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
Operational
Control
Narrow
Middle
Management
Control
Top
Strategic Planning
Wide
Narrow
Wide
4. Sources of
Data
Internal
External
5. Time Horizon
Historical
6. Level of Detail
Micro
3. Scope of
Information
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 Isn’t 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 on-line 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 multidimensional schemas (usually star schema).
Contd.