Business Intelligence (bi)

"In the early 1990s, Howard Dresner, then an analyst at the Gartner Group, coined the term business intelligence due to the growing need for applications designed to support decision making based on data collected. Nowadays, business leaders and top management have access to more data than ever before; however data by itself doesn’t generate insights. Business Intelligence (BI) Tools have become the go-to resource for helping companies harness the power of big data and analytics and make smarter, data-driven decisions. During the various years, there have been various definitions of BI according to its form, usage and the industry it is applied to. Many of them are focused only on the software used for business intelligence and neglect to include the primary goal of business intelligence. While the term is often heard in relation to software vendors, there’s more to BI than just software tools.

Literature Review

Business intelligence became a popular term in the business and Information Technology (IT) communities only in the 1990s. Business intelligence (BI) refers to a managerial philosophy and a tool used to help organizations manage and refine business information with the objective of making more effective business decisions (Ghoshal and Kim, 1986; Gilad and Gilad, 1986). Dresner (1988) defined business intelligence as the “concepts and methods to improve business decision making by using fact-based support systems.” The term BI can either be used to refer to the relevant information and knowledge describing the business environment, the organization itself, and its situation in relation to its markets, customers, competitors, and economic issues or to an organized and systematic process by which organizations acquire, analyze, and disseminate information from both internal and external information sources significant for their business activities and for decision making (Lönnqvist and Pirttimäki, 2006). In European literature, the term BI is considered a broad umbrella concept for competitive intelligence (CI) and other intelligence-related terms, such as market intelligence, customer intelligence,competitor intelligence, strategic intelligence, and technical intelligence. Indeed the term has been defined front several perspectives (Casado, 2004), however they all focus on a shared purpose, analyzing data and information.

As Gilad and Gilad (1986) have stated, organizations have collected information about their competitors since the dawn of capitalism. The real revolution is in the efforts to institutionalize intelligence activities. BI presents business information in a timely and easily consumed way and provides the ability to reason and understand the meaning behind business information through, for example, discovery, analysis, and ad-hoc querying (Azoff and Charlesworth, 2004). Today, business intelligence is defined by Evelson and Nicolson (2008) at the Forrester as “a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information usedto enable more effective strategic, tactical, and operational insights and decision-making.” Business Intelligence today is never a new technology instead of an integrated solution for companies, within which the business requirement is definitely the key factor that drives technology innovation (Ranjan, 2009).

Ranjan (2009) stated that the major challenge of a BI application to achieve real business impact is to identify and creatively address key business issues. After discussing the many definitions of BI, the question of why do companies use it naturally arises. The primary goal is tostay ahead of the competition and make the right decision at the right time. Those decisions can be made around pretty much any aspect of running a business, such as figuring out how to increase the effectiveness of marketing campaigns, deciding whether and when to enter new markets, and improving products and services to better meet customers’ needs. One of the key aspects of business intelligence is that it’s designed to put information in the hands of business users.

Organizations are required to make decisions at an increasingly faster pace, so today’s business intelligence tools help decision makers access the information they need without having to first go through the IT department or specifically designated data scientists.

BI includes several software for Extraction, Transformation and Loading (ETL), data warehousing, database query and reporting, (Berson et.al, 2002; Curt Hall, 1999) multidimensional/on-line analytical processing (OLAP) data analysis, data mining and visualization.

Data and Data Sources

Business intelligence all starts with the data. As mentioned in the introduction, businesses have access to more data than ever. Data sources can be operational databases, historical data, external data (from market research companies or from the Internet), or information from the already existing data warehouse environment. The data sources can be relational databases or any other data structure that supports the line of business applications. They also can reside on many different platforms and can contain structured information, such as tables or spreadsheets, or unstructured information, such as plaintext files or pictures and other multimedia information.

Extract, Transform, Load (ETL)

A key part of BI is the tools and processes used to prepare data for analysis. When data is created by different applications, it’s not likely all in the same format, and data from one application can’t necessarily be looked at in relation to data from another. In addition, if business intelligence is relied on to make critical decisions, businesses must make sure the data they are using is accurate. The process of getting data ready for analysis is known as Extract, Transform, and Load (ETL). The data is extracted from internal and external sources, transformed into a common format, and loaded into a data warehouse. This process also typically includes data integrity checks to make sure the data being used is accurate and consistent.

