Big data analytics and enterprise decision-making in modern organizations
The level of uncertainty and risk that pervade many enterprises today is growing, as the dynamics and economics of markets are changing rapidly. The many rapid, turbulent structural changes in industries is also leading to a greater reliance on analytics and the nascent area of Big Data as well. The potential of this second area, Big Data, is in determining patterns in massive data sets that have in many cases been collected for decades within enterprises. The abundance of data within enterprises, when combined with Big Data aggregation and analytics techniques, can be used for drastically reducing risk and uncertainty in even the most challenging and fast-moving industries. Big Data is being hyped heavily by analytics systems and enterprise application providers as well, as this category of software allows for the use of long-standing analytics and business intelligence (BI) tools expanded supporting larger data sets. Many companies today are working to create enterprise-wide platforms for managing massive data sets, many of them integrating legacy and 3rd aprty databases many of which have never been integrated into a broader platform strategy before (Jacobs, 2009). These larger data sets and their inherent complexity make the overall analysis, aggregation, creation of taxonomies and customizing of reports challenging and difficult to achieve with the baseline or current set of analytics and BI tools available today however. The continual evolution of these applications and the fine-tuning of specific aggregation technologies including Hadoop and Map Reduce (Jacobs, 2009) have also contributed to making Big Data a more strategic foundation fro decision making. Enterprises are facing greater time and cost constraints than ever before, which also leads to the create and continually invest in larger data sets, analytics, BI and advanced reporting technologies all orchestrated to make the most of the terabytes of legacy data companies have (Chisholm, 2009).
The rapid development of analytics, BI and data reporting platforms and tools has led to a level of innovation in enterprise software that is making it possible for enterprises to get more insights from the terabytes of data they have been collecting for decades. This category of software tools include analytics, BI, data visualization, product lifecycle data and predictive analytics all orchestrated to create a common platform for reducing risk while bringing greater intelligence into an organization (Ericson, 2010). As is the case with any high growth enterprise software category, there is an abundance of hype surrounding what these analytics and BI platforms and tools are and aren't capable of. The tendency to overlook the very difficult processes to extracting, transferring and loading (ETL) data from legacy systems and creating a highly effective ecosystem of data is very expensive for companies who have never attempted this before. Further, the methodologies needed for consistently and accurately capturing the data within a given enterprise require a level of discipline that many companies are lacking in their core process areas (Jacobs, 2009). Simply put, it is very hard work to capture all the heterogeneous sources of data throughout an enterprise, from the legacy systems to the 3rd party databases, and then perform ETL functions on them in order to create a new system of record for the entire organization to make use of (Ericson, 2010). Yet for organizations to capitalize on the potential that exists from these many diverse forms of information, intelligence and insight throughout their businesses, they must take the time and effort to create a unified, highly integrated single system of record to galvanize their Big Data strategies together (Jacobs, 2009).
The objective of this analysis is to provide the arguments for and against having Big Data included in the strategic decision-making process within an enterprise. The strengths are presented first, followed by the weaknesses of this approach to harnessing data throughout an enterprise. The strengths and weaknesses are next compared and an assessment provided. One of the most prevalent technologies used for accomplishing Big Data analytics and intelligence are MapReduce and Hadoop, two aggregation technologies that can compress terabytes of data into taxonomies and quickly analyze them (Jacobs, 2009).
Crowding and its effects on human behavior and stress
Important points to consider regarding crowding include its definition, predisposing attributes, setting conditions, correlation studies, theories, practical effects of crowding, and the author's conclusions.
The European Union's economic and political influence on Ireland's development
EUROPA, the Internet informational site for the European Union, notes that one of the inspirations for the European Union was that, for centuries, "Europe was the scene of frequent and bloody wars," and it was hoped…
Break-even analysis and financial ratios for Cat and Dogs, Inc.
¶ … fixed costs that Cat and Dogs, Inc. have include rent and executive salaries, which are paid no matter how many units the company builds. The company's total fixed costs are $113,200 per month.