Enterprise Architecture

Although advanced analytical methods and complex algorithms have been available, primarily in the domain of academics for some time, today’s tools and techniques have opened the door for their innovative use to solve formerly intractable business challenges and to provide superior insight and predictability to support management decision making.

Enterprise executives, performance managers and business analysts have traditionally leveraged business metrics delivered in role-tailored tools such as score cards, BI reports and forecast. A variety of business matrices are utilized to understand how well the organization or a particular Line of Business (LOB) aligns with the desired business goals. The traditional enhanced by the use of emerging analytics and business optimization techniques aimed to provide full support for collaboration within the decision making cycle. This is leading to a more efficient use of the information intelligence.

This higher maturity level in the use of business insights involves the full exploitation of the traditional BI technologies combined with the use of advanced analytical models, social network behavior techniques, predictive models and other advanced analytical models, social network behavior techniques, predictive models and other sophisticated analytical models, The new approach is also characterized by the use of real-time integration techniques, massive data collection architectures and newly developed approaches that apply the analytics arsenal to streaming data to deliver the high degree of scalability and low latency demanded by many fast-paced business environments.

Figure 1. Traditional flow in Business Process Management

A framework for business Analytics and Business Optimization

Holistic approaches to business analytics and Business Optimization have emerged as trends to address the new BPM challenges, incorporating all of the new and advanced BPM techniques and principles together with the business analysts and business performance optimization offers more thorough performance management. A holistic approach to Business Analytics and Business Optimization uses the EIA and the organizations Information Governance strategy and processes to align operational and strategic business objectives to fully mange the process of achieving the goals the enterprise has established.

Business Analytics and Business Optimization require that the EIA deliver the capabilities to enable data to be collected and analyzed from anywhere (including internal and external sources , sensors, instruments and other sources); access the wide variety of formats such as structured, Unstructured and societal data , and grouped ,categorized, and processed in to information that business users can access and consume at the right time and in context. This approach also dictates that beyond the performance measurement, the business insights enable detection, direction, and prediction of business outcomes, which are core characteristics of optimized business processes across an integrated enterprise.

In addition to the new technology trends in business analytics and business optimization, which are the subject of the business, organizations are required to consider several areas of competency and strategies. Such strategies include:

Creating a strategy for analysis and optimization: An important consideration in this approach is the creation of an enterprise strategy to better align the organization’s information assets with its business goals. This strategy describes how organizations achieve their business objectives faster, with less risk and at a lower cost by improving how information is recognized and acted upon across the enterprise, within a business function or an LOB. An effective business Analytics and Business Optimization strategy not only addresses what should be done with the information, bust also how to act on it. That is, it defines the recommended actions at all levels: policy, analytics, business process, organization, applications and data.

Designing an Enterprise Information Management (EIM) Strategy: The creation of an EIM strategy includes the methodology, practices, principles and technologies needed to effectively manage disparate data. The EIM is a driver for impacting individual and organizational performance, and it is as well as enabler of BI standardization because the resulting EIM must ultimately ensure that relevant and timely information is delivered to business application and users in a way they understand. The EIM strategy derived from the business analytics and business optimization framework focuses on solving specific information related problems that have direct impact in the creation of an organization –wide business performance strategy or the implementation of a specific advanced analytical solution. Common areas around which this EIM strategy is created include an EIA with components such as Enterprise Information Integration (EII), Master Data Management (MDM), and Enterprise content Management (ECM). The EIA supporting the EIM is governed by Information Governance. Among the area considered to create this EIM strategy, we include the EIA with focus on components such as EII, MDM and ECM, as well as Information governance.

Lets briefly examine how metrics are used to quantify, monitor and evaluate the performance of the business. Business metrics or Key Performance Indicators (KPI) represents performance measurements for specific business activities. Each metric is associated to a specific business goal or threshold used to determine whether or not the business activity performs within the accepted limits.