Data Warehouse and Data Marts

The data warehouse is the significant component of business intelligence. It is subject oriented, integrated. The ETL process ends with data being loaded into the warehouse, because when the data is contained within the separate sources, it’s not much use for intelligence. A data warehouse is a repository containing information from all the business’s applications and systems, as well as external sources, so it canbe analyzed together. A data mart as described by (Inmon, 1999) is a collection of subject areas organized for decision support based on theneeds of a given department. Similar to data warehouses, data marts contain operational data that helps business experts to strategize based on analyses of past trends and experiences. The key difference is that the creation of a data mart is predicated on a specific, predefined need for a certain grouping and configuration of select data. There can be multiple data marts inside an enterprise. A data mart can support a particular business function, business process or business unit. OLAP (On-line analytical processing) It refers to the way in which business users can slice and dice their way through data using sophisticated tools that allow for the navigation of dimensions such as time or hierarchies. Online Analytical Processing or OLAP provides multidimensional, summarized views of business data and is used for reporting, analysis, modeling and planning for optimizing the business. OLAP techniques and tools can be used to work with data warehouses or data marts designed for sophisticated enterprise intelligence systems.

Advanced Analytics

It is referred to as data mining, forecasting or predictive analytics, this takes advantage of

statistical analysis techniques to predict or provide certainty measures on facts.

Corporate Performance Management (Portals, Scorecards, and Dashboards)

This general category usually provides a container for several pieces to plug into so that the

aggregate tells a story.

Real time BI

It allows for the real time distribution of metrics through email, messaging systems and / or interactive

displays.

Discussion

Overall, Business Intelligence provides benefits to companies utilizing it. Initially, BI reduces IT infrastructure costs by eliminating redundant data extraction processes and duplicate data housed in independent data marts across the enterprise. For example, 3M justified its multimillion- dollar data warehouse platform based on the savings from data mart consolidation (Watson, Wixom, and Goodhue, 2004, pp. 202-216). Moreover, it can eliminate a lot of the guesswork within an organization, enhance communication among departments while coordinating activities, and enable companies to respond quickly to changes in financial conditions, customer preferences, and supply chai operations. Over time, organizations evolve to questions like “Why has this happened?” and even “What will happen?” As business usersmature to performing analysis and prediction, the level of benefits become more global in scope and difficult to quantify (Watson andWixom, 2007). Information is often regarded as the second most important resource a company has (a company's most valuable assets are its people). So when a company can make decisions based on timely and accurate information, the company can improve its performance. However, there are also a few issues regarding Business Intelligence. Firstly, Most BI benefits are intangible before the fact. An empirical study for 50 Finnish companies found most companies do not consider cost or time savings as primary benefit when investing in BI systems (Hannula and Pirttimaki, 2003). The hope is that a good BI system will lead to a return at some time in the future. Secondly, experts view BI in different ways. Ranjan (2009, pg 62-63) is of the opinion that to data mining experts BI is set of advanced decision supportsystems with data mining techniques and applications of algorithms, while to statisticians BI is viewed as a forecasting and multidimensional analysis based tool. Data warehousing experts view BI as supplementary systems and is very new to them. These experts treat BI as technology platform for decision support application. Third, very few organizations have a full-fledged enterprise data warehouse. The main key to successful BI system is consolidating data from the many different enterprise operational systems into an enterprise data warehouse. Berson (2002) emphasizes that in view of emerging highly dynamic business environment, only the most competitive enterprises will achieve sustained market success. The organizations will distinguish themselves by the capability to leverage information about their market place, customers, and operations to capitalize on the business opportunities.

Conclusions and Future Study

The business intelligence (BI) has evolved over the past decade to rely increasingly on real time data. Enterprises today demand quick results and it is essential that not only is the business analysis done, but also actions in response to analysis of results and instantaneously parameters’ changes of business processes. The paper explored the concepts of BI, its components, benefits and issues of BI. It is importantto examine the impact BI has on each individual company and on the economy as a whole. The possible future of Business Intelligence lies in cloud computing. Security, data protection, lack of control, and several other barriers prevent widespread adoption of the BI; however cloud computing promises significant benefits, which need to be maturely and reasonably assessed.