Process of selection of BI tools requires tremendous effort as it can be perplexing to select the tools to best serve the needs of the enterprise. IBIM™ lays down a process for BI tools selection based on the industry best practices as the use of these practices streamlines the selection process.

Adoption of best practices by IBIM™ helps you in mitigating business risk and managing effort.

Considering the importance of BI initiatives for the enterprise, executives are spending larger portions of IT budgets on BI. Increase in spending is due to urgent requirement to deliver consistent, accurate, and trusted information to all stakeholders to meet the business goals. Often the selection process is launched without a methodology and guidance to realize later that the selection process is in a state of chaos. IBIM™ best practices enable you to meet your schedule and budget.

IBIM™ enables enterprises to:
  • Define and classify business, functional, and technical requirements
  • Categorize requirements as essential, important, and desired
  • Evaluate the functionality, features, and the fit
  • Assess vendor capabilities, framework, stability, and support
  • Evaluate professional services, including consulting and education
  • Leverage Gartner, Forrester, and The Data Warehousing Institute (TDWI) etc. research
  • Assess licensing and support cost models
  • Go through the vendor selection process to make an informed business decision

Processes and methodologies based on proven approaches help the enterprise manage risk, schedule, cost, and effort.

BI tools selection has less to do with the features and more to do with the fact that selected tools can deliver on the specific BI requirements of business.

IBIM™ broadly refers to concepts and technologies used to analyze the information in enterprise. Existence of multiple disparate datasources and systems complicates the BI environment.

IBIM™ Selection process also takes into account the existing infrastructure of the enterprise to enable the best use of existing capabilities. The idea is not to implement BI software with cutting edge features but to provide a collaborative environment enabling all users to work towards the common goals of the enterprise. Instead of comparing the products from BI vendors for the rich technology features, the focus by IBIM™ is to evaluate the tools and technologies to see how effectively the tools can deliver to achieve the enterprise goals.

Data governance

IBIM™ ensures that you achieve the goals of increasing confidence in decision making, making the data universally visible throughout the enterprise, and instilling confidence in users across enterprise that the data is accurate. IBIM™ Data governance provides for an enterprise-wide data governance body, a policy, a set of processes, standards, controls, and an execution plan for managing the data. It promotes data quality, data integrity, data consistency, data timeliness, data security, information privacy, and thus increases the information usability and reliability. It provides a framework to create a consistent and methodical approach towards managing the data across the enterprise.

’Any time data crosses an organizational boundary, it should be governed, whether you’re sharing data among business units internally or publishing data to customers, partners, auditors, and regulatory bodies externally.

Organizations are under renewed pressure to ensure that compliance and accountability requirements are met as the scope of data integration broadens.’3 Data governance should include identification of data stakeholders, such as data owners, data stewards and their roles in handling enterprise data assets. These individuals in data governance council provide for how the data is created, collected, processed, manipulated, stored, made available for use, or retired. Data governance comes into play as these activities require stakeholders from various functional areas to take decisions according to a set of defined processes. IBIM™ Data governance program encourages the understanding and management of the data from both business and technical perspectives, plus it promotes the importance of the data as a valuable resource, allowing the enterprise to use the data confidently to satisfy business needs.

Data architecture

IBIM™ BI strategy incorporates data architecture as it transforms abstract data models to logical business entities and subsequently leads to implementation of physical data models.

IBIM™ ensures:

  • Detailing the subjects into atomic level data and then composing the desired form using atomic level data during definition phase.
  • Data models for the subject areas of the core functions of the enterprise are defined.
  • Conceptual, logical, and physical data models are drawn to provide the foundation for overall data architecture goals. Conceptual data model lays out business entities and their relationships. Logical data model defines detailed attributes of business entities. Physical data model provides for the actual implementation of logical model.
  • Define and document the data architecture goals, assumptions, and constraints surrounding enterprise data architecture.
  • Document the guidelines detailing usage of the data modeling techniques, establishment of atomic level of the data, significant components of the data architecture, and appropriate security measures as part of the BI strategy.
  • Addresses technical and nontechnical issues surrounding better data collection, usage, and governance.
Data integration

Data integration is a major component of the BI strategy as it refers to data assets, processes, methodologies, tools, and philosophies of the enterprise by which fragmented data in multiple disparate systems is integrated to support business goals..

IBIM™ optimizes the data integration process by documenting it, making it repeatable, easy to define, and easy to use.

Data is integrated to deliver useful information to enable better business decisions. IBIM™ adopts several strategies to achieve data integration for a given business purpose. Broadly, the strategies are determined as using virtual data federation, virtual data marts, virtual operational data stores, web data services, relational views, physical data warehouses, physical data marts, and physical operational data stores.

Most common of the data integration approaches use ETL, EAI and EII. ETL solutions read the data from a set of data sources, transform the data to the target form, and subsequently move that data to a target data store. EAI is sharing the data and processes among the various applications in the enterprise while keeping the changes to the existing applications at a minimum. EII uses data abstraction to present a single integrated view of the business. EII makes the data from multiple disparate data sources to appear as coming from a single datasource. IBIM™ strategy also aligns with enterprises that are using MDM along with these approaches to deliver consistent data enabling ‘one version of the truth’.


Metadata roadmap is an essential part of the IBIM™ strategy as metadata explains how, why, and where the data can be found, retrieved, stored, and used in an information management system.

IBIM™ metadata strategy enables productivity improvements by helping with the data lineage, reduction in data redundancy, better understanding of how the information is used in the enterprise, impact analysis, better use of the data in the enterprise, information sharing, knowledge transfer, navigation of the corporate data assets, inventory of corporate data assets, and identification of data discrepancies and overlap. Technical metadata provides for the data lineage and impact analysis. It should include the data for all data integration, data modeling, data profiling, data quality, database, reporting, analysis, usage, and monitoring processes. It should include source system information, entity and attribute definitions, system usage information, and an understanding of what information is fed from BI to other systems. Business metadata provides context to the data and thus it makes the meaning of the data explicit and provides definitions of data elements in business terms from the business point of view.

IBIM™ enables enterprises to trace the data as it flows from data entry systems, transactional systems, data-staging environments, data warehouses, and data-marts to the means of Information delivery used for business analysis. Metadata enables the tracking and monitoring of the data through the entire data flow.

Data quality

IBIM™ delivers complete and consistent data lays which are the true foundation of the successful BI environment

IBIM™ emphasizes data quality and this emphasis is continued throughout the entire lifecycle and through all iterations.

IBIM™ data quality initiative includes:

  • Setting up data governance council.
  • Defining roles of people in the enterprise to handle data.
  • Building consensus on definition of data.
  • Establishing framework to deal with and resolve the issues with data.

IBIM™ Data quality initiative provides complete, consistent, and accurate data.

Collaborative Approach

Use of knowledge management, content management, and portals is the key to sharing information in a collaborative environment. The enterprise BI purpose should be to bring everyone together to work towards the common goals of the enterprise. IBIM™ strategy emphasizes on the integration of BI with the overall knowledge management environment of the enterprise. IBIM™ ensures all components of end-user information delivery are addressed in BI strategy. To name a few, it could be the use of standard reporting, ad-hoc analysis, OLAP cubes, dashboards, scorecards, notifications, and use of semantic layer, budgeting, planning, forecasting technologies, etc. The purpose is to provide users with action-oriented information and analysis capabilities in a collaborative environment.