BUSINESS INTELLIGENCE: CONCEPTS, COMPONENTS TOOLS, TECHNIQUES ... - IJARIIE

Vol-3 Issue-3 2017

IJARIIE-ISSN(O)-2395-4396

BUSINESS INTELLIGENCE: CONCEPTS, COMPONENTS TOOLS, TECHNIQUES,

BENEFITS AND CHALLENGES

Anand Ramesh Gupta Student, MCA SEM VI

DES's NMITD Mumbai, India anandrkg005@

Abstract:

For companies keeping direct contact with large numbers of customers, however, a growing number channel-oriented applications (e.g. e-commerce support) etc create a new data management challenge: that is effective way of integrating. The decision supports of system to execute information you can discover in efficient business processes & hidden patterns. Identify areas of strength and weakness. Discover new opportunities.

Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, business performance management, benchmarking, text mining, and predictive analytics. Business intelligence aims to support better business decision making

Keywords--Business Intelligence, OLAP, OLTP , Data warehousing, Dimensions, Cube, Data marts, ETL, EIS, DSS,

ERP , CRM.

1 INTRODUCTION

The word Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information. The reason of Business Intelligence is to support better business decision making. the fundamental, Business Intelligence systems are data-driven Decision Support Systems (DSS). Business Intelligence is sometimes used apparently identical briefing books, report and query tools and executive information systems.

Business intelligence existed before technology. Today it`s understood as a set of analyses that derive value and insight from data.

Cyclopaedia of Commercial and Business Anecdotes contains the first known usage of the term business intelligence. He uses it to describe the way that a banker, Sir Henry Furnese, succeeded: he had a perception of political issues, lack of stability, and the market before his competitors.

Throughout Holland, Flanders, France, and Germany, he maintained a complete and perfect train of business intelligence, Devens writes of Furnese. The new was thus received first by him.

Furnese ultimately used this advance knowledge to duplicitous ends and became renowned as a corrupt financier. The idea of gathering information on business conditions, however, was a seed that would grow. Development until 1958 Technology did not advance to the point where it could be considered an agent of business intelligence until well into the 20th century. It was with the 1958 publication of a landmark

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article on the subject, written by IBM computer scientist Hans Peter Luhn , that the potential of BI was recognized.

The article, titled A Business Intelligence System, described detail an automatic system developed to disseminate information to the various sections of any industrial, scientific, or government organization. In the wake of the post-World War II boom, such sectors required a way to organize and simplify the rapidly growing mass of technological and scientific data. Luhn also cited Webster`s Dictionary definition of intelligence: the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.Essentially, this cut to the core of what BI is: a way to quickly and easily understand huge amounts of information so that the best possible decisions can be made by Luhn`s work did more than introduce and expand the possibilities of a concept. His research established methods that were built upon to create some of IBM`s touchstone analytical systems.

Today, he is popular as the Father of Business Intelligence. Advancements and Evolution into the late 1980`s with the advent of computers in the business world, companies finally had an alternative to storing data on paper.

IBM`s invention of the hard disk in 1956 revolutionized data storage. Floppy discs, laser discs, and other storage technologies meant that just as more and more data was being created, so too were there more and more places to store it.

This spawned the creation of the first database management systems, collectively referred to as decision support systems (DSS). By the 1970`s a few BI vendors came up with tools that made accessing and organizing this data achieving.

But it was a new and clumsy technology. Most importantly, it was very difficult to use. In 1988 international conference aimed to streamline data processes. The several Data Analysis, held in Rome, was a landmark in simplifying BI analysis.

Turning Points in the 1980`s and 1990`sThe modern phase of business intelligence began immediately after the 1988 conference. In 1989 Gartner analyst Howard Dresner again brought the phrase business intelligence into the common vernacular. He employed it as a general term to cover for data storage and data analysis, names like DSS and executive information system (EIS).

Competition from more vendors in the field led to advances including data warehouses. This new tool improved the flow of data as it moved from operational systems to decision support. Data warehousing drastically reduced time it took to access data. Data that traditionally had been stored in multiple places was now all in a single location.

Along with this development came supplemental facets of BI data warehousing that are staples of BI today. These included Extract, Transform, Load (ETL) tools and Online Analytical Processing (OLAP) software.[1]

In later years, this phase of development became known as business intelligence 1.0.

1.1 Business Intelligence 1.0

As business intelligence became a commonly known word in the late 1990`s and early 2000`s, dozens of new vendors hit the market. During this period, there were two basic functions of Business Intelligence: producing data and reports, and organizing it and visualizing it in a presentable way. Yet there remained two significant issues holding back this developing phase of the technology: complexity, and time. multiple projects were owned by the IT department, meaning that most users were still not capable of executing BI tasks on their own. Current BI tools had not been developed with anyone but experts in mind, and large-scale analytics training was required to gain insights. And because data was siloed, it took more time to formulate and deliver reports to decision makers. Only knowledgeable person technical experts were able to utilize advanced data analysis software. Tools began to evolve to non-technical users, but it happened slowly.

