The era that we live in, in the year 2015 is considered the “big data” era. Industries all over the world are analyzing data and determining marketing and business processes to attract consumers. Data analysis which started off on a much smaller scale today can be used in much broader aspects from coupons you receive in your email, to advertisements you see when you use applications on your smart phone data also can be used to determine the frequent of a customer to a particular store or website. These are both processes of data analytics being used and conveyed in a way to attract customers or satisfy consumer needs. Data analysis focuses on finding the specific data to answer your question, understanding the processes underlying the data, discovering the important patterns in the data, and then communicating your results to gain optimal results. Data is not a new concept in any form, the technology of today’s world makes obtaining and analyzing data easier. The recent decades have seen a fundamental change in the model of data analysis. IMB Tech Trends Report (2011) identified business analytics as one of the four major technology trends in the 2010s. According to Chen (2012) Business intelligence began its practice in business and IT communities just a few decades ago in the 1990s. In the 2000s business analytics was introduced to represent the key analytical components in business intelligence. The business intelligence and data analytics previously adopted in an
The data analytic process is one in which a large amount of information is collected using software specifically geared towards collecting, identifying and storing information for use by the company. The information is gleaned from different forums, with social media being the most rich and useful. The information is then quickly sorted and organized for use by the collecting agency (Turban, Volonino, Wood, & Sipior, 2002, p. 6). The use of data analytics really took flight in 2010 when different companies offered software that enabled a company to implement their own data analytics. This led to better marketing campaigns, improved customer relations and it gave companies using the software a bigger advantage over their competitors (Savitz, 2012).
In broad terms, data analytics is the process by which any company collects information about its targeted consumer-base, makes sense of the data collected, and based on the resulting assessment of that data, enables business management to determine if change is necessary to better reach the customer. While that description may seem simple enough, the means by which that data is collected has radically changed in the last twenty years and in which a new era in data analytics capabilities as emerged. Not long
Practice research is building theory from practice; practice experience improves research findings, practice, policy, and social service delivery from and by previous applications. These approaches are based on field research and practical experience in combination with research methodology. The definition of theory per our text is a “theory is an organized set of ideas that seek to explain a particular phenomenon” (8).
What is data analytics? How has its use in business evolved over time? What are the advantages and disadvantages of using data analytics within a specific company or industry? Are there any challenges or obstacles that business management must overcome in order to implement data analytics? If so, is there a strategy that can be used to overcome those challenges or obstacles? How has data analytics transformed the healthcare industry with regard to customer responsiveness and satisfaction? Within the next ten years, what do you think the trend in using data
According to Turban (2013), the processes required to conduct data analytics are both intricate and expensive. Data contains errors and incomplete information. Data is also unpredictable and repetitive. Dirty data costs time and resources to extract information that can be utilized for analysis. Experts must be able to detect and collect data that is reliable from any number of sources that are in various formats and this may pose a challenge to some companies.
Today, the world’s trend in operating business focuses on data availability to enact the best suitable decision to improve, develop, and increase business revenues. Moreover, the availability of data helps to monitor and control the quality of provided products or services. However, the availability of data without proper analytics operations would have no meaning (1). Data analytics provide an important aid to an organization to figure out their position in the market in comparison with their competitors. Also, data analytics helps to identify what is the organization’s competitive ability in the market, what they should bet on, and what they should strive for. With that being said, many of today’s most successful organizations utilize
Business analytics (BA) is a method combining skills, technologies, applications, and processes that is used by the company to gain valuable insight on the processes of the business by collecting statistical data. BA is used by the company to improve the business decisions and can be used to automate and optimize the business processes. A data driven company, such as SMB Computation treats their data as a corporate asset and uses it to gain a competitive advantage. For business analytics to be successful, skilled analysts who recognize the technologies and the companies, organizational guarantee to the data-driven decision-making must collect quality data (Rouse, 2010).
Big data and analytics are hot growth areas, not only for IT organizations, but for businesses across all industries.
In every long term strategic planning, many companies considered data collection and analysis as a fundamental activity. Big companies that strive to achieve a sustainable advantage over their competition made use of information management system to help them analyze their data. These activities have evolved to what is now known as business intelligence of BI.
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
Business thrive when they have the most accurate, up-to-date, and relevant information at their disposal. This information can be used for a plethora of pertinent markers in small and large businesses, relating to accounting, investments, consumer activity, and much more. Big data is a term used to describe the extremely large amounts of data that floods a business every day. For decades, big data has been a growing field, facing controversy on many levels, but as of late, it has been a major innovator in the challenge of making businesses more sustainable. Big data is often scrutinized for its over-generalization and inability to display meaningful results at times. When applied correctly, data analysis can bring earth-altering information to the table.
Competing on analytics is currently one of the most essential qualities for companies looking to gain a larger market share in their given industry. This is due to the fact that the easiest way for corporations to differentiate themselves from their direct competitors is to maximize efficiency through cohesive processes and decision making. Analytics is defined as, “The extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact based management to drive decisions and actions.” (Davenport, 2007, p.7) So in order for companies to compete on analytics they need to be willing to invest in the proper technologies that are able to compile all of this information and data into output that can be used
With the practice of data-driven marketing and decision-making becoming more mainstream in the business world, a new term, business intelligence (BI), has emerged. BI encompasses a mixture of applications used to analyze an organization’s raw data. Though it is much more than just comparing point A to point B, BI can be used to identify inefficient processes inside of businesses ready to be reengineered or uncover new revenue stream opportunities.
Data is being produced at a huge rate and 90% of the data which exists today were produced in the last two years. Thus, it is difficult to manage big data which are extremely large, structured/ unstructured data sets analyzed to find trends, associations, reports, etc. The biggest challenge today is to find the quickest and the most inexpensive tool to analyze the big data which consists of emails, videos, pdf, audio files, and tweets. Predicting future with being able to access and store real time data is the future of BI and big data analytics (Jamack, 2012). When BI reports are run using the data and queries, information is retrieved and it is called Descriptive Analytics. When the dataset is further analyzed and drawn inferences using statistical methods like correlation or regression then it is called Diagnostic Analysis. Based on this information when the possible outcomes are predicted it is called Predictive Analytics. Finally, Predictive analytics uses previously tested or predicted models which are put into a reiterated process to produce an anticipated outcome. Big data technology combines all of these analytics, along with being fast and efficient in handling real time data (Payandeh, 2013). Business Intelligence consists of different tools to make better informed decisions with the data they have. Traditional BI were focused on the OO of the OODA loop (Observe, Orient, Decide, Act) but the modern BI needs to directly integrate the Decide and Act since there is a
The field of business analytics has improved significantly because users are generating more data and the process of analysing this data has been enhanced too. Nowadays a data analyst can analyse large amounts of fast moving data from different sources and gain insights that were never possible to