The Intelligence Phase

Intelligence in decision making involves scanning the environment, either intermittently or continuously. It include monitoring the results of the implementation phase of a decision making process.

Problem or opportunity
The intelligence phase begins with the identification of organizational goals and objectives related to an issue of concern and determination of whether they are being met. Problem occurs because of dissatisfaction with the status quo.

Dissatisfaction is the result of a difference between what people desire or expect and what is occurring. In this first phase, a decision maker attempts to determine whether a problem exists, identify its symptoms, determine its magnitude and explicitly define it. Often what is described as a problem may be only a symptom of a problem.

Because real-world problems are usually complicated by many interrelated factors, it is sometimes difficult to distinguish between the symptoms and the real problem. New opportunities and problems certainly may be uncovered while investigating the causes of symptoms.

The existence of a problem can be determined by monitoring and analyzing the organization’s productivity level. The measurement of productivity and the construction of a model are based on real data. The collection of data and the estimation of future data are among the most difficult steps in the analysis.

The following are some issues that may arise during data collection and estimation and thus plague decision makers:
  • Data are not available. As a result, the model is made with and relies on potentially inaccurate estimates.
  • Obtaining data may be expensive.
  • Data may not be accurate or precise enough.
  • Data estimation is often subjective.
  • Data may be insecure.
  • Important data that influence the results may be qualitative.
  • The may be too many data.
  • Outcomes may occur over an extended period. As a result, revenues, expenses and profit will be recorded at different points in time. To overcome this difficulty, a present value approach can be used if the results are quantifiable.
  • It is assumed that future data will be similar to historical data. If this is not the case, the nature of the change has to be predicted and included in the analysis.
When the preliminary investigation is completed, it is possible to determine whether a problem really exists, where it is located and how significant it is. A key issue is whether an information system is reporting a problem or only the symptoms of a problem.

Problem classification
Problem classification is the conceptualization of a problem in an attempt to place it in a definable category, possibly leading to a standard solution approach. An important approach classifies problems according to the degree of structuredness evident in them.

Problem Decomposition
Many complex problems can be divided into subproblems. Solving the simpler subproblems may help in solving a complex problem. Also, seemingly poorly structured problems sometimes have highly structed subproblems.

Just as a semistructured problems results when some phases of decision making are structured while other phases are unstructured, so when some subproblems of a decision making problem are structured with others unstructured, the problem itself is semistructered.

As a DSS is developed and the decision maker and development staff learn more about the problem it gains structured. Decomposition also facilitates communication among decision makers. Decomposition is one of the most important aspects of the analytical hierarchy process, which helps decision makers incorporate both qualitative and quantitative factors into their decision making models.

Problem ownership
In the intelligence phase, it is important to establish problem ownership. A problem exists in an organization only if someone or some group takes on the responsibility of attacking it and if the organization has the ability to solve it.

The assignment of authority to solve the problem is called problem ownership. For example, a manager may feel that he/she has a problem because interest rates are too high. Because internet rate levels are determined at the national and international levels, most managers can do nothing about them, high interest rates are the problem of the government, not a problem for a specific company to solve.

The problem companies actually face is how to operate in a high interest rate environment. For an individual company, the interest rate level should be handled as an uncontrollable (environmental) factor to be predicted.

When problem ownership is not established, either someone is not doing his/her job or the problem at hand has yet to be identified as belonging to anyone. It is then important for someone to either volunteer to own it or assign it to someone.

Decision Making Process Phases

Decision making phases involves 4 major phases: intelligence, design, choice and implementing. The decision making process starts with the intelligence phase. In this phase, the decision maker examines reality, identifies and defines the problem. Problem ownership is established as well. In the design phase, a model that represents the system is constructed.

This is done by making assumptions that simplify reality and writing down the relationships among all the variables. The model is then validated and criteria are determined in a principle of choice for evaluation of the alternative courses of action that are identified. Often the process of model development identifies alternative solutions and vice versa.

The choice phase includes selection of a proposed solution to the model (not necessarily to the problem it represents). This solution is tested to determine its viability. When the proposed solution seems reasonable, we are ready for the last phase, implementation of the decision (not necessarily of a system). Successful implementation results in solving the real problem. Failure leads to a return to an earlier phase of the process. In fact, we can return to an earlier phase during any of the latter three phases.


