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Business Intelligence and Data Warehouse

HomeDev BlogBusiness Intelligence and Data Warehouse
HomeDev BlogBusiness Intelligence and Data Warehouse

Business Intelligence and Data Warehouse

posted in DevBlog by Michael Díaz


Data is crucial raw material in this, the information era. Data storage and management have become the focus of database design and implementation. Today, the reason for collecting, storing, and managing data is to generate information that become the basis for rational decision making.

Data warehouse is a new storage facility that extracts or obtains its data from operational databases as well as from external sources to provide a more complete data pool. Business intelligence is the collection of best practices and software tools developed to support business decision making in this era of globalization, emerging markets, changes, and regulations. Business intelligence encompasses tools and techniques such as data warehouses among others, with a more complete focus on incorporating them to form a company – wide viewpoint.

Why business intelligence and data warehouses?

At the beginning of the information technology era, the decision support systems (DSS) were originally developed to facilitate the decision-making process; but, it was difficult to extract the necessary information of those systems. As the time passes, organizations have a tendency to grow and prosper as they gain a better understanding of their business environment. By checking the database, company management can develop strategies to meet the organizational goals, this is part of the data analysis. Data analysis can provide information about short-term tactical evaluations and strategies to the organization. (Turban, Sharda, Delen, & King, 2011)

Given the many varied competitive pressures, organization managers are always looking for a competitive advantage over product development and maintenance, service, market positioning, sales promotion, etc. To fulfill this needs, they have interest in making better support systems dedicated to facilitate the making of quick decisions in this complex environment.

Given the many varied competitive pressures, organization managers are always looking for a competitive advantage over product development and maintenance, service, market positioning, sales promotion, etc. To fulfill this needs, they have interest in making better support systems dedicated to facilitate the making of quick decisions in this complex environment.

 

Business Intelligence

Business intelligence is a term uses to describe a complete, consistent, and integrated set of tools and process used to capture, collect, integrate, store, and analyze data with the purpose of generating and presenting information used to support business decision making. The intelligence here is based on learning and understand the facts about a business environment.

Business Intelligence and Data Warehouse intelligence is a framework that allows an organization or business to transform data into information, information into knowledge, and knowledge into understanding. This business understanding allows users to make better business decisions based on the accumulated knowledge of the business as reflected on the recorded historical data. (Turban, Sharda, Delen, & King, 2011)

Business processes are the central units of operation in a business, and business intelligence include all of them. Business intelligence is a complex proposition that requires a deep understanding and alignment of the business processes, the internal and external data, and the information needs of users at all levels in an organization.

Business intelligence is not a product, it is a framework of concepts, practices, tools, and technologies that help a business to understand its core capabilities, provide snapshots of the company situation, and identify key opportunities to create competitive advantage between the markets. Business intelligence involves some general steps.

They are: (Rob & Coronel, 2006)

  1. Collecting and storing operational data.
  2. Aggregating the operations data into decision support data.
  3. Analyzing decision support data to generate information.
  4. Presenting such information to the end user to support business decisions.
  5. Making business decisions, which in turn generate more data that is collected, stored, etc. (the process is restarted, like a loop but with different data because the result of the previous data)
  6. Monitoring results to evaluate outcomes of the business decisions (providing more data to be collected, stored analyzed, etc.)

Business Intelligence Architecture

There is no single business intelligence architecture, but there are some general types of functionalities that all business intelligence implementations share. Business intelligence covers a range of technologies and applications to manage the entire data life cycle from acquisition to storage, transformation, integration, analysis, monitoring, presentation, and archiving. The business intelligence architecture is composed of data, people, processes, technology, and the management of those components. (In Lih Ong, 2011)

As the figure above present, business intelligence integrates people and processes using technology in order to add value to the business. Such value is derived from how end users use such information in their daily activities, and in particular, their daily business decision making. Business intelligence tools focus on the strategic and tactical use of information. Because technology is not enough, business intelligence uses an arrangement of the best management practices to manage the data as a corporate asset. One of this is the master data management techniques.

This master data management is a collections of concepts, techniques, processes, definitions, and management of data elements inside the organization. Master data management ensures that all company resources that operate over the data have uniform and consistent views of the company data.

In order to monitoring the business health, business intelligence uses metrics as the key performance indicators. These indicators are computable measurements that evaluate the company efficiency or success in reaching its strategic and operational goals.

Business intelligence architecture have some basic components that form part of its infrastructure. Some of them are (see figure 1):

  1. ETL tools − Data extraction, transformation, and loading (ETL) tools collect, filter, integrate, and aggregate operational data to be saved into a data store optimized for decision support.
  2. Data Store − The data store is optimized for decision support and is generally represented by a data warehouse or a data mart. The data store contains two main types of data: business data and business model data. The business data are extracted from the operational database and from external data sources. The business data is stored in structures that are optimized for data analysis and query speed. The external data sources provide data that cannot be found within the company but that are relevant to the business, such as stock prices, market indicators, marketing information, and competitors data. Business models are generated by special algorithms that model the business to identify and enhance the understanding of business situations and problems.
  3. Data query and analysis tools − this component performs data retrieval, data analysis, and Business Intelligence and Data Warehouse 7 data-mining tasks using the data in the data store. This component is used by the data analyst to create the queries that access the database. Depending on the implementation, the query tool accesses either the operational database, or more commonly, the data store. This tool advises the user on which data to select and how to build a reliable business data model. This component is generally represented in the form of an OLAP1 tool.
  4. Data presentation and visualization tools − this component is in charge of presenting the data to the end user in a variety of ways. This component is used by the data analyst to organize and present the data. This tool helps the end user select the most appropriate presentation format, such as summary report, map, pie or bar graph, or mixed graphs. The query tool and the presentation tool are the front end to the BI environment.

