Operational vs. Informational System Operational System Operational systems maintain records of daily business transactions whereas a Data Warehouse is a special database that serves as the integrated repository of company data, for reporting and decision support purpose. In other words operational systems are where the data is put in, and the data warehouse is where we get the data out. In an operational system users takes orders, sign up new customers, and log complaints. They almost always deal with one record at a time in an operational system. They repeatedly perform the same operational tasks over and over. On the other hand, the users of a data warehouse watch the wheels of the organization turn. They count the new orders and compare them with last week's orders and ask why the new customers signed up and what the customers complained about. Users of a data warehouse almost never deal with one row at a
time. Typical relational databases are designed for on-line transactional processing (OLTP) and do not meet the requirements for effective on-line analytical processing (OLAP). As a result, data warehouses are designed differently than traditional relational databases. Information Must be Tailored to Management Level Management Level Lower 1. Function 2. Scope of Responsibility 3. Scope of Information 4. Sources of Data
Operationa l Control Narrow Middl e Management Control Top Strategic Planning Wide Narrow
Wide Internal External 5. Time Horizon Historical 6. Level of Detail Micro Future
Macro ? Type of Data in the Info. System Not just Hard, Internal Data Not limited to Financial Data Must include Soft, External Data Key Areas to be Considered: Measurement of Customer Service Market Information on Customers & Competition High-Potential Evaluation, Succession Planning & Career Development of Employees The Data Isnt Where We Need It ! Senior Managers -Strategic Planning Middle Managers
- Management Control Internal, Hard Data External, Soft Data Front-Lines - Operational Control Corporate Data Warehouse The greatest challenge of the computer industry is to learn how to build information bases, not databases. The really important information cannot be easily quantified and exists outside the organization. - Peter Drucker (1993) A Different Perspective on Data
Quality ... Depending on Use Operational Systems (e.g., Invoicing, Airline Reservations, Electronic Commerce, etc.) Emphasis on complete, accurate and timely data But limited to internal, hard data Cost of data quality justifiable because systems will be used Information Systems (e.g., Performance Evaluation, Market Analysis, etc.) Scope of Data is Wider - External and Soft data But ... Is Better Data Worthwhile?
Value is zero if system is not used COST versus VALUE OF DATA - Satisficing Concept Better data Higher cost Value Impact on the decision Aim: Get a Satisficing Solution for Decision-Making - Select a satisfactory decision with limited information in a limited time instead of searching for the best solution entailing more time and information
"We are subjecting every activity, every function to the most rigorous review, distinguishing between those things which we absolutely need to do and know versus those which would be merely nice to do and know." GE CEO Actionable Information Information that becomes the basis for action Must be Timely Satisficing Accuracy is Enough
Must Help in ... Problem-Finding and Problem-Solving Attributes of Actionable Information Timeliness If it is late, managers will make decisions without it Complete and Accurate? How much? Just good enough for decision-making What is absolutely needed in relation to
What is at stake Reason: $$$$$$ 100% Complete and Accurate takes time and is expensive The key concept in information accuracy and completeness is Satisficing. Attributes of Actionable Information Timeliness If it is late, managers will make decisions without it Complete and Accurate? How much? Just good enough for decision-making
What is absolutely needed in relation to What is at stake Reason: $$$$$$ 100% Complete and Accurate takes time and is expensive The key concept in information accuracy and completeness is Satisficing. Collaboration of Both Detailed Notes Operational vs. Informational Systems Perhaps the most important concept that has come out of the Data Warehouse movement is the recognition that there are two fundamentally different types of
information systems in all organizations: operational systems and informational systems. "Operational systems" are just what their name implies; they are the systems that help us run the enterprise operation day-to-day. These are the backbone systems of any enterprise, our "order entry', "inventory", "manufacturing", "payroll" and "accounting" systems. Because of their importance to the organization, operational systems were almost always the first parts of the enterprise to be computerized. Over the years, these operational systems have been extended and rewritten, enhanced and maintained to the point that they are completely integrated into the organization. Indeed, most large organizations around the world today couldn't operate without their operational systems and the data that these systems maintain. On the other hand, there are other functions that go on within the enterprise that have to do with planning, forecasting and managing the organization. These functions are also critical to the survival of the organization, especially in our
current fast-paced world. Functions like "marketing planning", "engineering planning" and "financial analysis" also require information systems to support them. But these functions are different from operational ones, and the types of systems and information required are also different. The knowledge-based functions are informational systems. "Informational systems" have to do with analyzing data and making decisions, often major decisions, about how the enterprise will operate, now and in the future. And not only do informational systems have a different focus from operational ones, they often have a different scope. Where operational data needs are normally focused upon a single area, informational data needs often span a number of different areas and need large amounts of related operational data.
In the last few years, Data Warehousing has grown rapidly from a set of related ideas into an architecture for data delivery for enterprise end-user computing. OLTP vs. OLAP Contd. OLTP (On-line Transaction Processing) is characterized by a large number of short online transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model
(usually 3NF). OLAP (On-line Analytical Processing) is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema). Contd.
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