and management accounting across the organization. Activity-based management (ABM) enables managers to enhance profits through cost control and tracking practices. More 

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