Business Intelligence Architecture

Business intelligence (BI) has four major components: a data warehouse, business analytics, business performance management and a user interface. Data warehouse and its variants is the cornerstone of any medium-to-large BI system.  Originally the data warehouse included only historical data that were organized and summarized so end users could easily view or manipulate data and information. Notice that the data warehousing environment is mainly the responsibility of technical staff, while the analytic environment also known as business analytic is the realm of business users. Any user can connect to the system via the user interface such as a browser and top managers may use business performance management component and also a dashboard.

Data Warehousing
The data warehousing and its variants are the cornerstone of any medium to large business intelligence (BI) system. Originally the data warehouse included only historical data that were organized and summarized, so end users could easily view or manipulate data and information. Today, some data warehouses include current data as well so they can provide real-time decision support.

Business Analytics
Ends user can work with the data and information in a data warehouse by using a variety of tools and techniques. These tools and techniques fit into three categories:
1.  Reports and queries. Business analytics include both static and dynamic reporting , all types of queries, discovery of information, multidimensional view, drill-down to details and so on.
2.  Advanced analytics. Advanced analytics include many statistical, financial, mathematical and other models that are used in analyzing data and information.
3.  Data, text and web mining and other sophisticated mathematical and statistical tools. Datamining is a process of searching for unknown relationships or information in large databases or data warehouses, using intelligent tools such as neural computing, predictive analytics techniques or advanced statistical methods.

Business Performance Management
Business performance management (BPM) which is also referred to as corporate performance management (CPM) is an emerging portfolio of applications and methodology that contains evolving BI architechture and tools in its core. BPM extends the monitoring, measuring and comparing of sales, profit, cost, profitability and other performance indicators by introducing the concept of management and feedback. It embraces process such as planning and forecasting as core tenets of a business strategy. In contrast with the traditional DSS, EIS and BI which support the bottom-up extraction of information from data, BPM provides a top down enforcement of corporate wide strategy.

User Interface
Dashboards provide a comprehensive visual view of corporate performance measures also known as key performance indicators, trends and exceptions. They integrate information from multiple business areas. Dashboards present graphs that show actual performance compared to desired metrics, a dashboard presents an at a glance view of the health of the organization.

Computerized Decision Support System

When managers want make a decision they need considerable amounts of relevant data, information and knowledge. Processing these, managers must be done quickly, frequently in real-time and usually requires some computerized support system.

1.  Speedy computations
A computer enables the decision maker to perform many computations quickly and at a low cost. Timely decisions are critical in many situations, ranging from a physician in an emergency room to a stock trader on the trading floor. With a computer thousands of alternatives can be evaluated in the seconds.

2.  Improved communication and collaboration
Many decisions made today by groups whose numbers maybe in different locations. Groups can collaborate and communicate readily by using web-based tools. Collaboration is especially important along the supply chain where partners must share information.

3.  Increased productivity of group members
Assembling a group of decision makers especially experts in one place can be costly. Computerized support can improve the collaboration process of a group and enable its members to be at different locations by saving travel costs. Also increase the productivity of staff support such as financial, legal analyst by using software optimization tools that help determine the best way to run a business.

4.  Improved data management
Huge amounts of data can be stored in different databases anywhere in the organization and even possibly at web sites outside the organization. The data may include text, sound, graphics and video and they can be in foreign languages. It may be necessary to transmit data quickly from distant locations. Computers can search, store and transmit needed data quickly, economically, securely, transparently and paperless.

5.  Managing giant data warehouse
Large data warehouse like the one operated by Wal-Mart contain terabytes and even petabytes of data. Computers can provide extremely great storage capabilities for any type of digital information and this information can be accessed and searched very rapidly.

6.  Quality support
The more data can be accessed the more alternatives can be evaluated, forecasts can be improved, risk analysis can be performed quickly and the views of experts can be collected quickly and at a reduced cost. Expertise can even be derived directly from a computer system using artificial intelligence methods.

7.  Agility support
Competition today is based not just on price but also on quality, timeless, customization of products and customer support. In addition, organization must be able to frequently and rapidly change their mode of operation, reengineer processes and structures, empower employees and innovate in order to adapt to their changing environments. Decision support technologies such as intelligent systems can empower people by allowing them to make good decisions quickly even if they lack some knowledge.