BI and analytic techniques are used to monitor these KPIs periodically or in the real-time and the values delivered to executives, managers and other information consumers through personalized, role-based tooling to facilitate the assessment of the present state of the business and to assist in prescribing a corrective action to achieve the desire result. By implementing the definition, monitoring analysis, and tracking of KPIs, BPM provides business users and decision makers with the insights required for implementing actions aimed to optimize business performance. The mechanism turns BPM into a powerful enabler of the close link that must exist between the IT and business communities.

Let us look at a business scenario that involves many of the core elements of the holistic approach for business analytics and business optimization to establish a framework for a solution that addressed a common business challenges in the banking and financial sectors around Enterprise Risk Management (ERM).

ERM at a financial institution requires full transparency and visibility into the many areas of risk where the institution is exposed, and this requires the EIA to be the enabler of risk-related information management capabilities to:
• Facilitate the use of more robust risk modeling engines.
• Use a larger number of risk metrics to capture and promote the understanding of the institution’s full exposure.
• Provide an integrated view of risk information including correlation across the institution asset classes, portfolios and the relationship of different types of risks.
• Enable real-time and intra-day information delivery capabilities to account for the rate of change of risk factors.

Under these conditions, the traditional risk reporting schedule aimed at compliance with government institutions and regulatory agencies is no longer sufficient to understand the overall risk the organization has and what are the different risk exposures by product, geography, business units and other dimensions. To satisfy these wider and more up-to-date risk demands, organizations use advanced risk modeling engines and scenario analysis tools that use near real time or real-time analysis to predict external threats to the institution stability such as changes in the dynamic of the market. To satisfy the business requirements of today, risk officers and the business manager must be able to align risk management with the existing business silos to have visibility into an enterprise-wide view of the risk exposures(for example counter party credit risk)
Among the main factors forcing banks and other financial institution to define and implement effective ERM strategies are several industry regulations. These regulations are also major drivers for implementing a wide array of enterprise business Analytics and Optimization solutions by yond the ERM area. Is is also interesting to note that for many financial institutions embarked in initiatives to support regulations such as surbane-Oxley act, the legislation is seen as an opportunity to streamline systems and improve the efficiency of business processes. At the same time, supporting Based II requires companies to implement a strategy to integrate data nad processes that are ofter split between finance, operational and risk management functions and this is another strong incentive to speed up the development of strategies to turn the EIA into a more streamlined, agile and responsive asset that is capable of providing trusted and relevant insights for better risk management, better performance management, and better decisions around capital allocations.

Banking Use Case:

We briefly examine the core requirements for an integral ERM view at a banking institution and validate what capabilities the components of the EIA must deliver to meet the requirements of providing business insights into enterprise risk.

A comprehensive strategy for management of enterprise risks in the banking industry involves business capabilities such as:
• Understand the various financial risks across business silos, which involves obtaining accurate information about risk exposure across business lines.
• Detect and address the risks associated with internal and external financial crime and fraud. This involves analytical and business rules and the capacity to detect time patterns in a timely manner while processing vast amount of data from a large number of sources in real time.
• Anticipate and mitigate potential risk from failed internal processes, people or systems including the capacity to understand correlation of risks across asset classes and risk types.
• Comply with risk-related regulations across jurisdictions where the organization operates, and delivers useful risk insights to the right people.

In below figure we present a high level view f a framework for the information dlow in a risk solution. The figure illustrates the major levels of this framework : an information integration layer to extract risk related information from the enterprise operational systems, a trusted information delivery level that consolidates and makes information available in a timely manner, a level containing is a rich set of traditional and advanced analytical and reporting capabilities to generate risk insights from the consolidated data and a variety of delivery channels so risk intelligence can be made accessible to the users and decision makers in time and in context.

The framework highlights the core information architecture technical capabilities and major information flows but it does not explicitly address key dimensions such as the latency allowed for the information intelligence to be available.