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.

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.