Most business sectors are currently undergoing a wave of deconstructions. Today's business model is a vertically and horizontally integrated value chain: multiple products are originated, packaged, sold, and cross-sold through a common set of proprietary distribution channels. These channels have high fixed costs and substantial economies of utilization and scale. These factors often determine the rules of engagement and competitive advantage. The fundamental unit of value is the customer relationship.

Distribution systems are optimized around servicing these relationships. Some products are sold at low or negative margins in order to acquire and build relationships. Others are then cross-sold at high margins to extract value from relationships that have been established. All contribute to the common costs of an integrated distribution system. In addition, regulatory and compliance based demands on financial institutions have grown, and with them, risk.

In these integrated systems, companies are gaining access to intelligence that breaks the trade-offs between richness (depth) and reach (breadth) never before available to them. In the case of home banking for example, they are able to contact any financial institution for any kind of service or information. They are able to maintain balance sheets on their desktops drawing on data from multiple institutions. They are increasingly able to compare alternative product offerings and sweep funds automatically between accounts at different institutions. They are increasingly able to announce their product requirements, accept bids, and make sophisticated comparisons between product and service offerings. The sheer breadth of choice of product and solution within this system has created the need for third parties to play the role of navigator or systems integrator in order to consolidate data into analytical form and so extract further decision economies or higher ROIs.

In addition, the second order consequences of deconstruction are profound. As it becomes easier for customers to compare and switch from one supplier to another, the value, indeed the meaning, of any primary customer relationship will become problematic. The competitive value of one stop shopping and established relationships will drop. Cross-selling will become more difficult. Available information about the customer's behavior and preferences will become more evenly distributed among competing institutions. The winner in any contest is less likely to be the primary customer relationship and more likely to be whoever makes the best offer for that particular product or service. Competitive advantage will be determined product by product, and so providers with broad product lines will lose ground to focused specialists.

To address these challenges, Our company, The Normandy Group has developed a unique suite of services called Strategic Business Insight specifically designed to meet the data mining, enterprise analytics, business intelligence and decision optimization (ROI) demands of today's time, data and decision-challenged institution. These four key strategies are examined below.

Data Integration and Data Mining

We have grown accustomed to the fact that there are enormous volumes of data filling our computers, networks and our lives. Companies and governments have dedicated enormous resources to collecting and storing data. A study by the Standish Group revealed that, on average, only about 15 -20% of these resources are actually used in direct support of decision making. In reality, only a small amount of the data reposed in these systems will ever be used because, in many cases, the volumes are too large to manage, or the data structures themselves are too complicated to be analyzed effectively.

The need to understand large, complex, information-rich data sets is common to virtually all fields of science. In the business world, corporate and customer data are growing in recognition as strategic assets. The ability to extract useful knowledge hidden in these data and to act on that knowledge is becoming increasingly important in today’s competitive world. The entire process of applying and combining computer based methodologies with new techniques, for discovering knowledge in data is called data mining. Data mining is the iterative process via which new, valuable and non-trivial information is sourced from existing large data sets. Some of the typical challenges addressed by data mining are:

• Data extraction, integration, preparation and reduction for business performance measurement
• Information and machine learning for training business and service applications intelligently
• Statistical and mathematical modeling for analyzing hidden data trends and behaviors
• Predictions and visualizations for understanding how to build strategic and operational planning models

Enterprise Analytics

An integrated performance management process is anchored by robust enterprise analytics. Enterprise analytics are based upon two premises. First, measurement motivates (i.e., if you can measure it, you can manage it). Second, measurement must be linked to strategy and decisions. An Economist Intelligence Unit study revealed that almost all companies (87%) indicate a critical or important need to improve their performance. Typical performance management and enterprise analytics issues include:

• Risk Analysis for risk determination and risk management
• Linking critical business processes to results through the strategy
• Excessive time spent on non-value added activities
• Poor product, customer and channel profitability
• Inadequate measurements of intangibles such as intellectual capital, R&D returns, lifetime customer value and brand effectiveness
• Poorly derived causal models of business value drivers, metrics and desired outcomes

The demands of e-business have accelerated the trend toward greater information access and greater horizontal and vertical integration to facilitate the creation of virtual structures and alliances. These trends have resulted in the growth of a number of different business models such as eco-systems (e.g., auto exchanges, integrated SOAs, integrated compliance models etc), buyer-led models (groups of buyers with common needs aligning to gain leverage), seller-led models - distributor-group sellers focusing on a common group of buyers (Grainger.com and independent 3rd party B2B exchanges), and integrated supply chains (neutral party builds platform for e-business within a vertical market). In turn, these business models have resulted in the need for more robust enterprise analytics models.

Enterprise Analytics is a critical driver of discontinuity given that it enables early recognition of trends much earlier thus enabling organizations to respond faster and with greater decision accuracy.

Business Intelligence

At the enterprise level, Business Intelligence has demonstrated its ability to improve profits by tens or hundreds of millions of dollars annually, depending on the size of the company. Given its proven success, BI should be a key tool for managing and improving performance and profits. Instead, most companies tend to see BI as a technology play, and consequently they fail to leverage business information and analytical techniques to drive better performance and improved profits. Some of the typical business challenges that BI addresses are:

• Misalignment between business strategies, core business processes that drive performance, and BI programs
• Misalignment between strategic planning, performance management, budgeting and forecasting
• Insufficient leadership for how the business should use information and analytical tools to drive results
• Misalignment between how large data repositories are constructed around business decision support needs
• Misalignment between service support architectures (supply) and decision support needs (demand).

Business Intelligence, if done well, extends the decision boundaries beyond what's known today to deliver quality predictions about markets, trends, solutions and value discontinuities that define business profitability tomorrow.

Decision Optimization

Decision optimization is about achieving maximums (returns) and minimums (cost and resource consumption curves) within a business management model. In effect, it assumes that the sum of the returns from all decisions is maximized and the sum of the consumption curves from all actions undertaken to drive returns are minimized given all known and quantifiable constraints. The benefit of decision optimization is it consolidates the benefits from its first three drivers (data mining, enterprise analytics and business intelligence) into a powerful metric for measuring the efficiency of profits and decision success.