Need for an integrated approach to data warehousing

The ubiquitous KFC satisfies the desires of millions of its patrons in over 70 countries, generating annual revenues of $7 billion. With a wide network across the globe and tons of data, how do their IT managers maintain the humungous database that has been generated? How do they analyze their customer preferences?

KFC had conducted data analysis using a variety of tools, however it failed to provide comprehensive details such as sales report of one particular region or the food preferences of its varied global customers and comparative sales figures of the outlets. In a situation like this, the MNC realized the need for a centralized bank with minimum training facilities that would store and find information easily. Thus, the organization decided to build a Data warehousing solution to enhance their efficiency in customer interaction.

This shows that in today’s competitive global business environment, understanding and managing enterprise-wide information is crucial for making timely decisions and responding to changing business conditions where clearly ‘customer is the king’ now and CRM tops their agenda. Many companies are realizing the business advantage of leveraging one of their key assets - business data.

As enterprises mull over a series of IS initiatives, it can be mind boggling to think as to how they will respond to unforeseen changes in the organization and the unpredictable information needs of their employees. Many research reports indicate that the amount of data in a given organization doubles every five years.

According to Sonata software, Head —Business Intelligence Practices, Sayinath, some challenges in IS are-

• Reengineering systems to run the business will need information to suggest the best ways to do so.



• The task of merging business systems has become more frequent, intricate and delicate.



• The need to provide information for supporting decision making throughout the organization demands re-thinking traditional data management strategies.



• The need to analyze business performance at all levels in the organization requires a deeper, broader base of information to do so, taking care not to disrupt production systems".

Experts agree that the above IS challenges may not be totally overcome; in fact they will become more pronounced as the gap grows between systems that run the business and those that report it. Hence, it is suggested that creating a data architecture that includes data warehousing to bridge this chasm should be considered as a long-term solution and not a stop-gap approach to serious data management.

Data Warehousing has emerged as an increasingly popular and powerful concept of applying information technology to turn this huge quantums of data into meaningful information for better business decisions. Says Sayinath " The data warehouse concept sprang from the growing competitive need to quickly analyze business information. Existing operational systems cannot meet this need because of lack of on-line historical data; data required for analysis resides on different operational systems; and operational database designs are inappropriate for decision support."

A data warehouse eliminates these problems by storing the current and historical data from disparate operational systems in a single consolidated system. For eg. In hospital DW, patient information may come from admitting, billing, medical records, and clinical information systems and be clubbed various entities or "subjects." This makes data readily accessible to the people who need it without interrupting on-line operational workloads. A data in a data warehouse may refer to ‘customer’ or a ‘product’ to a ‘region’, etc.

Data warehouse contains a wide variety of data that present a complete picture of business conditions at any single point of time and emphasizes the capture of data from diverse sources for useful analysis and access. They combine databases across an entire enterprise, however anybody requiring access to specialized databases stored locally may not necessarily go through the entire database and this is where the concept of a data mart comes into the picture.

A Data mart is defined as the subset of a data warehouse. While a data warehouse provides for information on various subjects across various functional domains of the business, a datamart provides for data relating to one ‘subject’. Data marts are like workgroups which are small in size, typically 10-50GB according to a report. They are less expensive and take less time for implementation with quick results. They can be evolved into full fledged data warehouses. However they offer limited scope as compared to a data warehouse as it supports a particular business unit or a particular subject area.

One of the primary functions of a data warehouse is to provide enterprise level information architecture i.e., a data warehouse/datamart should be able to consolidate data from various data sources and provide information to a wide variety of users. The information provided to the users should be in business terms and should be provided when they want it, where they want it and how they want it.

Says IDC(India), Manager, computing products, A.S. Srinivas, " The increased dependence on internet and post 9/11 incidents have all contributed for data explosion and increased the demand of more data storage. This consequently has increased the demand for data warehousing/datamart solutions for efficient delivery of information to enterprise business users. Besides, it should provide a scalable data storage model and secure information delivery".

" The other main objective of designing a data warehouse is to provide able ‘Decision support’ —to analyze data for making decisions. As a part of the decision support system the data warehouse/ datamart should lend itself to analytics by the business user. The spectrum of analytics provided should range from simple analytics to high end and advanced analytics features", opines Sayinath.

The data warehouse along with the OLAP technologies provides business users to perform online analytics, easy report creation facilities and information sharing opportunities. This reduces the business user dependence on the IS department and also hastens the decision making process for the business user. These inherent qualities of the data warehouse/datamart help in increasing the productivity of an organization.