In the presence of the very powerful business intelligence, the human component is still at the center of business technology. While business intelligence have a variety of components and tools the most important is its data warehouse component.


Data Warehouse

 Ponniah uses the definition originally made by Chuck Kalley and Bill Inmon that describe data warehouse as an integrated, subject oriented, time variant, nonvolatile collection of data that provides support for the decision making. (Ponniah, 2010) Those components are defined by Chuck Kalley and Bill Inmon in “The Twelve Rules of Data Warehouse for a Client/Server World” (Inmon & Kelley, 1994), and are:

  • Integrated. The data warehouse is a centralized, consolidated database that integrates data derived from the entire organization and from multiple sources with diverse formats. Data integration implies that all business entities, data elements, data characteristics, and business metrics are described in the same way through the enterprise. The data in the data warehouse must conform to a common format acceptable over the organization. This integration can be time-consuming, but once accomplished, it enhances decision making and helps managers better understand the company’s operations. This understanding can be translated into recognition of strategic business opportunities.
  • Subject Oriented. Data warehouse data are arranged and optimized to provide answers to questions coming from diverse functional areas within a company. This form of data organization is quite different from the more functional or process-oriented organization of typical transaction systems. Data warehouse designers focus specifically on the data rather than on the processes that modify the data.
  • Time Variant. In contrast to operational data, which focus on current transactions, warehouse data represent the flow of data through time. The data warehouse can even contain projected data generated through statistical and other models. It is also time-variant in the sense that once data are periodically uploaded to the data warehouse, all time- dependent aggregations are recomputed.
  • Nonvolatile. Once data enter the data warehouse, they are never removed. Because the data in the warehouse represent the company’s history, the operational data, representing the near-term history, are always added to it. Because data are never deleted and new data are continually added, the data warehouse is always growing.

The data warehouse is usually a read only database optimized for data analysis and query processing. The data are extracted from various sources and are then transformed and integrated. This process of extracting, transforming, and loading the aggregated data into the data warehouse is known as ETL, as mentioned before.

Because creating data warehouses requires time, money, and management effort, companies generate a small group of data stores called data marts for the small groups inside the organization. A data mart is a small, single subject data warehouse subset that provides decision support to a small group of people. Data marts and data warehouses can coexist inside a business intelligence. The only difference between data mart and data warehouse is the size and scope of the problem being solved.

William Inmon and Chuck Kelly created twelve rules that define a data warehouse and they are: (Inmon & Kelley, 1994) (Rob & Coronel, 2006)

  1. The data warehouse and the operational environments are separated.
  2. The data warehouse data are integrated.
  3. The data warehouse contains historical data over a long time.
  4. The data warehouse data are snapshot data captured at a given point in time.
  5. The data warehouse data are subject oriented.
  6. The data warehouse data are mainly read-only with periodic batch updates from operational data. No online updates are allowed.
  7. The data warehouse development life cycle differs from classical systems development. The data warehouse development is data-driven; the classical approach is process-driven.
  8. The data warehouse contains data with several levels of detail: current detail data, old detail data, lightly summarized data, and highly summarized data.
  9. The data warehouse environment is characterized by read-only transactions to very large data sets. The operational environment is characterized by numerous update transactions to a few data entities at a time.
  10. The data warehouse environment has a system that traces data sources, transformations, and storage.
  11. The data warehouse’s metadata are a critical component of this environment. The metadata identify and define all data elements. The metadata provide the source, transformation, integration, storage, usage, relationships, and history of each data element.
  12. The data warehouse contains a chargeback mechanism for resource usage that enforces optimal use of the data by end users.

Those rules capture the complete data warehouse life cycle, from its introduction as an entity separate from the operational data store to its components, functionality, and management processes.


Conclusion

Analyzing data to predict market trends of products and services, and to improve the performance of enterprise business systems, has always been part of running competitive business. Business intelligence tries to address these needs by providing software tools that are customized for end business users, and deliver business visualizations in real time at the point of the decision. Business intelligence in parallel with data warehouses define new ways to analyze and present decision support data for any particular organization.

Originally published as the paper: Business Intelligence and Data Warehouse

by Michael Díaz Rivera | Spring 2013

devblog bi businessintelligence


18 03, 15

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1 comment

Jayson Camack

Jayson Camack says: posted on July 02, 2015 - 06:00 am

Great website, really enjoyed it.

 

 

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