Business Intelligence (BI)

What is Business Intelligence (BI)?

•  Business Intelligence is a generalized term applied to a broad category of applications and technologies for gathering, storing, analyzing and providing access to data to help enterprise users make better business decisions
• Business Intelligence applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining
• An alternative way of describing BI is: the technology required to turn raw data into information to support decision-making within corporations and business processes

BusinessIntelligence Architecture

Business intelligence has become a critical element of information technology. It’s an old term with general or even ambiguous meaning. It has been used synonymously with decision support, analysis, and data warehousing, but today business intelligence has a more specific definition and a better understood application. Taken literally, business intelligence is just that—intelligence or understanding of your business. You get that understanding by analyzing your business operations.

This business intelligence process can deliver significant, bottom-line results. Implementing its technologies and applying its process can help make your business more effective and more efficient, increasing revenue, decreasing costs, and improving your relationships with customers and suppliers.

   Why BI?

• BI technologies help bring decision-makers the data in a form they can quickly digest and apply to their decision making.
• BI turns data into information for managers and executives and in general, people making decisions in a company.
• Companies want to use technology tactically to make their operations more effective and more efficient - Business intelligence can be the catalyst for that efficiency and effectiveness.

By definition, the moment any given business is operating, it begins generating data. Some obvious examples are sales, bookkeeping, production data, warehouse information, transportation and logistics, personnel, etc.In addition there also exists large volumes of data which are important to the business but not directly generated by business operations. Examples are market data, competitive data, tenders and proposal, legal information, raw material prices, etc.

As such, none of the above described information can be used in its raw form by corporate management to make decisions although the information is critical in helping make those business decisions.Therein lies the necessity for Business Intelligence. BI technologies help bring decision-makers the data in a form they can quickly digest and apply to their decision making. BI turns data into information for managers and executives and in general, people making decisions in a company.

     Benefits:

 The benefits of a well-planned BI implementation are going to be closely tied to the business objectives driving the project.

1. Identify trends and anomalies in business operations more quickly, allowing for more accurate and timelier decisions.
2. Deliver actionable insight and information to the right place with less effort .
3. Identify and operate based on a single version of the truth, allowing all analysis to be completed on a core foundation with confidence.

 

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Change data capture System in Datawerhouse

Change Data Capture (CDC) is a generic term for techniques that monitor operational data sources with the objective of detecting and capturing data changes of interest . CDC is of particular importance for data warehouse maintenance.

With CDC techniques in place, the data warehouse can be maintained by propagating changes captured at the sources. CDC techniques applied in practice roughly follow three main approaches,

namely

1.log-based CDC.

2.utilization of audit columns

3.calculation of snapshot differentials

1.Log-based CDC techniques parse system logs and retrieve changes of interest. These techniques are typically employed in conjunction with database systems. Virtually all database systems record changes in transaction logs. This information can be leveraged for CDC. Alternatively, changes may be explicitly recorded using database triggers or application logic for instance.

2.utilization of audit columns :

 Operational data sources often employ so called audit columns. Audit columns are appended to each tuple and indicate the time at which the tuple was modified for the last time. Usually timestamps or version numbers are used. Audit columns serve as the selection criteria to extract changes that occurred since the last incremental load process. Note that deletions remain undetected.

3.calculation of snapshot differentials

The snapshot differential technique is most appropriate for data that resides in unsophisticated data sources such as flat files or legacy applications. The latter typically offer mechanisms for dumping data into files but lack advanced query capabilities. In this case, changes can be inferred by comparing a current source snapshot with a snapshot taken at a previous point in time. A major drawback of the snapshot differential approach is the need for frequent extractions of large data volumes. However, it is applicable to virtually any type of data source.

• The above mentioned CDC approaches differ not only in their technical realization but also in their ability to detect changes. We refer to the inability to detect certain types of changes as CDC limitation. 
• As mentioned before deletions cannot be detected by means of audit columns. Often a single audit column is used to record the time of both, record creation and modification. In this case insertions and updates are indistinguishable with respect to CDC. Another limitation of the audit columns approach is the inability to retrieve the initial state of records that have been updated. 
• Interestingly, existing snapshot differential implementations usually have the same limitation. They do not provide the initial state of updated records while this would be feasible in principle. 
• since the required data is available in the snapshot taken during the previous run. 
• Log-based CDC approaches in practice typically capture all types of changes, i.e. insertions, deletions, and the initial and current state of updated records.

Defined different methods for Change data capture in datawearhouse.

1. Timestamp on rows.
2. Use in Optimistic locking.
3. Status Indicator on rows.
4. Time/Version/Status on Rows.
5. Trigger on Tables
6. Event Programming.
7. Log Scanners on databases.

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Logical Design In Datawearhouse

Logical Data Model (LDM) -

• A logical design is conceptual and abstract. The process of logical design involves arranging data into a series of logical relationships called entities and attributes.
• Logical data model includes all required entities, attributes, key groups, and relationships that represent business information and define business rules.
• An entity represents a chunk of information. In relational databases, an entity often maps to a table. An attribute is a component of an entity and helps define the uniqueness of the entity. In relational databases, an attribute maps to a column.

Logical design  in Data wearhouse

Important Terms:

Entity

•      Entity: Are the principal data object about which information is to be collected. A class of persons, places, objects, events, or concepts about which we need to capture and store data.

1.      Persons:  agency, contractor, customer, department, division, employee,       instructor, student, supplier.
2.       Places:  sales region, building, room, branch office, campus. 
3.       Objects:  book, machine, part, product, raw material, software license,  software package, tool, vehicle model, vehicle.  
4.       Events:  application, award, cancellation, class, flight, invoice, order, registration, renewal, requisition, reservation, sale, trip.
5.       Concepts:  account, block of time, bond, course, fund, qualification, stock.
6. 
   

•      Relationship:A natural business association that exists between one or more entities. The relationship may represent an event that links the entities or merely a logical affinity that exists between the entities

Relationship

         An example of a relationship would be:

1. Employees are assigned to projects.
2. Student enrolling in a curriculum.
3. Projects have subtasks.
4. Departments manage one or more projects 

• 
  
•      Cardinality:The cardinality of a relationship is the actual number of related occurrences for each of the two entities. The basic types of connectivity for relations are: one-to-one, one-to-many, and many-to-many. The minimum and maximum number of occurrences of one entity that may be related to a single occurrence of the other entity. Because all relationships are bidirectional, cardinality must be defined in both directions for every relationship.

Cardinality

•     Cardinality Notations:

       

    

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