Predictive Analytics, Data And Data

Business intelligence (BI) merges architectures, tools, databases, analytical tools, applications, and methodologies. It also is context free like DSS. BI deduces the connections between business entities by evaluating copious volumes of historical data which supports decisions. BI has four major components consisting of a data warehouse, business analytics, business performance management, and a user interface.

Since the 1970’s databases and report generators have been used to aid business decisions. In the 1990’s technology in this area improved. Now technology such as Hadoop has gone another step with the ability to store and process the data within the same system which sparked new buzz about “big data”. Big Data is roughly the collection of large amounts of data – sourced internally or externally - applied as a tool – stored, managed, and analyzed - for an organization to set or meet certain goals.

This is one of the greatest challenge that is acting as a key barrier in widespread adoption and pervasive use of the tools fostering for development and progress in this field of study. The real time of the big data analytics would require some of the unique features and special computing powers and potentials (Gantz, 2012). Tools have to made specially advanced so as to incorporate process terms in real time (Chen, 2012). Every business oriented organization should be transformed into information centric (Kaisler, 2013) to focus upon the real time data analysis in terms of both input and output.

New organizations can leverage Big Data in such a way that it provides benefits that can prove valuable especially to an organization seeking to establish itself and grow. One of these benefits is faster and better decision making. Particularly with the analytics of Big Data, organizations have access to real-time data

What is Business Intelligence (BI)? BI is the way that using modern data warehouse technology, online analytical processing, and data mining to analysis the data then achieve business value. As a tool, BI is used to deal with the existing data in the enterprise, and convert it into knowledge, analysis and conclusions, and then help the decision-maker to make a right and wise decision.

Business Intelligence is gaining popularity in many organizations and companies. Business Intelligence solutions are developed to help the organizations understand their customers, activities and performance. BI solutions act as measurements units for a business or an organization. Deficient intelligence leads to weak decision making. BI has

How much will organizations spend on business intelligence (BI) in 2016? According to Gartner, organizations are projected to spend almost $16.9 billion in 2016. That is an increase of 5.2% over 2015 sales . Business Intelligence plays a big role in today’s business, it now used in every part of the organization from supply chain to human resources (www.gartner.com). This paper will compare two business intelligence software tools. First, I will define business intelligence (BI). Then, I will examine the features of each of the BI tools. Next, I will discuss the pros and cons of each software. This paper will focus on the capabilities and costs for each BI software. Lastly, this paper will indicates which choice was made and an explanation of that choice.

Business Intelligence (BI) is defined by IBM as, “the discipline that combines services, applications and technologies to gather, manage and analyze data, transforming it into usable information to develop insight and understanding needed to make informed decisions.” (IBM.com, 2006) In its most basic form, BI is an umbrella principle that synergizes the core understanding of your business, including all of its facets, and acting on what that foundation is made up of.

Business Intelligence can be defined as the combined form of developing and learning the data that has been collected from various sources and then analyzing it. Business Intelligence is also used to provide the data in an interactive access to the data which enables the business analysts to process out certain analysis by making necessary manipulations in the data. The data that gets manipulated and analyzed includes the historical data along with the current data along with the performance levels and the situations through which the analysts are able to make precious insights that can be used to provide solutions that can result in benefitting the organization. Thus, Business Intelligence is basically taking actions based on the decisions that are taken by considering the transformed or the manipulated data (Turban, Sharda, Delen, King, & Aronson, 2011, p. 08)

While BI has been a staple of IT infrastructure and database environments for decades, the rise of Big Data has created new requirements. The sheer volume of information requires specialized capabilities to just pull the data together. In addition, the speed of business no longer allows for the traditional IT-centric “gather

Big Data is an outgrowth of the proliferation of databases and massive data sets. The insights needed to more intelligently manage an organization can be found in the myriad of data sets that comprise a Big Data platform. The greatest challenge of Big Data is contextual intelligence supported by integration to legacy, 3rd party and homegrown application systems located throughout an enterprise (Jacobs, 2009). To get ot his level of proficiency in analyzing Big Data sets and databases, enterprises need Business Intelligence (BI) and analytics tools that can parse through terabytes quickly, finding patterns and analyzing massive amounts of data, then distilling it down to key

There is a lot of hype recently around big data and its potential. When leveraged correctly big data can provide many great benefits for organizations in almost any industry. Some organizations currently have a lot of big data they have gathered throughout their life but have no current way of leveraging this data. One of the ways companies are able to leverage all of their big data is through predictive analytics.

Business intelligence can provide companies with accurate data that can be analyzed to support business strategy therefore enabling companies to better predict effectiveness of their business goals and ultimately result in a business profit. “Business intelligence is already in use in many organizations today, by finance departments to analyze financial performance, sales and marketing to identify customer trends, and operations to enhance the efficiency of supply chains. Using real data helps them answer who, what, where, why and when of related performance” (Rylander,2009).

All in all, the notion of predictive analytics in a more advanced manner would probable change the way e-platforms work. Few of the currently implemented predictive analytics examples.

Regarding the Business Analytics we need to understand that it has different levels, based on the business value it has, and also based on the complexity to apply it. In regards to the complexity required to apply, we are moving from the descriptive type of analyzes through the predictive, into the