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The chip effect on business intelligence solutions

M. J. Xavier

MOORE'S law has come true, and it is showing up in every aspect of our lives. Every twenty years, since 1900, the amount of computational power that can be bought with one dollar has increased by a factor of thousand. Since 1950, there has been a more th an million-fold increase. If the same had happened to automobiles, a Rolls-Royce should cost just one dollar. The ten-second pre-recorded musical greeting-card has more processing power than any computer did in 1950. The Sony PlayStation video game (1995 ) at $299 had more power than the Cray-I that cost $20 million when it was introduced. The new Chevrolet has more computational power than the Apollo spacecraft in 1969.

Business applications have also seen a phenomenal growth. We are bombarded with three-letter acronyms such as ERP, SCM, CRM, ERM and so on. From simply automating manual processes such as keeping accounts, salary administration, inventory record-keeping, etc, we are now witnessing the emergence of decision support systems.

It all started with the simple MRP (Material Resource Planning) systems that helped managers plan the material requirements. Subsequently ERP was introduced to help plan the 4 M's of an enterprise's resources -- materials, men, machines and money -- to t heir best synergistic value, simultaneously improving the fifth M, methodology (processes).

The extended enterprise applications were introduced in the form of SCM (supply chain management) and CPM (customer relationship management). Supply chain management involves coordinating and integrating the flow of materials, information, and finances a s they move in a process from the supplier to manufacturer to wholesaler to retailer and to consumer, within and among companies.

CRM helps an enterprise enable its marketing departments to identify and target their best customers, manage marketing campaigns with clear goals and objectives, and generate quality leads for the sales team. It assists the organisation in improving tele sales, accounts, and sales management by optimising information shared by multiple employees, and streamlining existing processes (for example, taking orders through mobile devices).

It allows the formation of individualised relationships with customers, with the aim of improving customer satisfaction and maximising profits; identifying the most profitable customers and providing them the highest level of service. Overall, it provide s employees with the information and processes necessary to know their customers, understand their needs, and effectively build relationships between the company, its customer base, and distribution partners.

Knowledge management (KM) represents another major business application of IT. KM is the name of a concept in which an enterprise consciously and comprehensively gathers, organises, shares, and analyses its knowledge in terms of resources, documents, and people skills. In 1998, it was believed that few enterprises actually had a comprehensive knowledge management practice in operation. Advances in technology and the way we access and share information have changed that.

Many enterprises now have some kind of knowledge management framework in place. Knowledge management involves data mining and some method of operation to push information to users. Some vendors are offering products to help create an enterprise inventory and access knowledge resources. The Lotus Knowledge Discovery System, for example, advertises that it can locate and organise relevant content and expertise required to address specific business tasks and projects. It will analyse the relationships betw een content, people, topics, and activity, and produce a knowledge map report based on a point system, that can be shared.

Currently, a number of software vendors offer business intelligence (BI) solutions that refer to a broad category of applications and technologies for gathering, storing, analysing, and providing access to data to help enterprise users make better busine ss decisions. BI applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.

Basically it builds a data warehouse that is a central repository for all or significant parts of the data that an enterprise's various business systems collect. Typically, a data warehouse is housed on an enterprise mainframe server. Data from various o nline transaction processing (OLTP) applications and other sources are selectively extracted and organised on the data warehouse database for use by analytical applications and user queries. Patterns in the data are analysed using data mining software th at look for relationships that have not previously been discovered. Typical data mining results include:

*Associations, or when one event can be correlated to another event;

*Sequences, or one event leading to another later event;

*Classification, or the recognition of patterns and a resulting new organisation of data (for example, profiles of customers who make purchases);

*Clustering, or finding and visualising groups of facts not previously known;

*Forecasting, or simply discovering patterns in the data that can lead to predictions about the future.

Data mining is employed in CRM too. The difference here is that the techniques are applied on the enterprise-wide data as opposed to customer-related data in CPM.

Neural networks and Artificial Intelligence too are finding use in pattern recognition. AI or artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquis ition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction. Particular applications of AI include, speech recognition, and image recognition.

Neural network is a type of artificial intelligence that attempts to imitate the way a human brain works. Rather than use a digital model, in which all computations manipulate zeros and ones, a neural network works by creating connections between process ing elements, the computer equivalent of neurons. The organisation and weights of the connections determine the output. Neural networks are particularly effective for predicting events when the networks have a large database of prior examples to draw on.

(To be concluded)

(The author is Dean, Academy for Management Excellence, Chennai.)