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Business Intelligence is all the processes, which involve collection, organization, analysis and utilization of business viable information with an aim to achieve comparative advantage by players in a competitive business platform. Business Intelligence (BI) as a concept is as old as business practice itself. The careful organization, categorization and emphasis on practices that are known as Business Intelligence, however, are modern scenario. In a market situation where each business enterprise wants to succeed, there is always competition as a result of which some players may be forced out of business, while others succeed and dominate businesses. To survive, businesses engage in implementing business intelligence findings. However, it is estimated that above 70% of these kinds of initiatives mostly fail.
There are some major reasons why implementation of BI fails. First, lack of planning for the associated change makes it hard for firms to realize its target realization. These plans may involve licensing implications, legal bottlenecks and industry standards. Secondly, many business intelligence initiatives fail due to lack of complete research into the subject matter. In a gas and oil strategy formulation, for instance, the research team giving advice on BI implementation may fail to get accurate information regarding new field exploits, leading to massive losses in explorative new well sinking. Lack of skills, funds or labor may be other relations for failure. In addition, changing technologies and norms affect more than 80% of BI projects between formulation and implementation time.
Business Intelligence Solutions
BI solutions are all those viable measures that can be taken to mitigate the implementation challenges experienced due to, among others, the reasons mentioned above (Chaudhuri Dayal & Narasayya 2011). Business Intelligence maybe used by varied organizations or persons as long as they are in any form of business, such as corporate, private entities, government agencies or small businesses. It is notable that BI finds many applications in its history. Some of the most widely known ones are in Online Analytical Processing (OAP), Data Mining (DM), analytics, process mining, Business Performance Management (BPM), Complex Event Analysis (CEA), benchmarking and predictive analytics, among others (Chaudhuri Dayal & Narasayya 2011).
While some of these applications are older in nature, some of them are still emerging and rely on upcoming analytical software. For instance, data mining, though popularized in the current computer era of widespread Information Technology usage, may traditionally have been known in other terms, such as information trends analysis. Similarly, business performance management is as old as book keeping (Kernochan 2011).The more recent applications include online analytical processing and complex event analysis or processing. While the solutions themselves are as different as their application sectors, any business intelligence solution is aimed at providing an avenue for a business to retain customers, find a niche in emerging markets, remain competitive in a dynamic environment and find growth. This combination helps businesses face uncertainties and survive crises (Kernochan 2011). The section below will focus on the oil and gas market platform and on major BI implementation strategies used in the last three decades.
The Oil industry, like any other industry, is driven by competition and regulations. For this reason, each player in the Oil/Gas domain must continually monitor the market trends to maximize profits, retain competitiveness and avoid sanctions. Also, one major BI strategy of every oil producer is reserve implications (Kernochan 2011). This market, unlike other markets, is regulated by formation of the Organization of Petroleum Exporting Countries (OPEC), which limits the market freedom of individual countries that export oil. Research will target on ATP Oil and Gas Corporation, a company exploring for oil, as well as gas, in the North Sea and Gulf of Mexico which went bankrupt on August 17, 2012, following major losses in revenues (ATP Oil and Gas 2012).
ATP Gas and Oil was not alone; other companies in the oil and gas sector were adversely affected by the 2008-2009 global financial crisis with many either selling off to larger sector players like BP and Exxon Mobil (Durden 2012). The Gas and Oil sector depends on availability of sustainable, economically viable gas and oil resources for success. However, most reserves are getting depleted fast, and exploring companies are facing challenges of balancing the conflicting issues of exploration and production costs with economic viability and government regulations. To stay in business, most companies have either focused on offshore rigging and exploration, or turned to renewable energy alternatives (Johnson 2012). ATP Gas and Oil decided to focus on offshore exploration, which is very capital intensive. ATP was founded in 1991 in Texas, and has mostly been successful in its operations off-shore and on-shore. In 2010, the company had invested $800 million in an undersea production called Titan (ATP Oil and Gas 2012). The U.S government, however, banned off-shore production in the same year following the BP oil disaster in Prudhoe Bay, Alaska (Durden 2012). This halt on offshore operations caused ATP Gas and Oil losses of $349 million, and a net debt at the time of filing for bankruptcy on $3.5billion (ATP Oil and Gas 2012).
ATP’s Business Intelligence Prior to Bankruptcy
New Markets Analysis
This BI strategy led ATP to the venture into offshore rigging, seeing that very few competitors were willing to go offshore. In 1999, ATP was awarded for Best Field Improvement Project (BFIP) by Hart’s Magazine for making the longest umbilical hydraulic system in the world at 11 km, to support its 500 feet undersea well. ATP capitalized in analytical business intelligence, as well as process mining and business performance management in its expansion policy. Thus, in 2010, the company acquired a 1.5 billion dollar financing to expand its operations, with a target of doubling its output from 50000 barrels a day within the same year. All these strategies were carefully crafted, and implementation had begun at the time of its downfall. This leads to the question, why did ATP’s Business Intelligence implementation fail (Johnson 2012).
This section will analyze the conditions under which ATP went bankrupt, and the relationship between its BI implementation strategy and its downfall. To do this, the section will carefully analyze the mechanism of BI implementation in general and the ATP case in specific. Business Intelligence generally works through observing trends in a firm’s operation industry to enable it document many past observations and consequences. It looks at government influence on industry, consumer trends, demand and supply patterns, special or coming occurrences and most importantly, the ability to handle unforeseen crisis. Once these factors have been laid down, a strategic implementation schedule is drafted with a clear focus on goal achievement, and proper and timely checks for stability (Wik 2011).