1.1 Business Intelligence 2.0

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The dawn of the 21st century marked a distinct turning point, as technologies developed to address issues of both complexity and speed. They were also provide support to the onset of Cloud-based programs that expanded and simplified the reach of business intelligence platform.

Business Intelligence 2.0 included a host of different technologies such as real-time processing, which incorporated information from events as they happened into data warehouses, allowing companies to make decisions based on the most recent information available.

Other technologies that came into play included self-service access for non-expert users, meaning that employees could now complete projects without interference from the IT department.

The exponential growth of the Internet supported and advanced these developments, in part through the genesis of social networking tools. Facebook, Twitter, and blogs gave users very simple and very quick ways of sharing ideas and opinions. It also provided a way for users to evaluate methods and software, and more broadly spread a basic understanding of the different uses of business intelligence. The more that people communicated, the more that they understood in it. By 2005, the increasing interconnectivity of the business world meant that companies need real-time information, for a host of reasons. Chiefly they needed to keep abreast of the competition, and understand what their consumers wanted and what they thought of their company. [1]

BI was no longer an added utility, or a mere advantage. It was becoming a requirement for businesses looking to stay competitive, and even to remain afloat, in an entirely new, data-driven environment. Empowering End Users into the Modern Day The agility and speed of the mid-2000s business intelligence platform has undergone an intense refining process.

Figure 1: Architecture of Business intelligence

Tool specification, expanding self-service options, and improving visualization are three of the most important traits of the next frontier of BI evolution. BI tools in the current day are often designed with a specific industry in mind, be it healthcare, law enforcement, or even professional sports. Known as software virtualization, this growth of industry-specific tools

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has contributed significantly to increased adoption of business intelligence. Self-service tools and visualization features rely on one another for their growth. The big data revolution and explosion of the Internet left organizations with more data than before. Each person creates increasingly large amounts of information. Over 204 million emails are sent per minute.

Companies need even more visualization tools to actionably make sense of it. Visualization tools began to evolve to include the end-user even more. More platforms empowered users to complete self-service access, meaning that they could explore and utilize their data on their own, without training.

1.2 Cloud BI and Mobile BI

As more companies offered these unique, cutting-edge attributes became the only way to stay ahead of the curve.Vendors experimented with faster and cheaper tools.

One way to achieve both was through cloud BI, which hosts the software on the Internet, reducing storage costs and making access to organizational data and insights faster and more convenient. Tangential to the cloud is the rise of mobile-empowered platforms, which allows users to work with BI on-thego on smartphones, tablets, and other devices. As tools are perfectly improved, they are also being made simpler and more convenient, encouraging and wider changing.

2. Components of BI :

The research of B.I have the concepts of BI, its components, Emergence of BI, Benefits of BI, Factors influencing BI, Technology requirements, Designing and implementing business intelligence, Cultural imperatives, and various BI techniques.

This paper would be useful for researchers in the field of BI to understand the basic concepts of the OLAP, OLTP, ETL,CUBE and dimension

SQL Server Integration Services (SSIS): Programmable objects for moving, copying and transforming data, also (DQS) Data Quality Services.

SQL Server Analysis Services (SSAS): is BI stack, to develop Online analytical processing solutions. In simple terms, you can use SSAS to create cubes using data from data marts / data warehouse for deeper and faster data analysis.

SQL Server Reporting Services (SSRS): client components as in creating, managing and deploying tabular, matrix, graphical, and free from reports.

Master Data Service (MDS): To manage domain (Products, customer, accounts, & industries hierarchies, granular security, transaction, data versioning and business rules as well).

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3.Management's tools:

SQL Server Data Tools (SSDT) Provide an IDE for BI components, ssis, ssas, ssrs. in Microsoft business intelligence.

Business Intelligence Tools Business intelligence tools are a type of application software designed to retrieve, analyze, transform and report data for business intelligence. The tools generally read data that have been stored in past, often, though not necessarily, in a data warehouse or data mart.

? Spreadsheets ? Reporting and querying software: tools that extract, sort, summarize, and present selected data ? OLAP: Online analytical processing ? Digital dashboards ? Data mining ? Process Visualization ? Data warehousing ? Local information systems Standalone tools | suites of tools | Components of ERP systems |

Components of software targeted to a specific industry | Data warehouse appliances.

4. BI Competitors:

Pentaho, zap business, BIRST, SAS BI, TIBCO, Dataramo, Sisense, IBM CognosAnalytis, Sap Business object, tableau Server, Oracle BI 12c.

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Figure 2: BI Competitors



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