8.  Overcoming cognitive limits in processing and storing information
The human mind has only a limited ability to process and store information and people sometimes find it difficult to recall and use information in an error-fashion due to their cognitive limits. The term of cognitive limits indicates that an individual’s problem solving capability is limited when a wide range of diverse information knowledge is required. Computerized systems enable people to overcome their cognitive limits by quickly accessing and processing vast amounts of stored information.

9.  Using the web
Since the development of the Internet and Web servers and tools, there have been dramatic changes in how decision makers are supported. Most important, the Web provides the access to a vast body of data, information and knowledge available around the world, user-friendly graphical user interface that is easy to learn to use and readily available, the ability to effectively collaborate with remote partners and availability of intelligent search tools that enable managers to find the information they need quickly and inexpensively.

10.  Anywhere, anytime support
Using wireless technology managers can access information anytime and from anyplace, analyze and interpret it and communicate with those involved.

Business Intelligence Today and Tomorrow

In today’s highly competitive business the quality and timeliness of business information for an organization is not just a choice between profit and loss, it may be a question of survival or bankruptcy. No business organization can deny the inevitable benefits of BI. Recent industry analyst reports how that in the coming years millions of people will use BI visual tools and analytics everyday. Today’s organizations are deriving more value from BI by extending actionable information to many types of employees, maximizing the use of existing data assets.

Producers, retailers, governments, special agencies and others use visualization tools, including dashboards. More and more industry specific analytic tools will flood the market to do almost any kind of analysis and help to make informed decision making from the top level to the user level.

A potential trend involving BI is its possible merger with artificial intelligence (AI). AI has been used in business applications since the 1980s and it is widely used for complex problem solving and decision support techniques in real-time business applications.

It will not be long before AI applications are merged with BI bringing in a new era in business. To enable this integration, BI vendors are starting to use service oriented architecture and enterprise information integration (EII).

BI is spreading its wings to cover small, medium and large companies. Large BI players are for large enterprises and small, niche players service midsize and small companies. Analytics tools are also penetrating the market for very specialized functions, which will help some companies to go just for BA instead of full data warehouse based BI implementation.

BI takes advantage of already developed and installed components of IT technologies, helping companies leverage their current IT investments and use valuable data stored in legacy and transactional systems.

The Major Characteristics of Business Intelligence

Enterprise software systems are designed as transaction processing tools and today the main job is to optimize an informed decision making process for users at all levels of the organizational hierarchy. Recent trends seem to indicate that access to key operational data is no longer the purview of executives alone. Many executives of manufacturing and service companies today are allowing (and even encouraging) low level managers, supervisors and analysts on the shop floor and in distribution centers access to operational performance data to enable better and more timely decision making by those employees.

You’re familiar with the information systems that support your transactions such as ATM withdrawals, bank deposits and cash register scans at the grocery store. The transaction processing systems involved with these transactions constantly handle updates to what we might call operational databases. For example, an ATM withdrawal transaction needs to reduce the bank balance accordingly a bank deposit adds to an account and a grocery store purchase is likely reflected an appropriate reduction in the store’s inventory for the items we bought.

These online transaction processing (OLTP) systems handle a company’s ongoing business. In contrast a data warehouse is typically a distinct system that provides storage for data that will be made use of in analysis.

The intent of that analysis to give management the ability to scour data for information about the business and it can be used to provide tactical decision support whereby. For example, line personnel can make quicker and or more informed decisions.

Most operational data in enterprise resource planning (ERP) system and in their complementing siblings such as supply chain management (SCM) or customer relationship management (CRM) are stored in what is referred to as OLTP systems, which are computing processing systems in which the computer responds immediately to user requests.

Each request is considered to be a transaction which is a computerized record of a discrete event such as the receipt of inventory or a customer order. In other words a transaction requires a set of two or more database updates that must be completed in an all or nothing fashion.

In the 1980s many business users referred to their mainframes as the black hole because information went into it but none ever come back out. All requests for reports had to be programmed by IT staff and only canned reports could be generated on a scheduled.

 
Copyright © 2011. BI Articles and Study Case - All Rights Reserved
Proudly powered by Blogger