The implementation is usually in phases, which are determined through expert consideration of, among other things, existing external conditions (Franklin 2012). Important considerations in the ATP scenario include investigating if all important aspects of BI were considered, analyzing the company’s preparedness to handle the crisis and whether its ambitious borrowing was backed by extensive business research, particularly through risk analysis (Franklin 2012). To obtain this information, a study of ATP’s website, as well as journals, reports and news articles were done. The data sources include www ATP website, Yahoo’s Company profiling website, Reuter’s news website and the U.S. Government Energy Administration website.
ATP failed in its business intelligence implementation to put into consideration the effect of unexpected government regulations in its key industry (ATP Oil and Gas 2012). The US government announced a freeze on off-shore rigging until all proper safety measures were put to enhance dangers of oil spill in ocean waters following a spill from British Petroleum’s exploration in Alaska. This was not the first time that oil spilled had occurred raising major concerns and pressure mounting on governments to control the sea pollution. The fact that ATP oil was undertaking a risky project should have prompted its management to put necessary control measures in the event of such occurrences as oil spills.
In addition, ATP was totally unprepared to remain afloat in the occurrence of an economic downturn. It lacked the financial strength to cater for its overhead in the months during which no rigging was taking place (ATP Oil and Gas 2012). This is a lesson ATP should have learnt from the 2008-2009 financial crisis, just two years prior to its borrowing. Companies with high leverage are always at higher risks of insolvency in cases of asset depreciation of profit decline, yet the company borrowed $1.5 B against its equity of $596M (Yahoo Finance 2012). Another significant failure was ATP’s lack of a proper insurance policy against the occurrence of a spill. Such an occurrence has already known to cause major losses and to be frequent enough not just in pipelines, but also in oil tankers. Such insurance would have boosted ATPs’ earnings during the production halt.
Specific Findings on Causes of BI Implementation Failure
While the general failures reasons are broad, there always exist non-mainstream causes for BI implementation failure. In case of ATP, the following ideas arise.
In May, 2011, a federal Court Judge gave a ruling, indicating that ATP license applications were unduly delayed, thereby causing major overhead losses to the company. In addition, ATP president Paul Bulmahn blamed the US government for ATP’s bankruptcy, citing poor administrative policies and failure to regulate interest cuts during the months of no operation (Durden 2012). While this is an isolated case, it may be true to note that there are many scenarios where BI fails due to unfair practices such as licensing delays, biasness on the part of industry regulators including conspiracies to put unwanted competition out of business. There are also issues to do with vested interests in panel members, who are legally required to be non-partisan (Durden 2012).
Rigidity on Implementation Decisions
ATP’s management had witnessed the global depression in which many companies were forced to declare bankruptcy. Worst hit companies were those that relied mainly on debt as a source of funds (had high leverage). However, it is notable that ATP acquired financing to bring its leverage to at least five. If the firm management learnt from mistakes which had affected other companies in 2008-2009, there would have been the need to avoid over-leveraging, thus, seeking for other alternatives (Durden 2012).
Business Intelligence is an important tool for enhancing business survival and growth in an era of globalized competition and enhanced cultural diversity (Watson & Wixom 2007). However, the implementation of BI is perhaps more important than intelligence gathering, a factor that is evidenced by implementation failure rate approximated to range from 70 to 80 %. This realization also leads to another truth that the traditionally accepted reasons for implementation failure are easily replaced by newer, more case specific occurrences. This means that implementers should look at challenges in a more dynamic way.
Another key factor to note is that, companies attach so much focus on BI implementation that they neglect the very basic business rules (Rodriguez, Daniel, Casati & Cappiello 2010). BI initiatives are usually well-researched concepts intended to revolutionize some aspects of a business, but the fundamental rules of entrepreneurship remain the same, thus, ought to not be treated as a lesser priority during the BI implementation (Rud 2009).
Comparison and Suggestions
The ATP case failure reasons are to some extent related to the industry reasons for BI implementation failures, although, some reasons are specific only to ATP. The lack of proper financial backing is a general reason, as well as the specific reason for ATP failure. If ATP had held sufficient reserve funds, it would have used the fund to survive the production freeze months. In addition, lack of proper planning, which affects many firms’ BI implementation, contributed to ATP’s failure to sustain the off-shore production (Rud 2009). The third similarity in cause of failure is change in regulations. The government regulations that halted off-shore rigging were unexpected to ATP. Unfair practices, however, was a factor which affected ATP’s profitability from its BI implementation and this is a non-mainstream cause of BI implementation failure (Litkowski 2012). Rigidity or failure to learn from others mistakes is another case specific to ATP, though it may be applicable to other sectors and companies (Michalewicz 2007).
The study of ATP Gas and Oil scenario reveals additional information to broaden the traditionally held beliefs regarding the failure in implementing Business Intelligence initiatives. The case, in addition to ascertaining the general reasons why BI may fail, has indicated that it is possible for newer challenges to arise. These challenges may not expressly be foreseeable, but necessary flexibility on the part of implementing companies may help to reduce their impacts. It is recommendable that, businesses and corporations implementing business intelligence initiatives should strive to do, in addition to general evaluation and analysis, a scenario sensitive analysis to reveal potential hindrances that may arise (Durden 2012).
In addition, scholars in the respective business sectors should continually upgrade information regarding common BI failure scenarios for businesses. This kind of tradition will help investors and entrepreneurs take advantage of expert analysis before venturing into business (Liebowitz 2006). The globalization of many sectors have brought with it a paradigm shift in the way different market cultures operate, and consequently, how businesses perceive development versus risk in various market segments. Rewriting the Business Intelligence implementation rules will contribute to business enterprises’ health in this dynamic global business platform (Litkowski 2012).
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