Enterprise Modeling for Business Intelligence

Abstract:

• • Business Intelligence (BI) software aims to enable business users to easily access and analyze relevant enterprise information so that they can make timely and fact-based decisions.

  • However, despite userfriendly features such as dashboards and other visualizations, business users still find BI software hard to use and inflexible for their needs.

  • Furthermore, current BI initiatives require significant efforts by IT specialists to understand business operations and requirements, in order to build BI applications and help formulate queries.

  • In this paper, we present a vision for BI that is driven by enterprise modeling.

  • The Business Intelligence Model (BIM) aims to enable business users to conceptualize business operations and strategies and performance indicators in a way that can be connected to enterprise data through highly automated tools.

  • The BIM draws upon well-established business practices such as Balanced Scorecard and Strategy Maps as well as requirements and conceptual modeling techniques such as goal modeling.

  • The connection from BIM to databases is supported by a complementary research effort on conceptual data integration.

Introduction:

• • In all kinds of enterprises, from businesses to government to healthcare, data is becoming increasingly abundant.

  • As more and more operations are conducted or supported digitally, massive amounts of data can be collected and analyzed.

  • Organizations are taking advantage of computational capabilities to slice and dice the data, pose ad hoc queries, detect patterns, and to measure performance.

  • The vision of the data-driven enterprise holds promise for greater strategic agility and operational efficiency [1], supported by a range of software tools under the general label of Business Intelligence (BI).

  • Yet the benefits of BI can be elusive.

  • Despite the availability of voluminous data, meaningful and productive use of that data remains a major hurdle for BI initiatives.

  • Data exists throughout the enterprise to serve numerous different purposes, and have diverse semantics and representations.

  • Much of the IT implementations of business operations are not directly suitable or comprehensible for enterprise level decisionmaking.

  • There is a huge conceptual distance between business thinking and decision making on the one hand, and the raw data that is the lifeblood of daily operations on the other.

  • BI initiatives therefore can be very costly, takemanymonths, require serious commitment from business stakeholders and IT personnel, and still produce results that are of uncertain benefits.

  • We argue that the benefits of BI and the data-driven enterprise can be more easily attained by constructing a smoother path between business thinking and IT implementation.

  • The core of this vision is a conceptual model for representing a business viewpoint of data. Business decision makers do not want to think in terms of tuples in databases, or dimensions in star schemas. They think in terms of customer satisfaction, market share, opportunities and threats, and how to rearrange business processes.

  • These concepts then need to be mapped to IT implementations in a coherent and effective way that minimizes manual effort.

  • We propose a Business Intelligence Model (BIM) that draws upon wellestablished concepts and practices in the business community, such as the Balanced Scorecard and Strategy Maps, as well as techniques from conceptual modeling and enterprise modeling, such as metamodeling and goal modeling techniques.

  • The BIM will be used by business users to build a business schema of their strategies and operations and performance measures.

  • Users can therefore query this business schema using familiar business concepts, to perform analysis on enterprise data, to track decisions and their impacts, or to explore alternate strategies for addressing problems.

  • The business queries are translated through schema mappings into queries defined over databases and data warehouses, and the answers will be translated back into business-level concepts.

  • The BIM is the foundation for the broader research agenda of the Business Intelligence Network (BIN), which aims to raise the level of abstraction for the next generation of BI tools, so that the benefits of BI will be accessible to all members of the enterprise, with minimal help from specialist intermediaries.

  • The BIN research project is supported by BI industry leaders.

  • A case study to test the BIM in a real world setting is being conducted at a hospital currently engaged in a BI initiative.

  • In this paper, we outline the key features of the BIM using a hypothetical business setting, loosely based on and extending an example from the BSC Institute.

    • Details of the BIM can be found in [2].

    • Section 2 of this paper describes the BestTech case study.

    • Section 3 introduces the main features of BIM and its metamodel.

    • Section 4 presents how to use BIM for strategic planning while, in Section 5, its application for operations management.

    • In Section 6,we illustrate analytic queries for the example enterprise setting.

    • Sections 7 and 8 discuss, respectively, related work and conclusions.

The Business Intelligence Model (BIM):

• • The BIM fragment which provides Strategic, Operational and Analytic primitives.

  • The “type” attributes are used to represent different business terminology.

  • For example, the type attribute for “ProcessClass” can assume the values: Initiative, Project, Action, Activity and Task.

SWOT Situational Analysis:

• • The BestTech strategy plan, including Financial, Customer, Process, and Learning & Growth perspectives.

  • One of the possible sub-strategies to increase revenue is highlighted in thicker red lines.

Allocation and Monitoring of Resources:

<

• • An example of resource allocation and monitoring.

Modelling and Reasoning about Operations:

• • Strategic, Mid-Level, and Operational intentions leading to increased shareholder value (partial view).

• • The Online sales process workflow