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Big Data Analytics

by GBAF mag

Business Analytics refers to the set of skills, processes, and practices for ongoing iterative exploration and measurement of previous business performance to provide insight into how business units are performing relative to an organization’s goals and objectives. There has been a lot of recent talk about “The Business Case”. The Business Case is a common practice used by strategic management professionals as a means of presenting a framework for the organization to work within in order to reach its business objective. This framework generally includes five primary categories that help to explain who, what, when, how, and why. I will discuss the key aspects of a business case in this article.

Data Science: A data analytics process revolves around the core capabilities of business operations. This encompasses a range of approaches and models that are used to collect and manage large volumes of unstructured and complex data from a wide variety of sources. The ability to extract valuable information from data analytics processes is at the heart of data science. The primary focus of a data science project is to build a model that can effectively and accurately predict, as well as provide insight into, the performance of business units.

Predictive Analytics: This approach focuses on statistical analysis of historical or actual data mined from large databases. One of the hallmarks of predictive analytics is the use of mathematical techniques to filter, manipulate, and rank large databases to create new insights. One of the largest challenges that face data mining techniques is the enormous amount of information required to perform successful and accurate predictions. Because the accuracy of such predictions rely heavily on the quality of the underlying database and the quality of the calculations performed by the algorithm, it is extremely important that these databases are maintained in high integrity and security. Many companies have found success through the use of statistical analysis techniques that exploit the mathematical language of the Internet and other public networks.

Historical Datasets: The historical data sets produced by business analytics are exceptionally rich in terms of both quality and quantity. These sets are created from historical events and trends. Because they represent much more time than any traditional analytical tool, they provide an extremely rich source of predictive insight. The ability to leverage the information from these large databases allows business owners to make strategically important decisions regarding the future of their company.

Data Mining: Determining the usefulness of statistical methods is what is business analytics. While traditional business analytics deals primarily with identifying and tracking customer behavior, this approach seeks to identify and measure what is important to a business. Through the use of statistical methods, a business owner can gain a better understanding of what drives their customer behavior and what is likely to influence their purchasing decisions. However, data mining also encompasses the use of mathematical algorithms to identify patterns from large amounts of historical data. These patterns are used to generate richer and more personalized recommendations for what is business analytics.

Data descriptive analytics: Unlike most statistical approaches to business analytics, data descriptive analytics is less driven by specific predictions about the future outcomes of specific pieces of historical data. Data descriptive analytics seeks instead to provide a general sense of direction and trend. This approach is not based on strict predictions about the range or timing of future results. Instead, it relies on general statements about past performance that can be used to forecast future outcomes. For example, a company could use data from a period of time such as five years to predict what is business analytics in the next five years.

Machine Learning: Machine learning deals with the training of computers to gather large sets of relevant data from the internet. In this process, computer programs would be trained to recognize patterns, classify them and then function to extract useful information from large sets of unlabeled data. For example, a data mining program might be taught to recognize spam in a particular email account and classifies it into a database for further analysis. Another example of how machine learning can be applied is by using a web application to retrieve data from websites and index it according to a certain domain or field. This can help improve efficiency and cut costs in organizations by reducing the time and effort necessary for repetitive, boring tasks like data analysis.

These four approaches are among the subspecialties of business intelligence analyst. Each of these has its own benefits and drawbacks. When choosing which one to specialize in, however, business intelligence analysts should consider how each sub-specialty fits within their strategic objectives. The analyst should also consider whether or not they have the background and skills necessary to apply each of these strategies in real world business situations. Ultimately, a business intelligence analyst must demonstrate the ability to make good, unbiased decisions regarding the use of big data analytics and the identification of its underlying structure, methodology and benefits in terms of strategy. Being an effective business intelligence analyst requires being able to balance technical and operational skills while synthesizing outside sources’ information into clear business intelligence.

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