Big Data, Business Analytics, and Data Visualization

Business Analytics refers to the various techniques, technology, and skills for continuous observational exploration and analysis of past business performance in order to gain insight into business issues and drive business strategy. The term was first used by Robert Kaplan in his famous book “The Causes Of Commercial Stress” in 1988. For the past two decades, Business Analytics has been one of the fastest growing fields in strategic management. In fact, it is quickly becoming one of the most prominent and beneficial trends in strategic management today.

The core elements of business analytics are predictive analytics, which seeks to generate or discover patterns from large-scale data mining, and decision-making analytics, which attempts to make sense of the complex data that makes up this predictive information. While both are important, they are distinctly different from each other. To illustrate, statistical analysis relies on analysis of the raw data, which is then used to support strategic decisions. On the other hand, predictive analytics attempts to uncover and reveal patterns from large-scale and often multiple-period historical data.

The first step to take when beginning a business analytics process is to choose a domain or topic and research the topic intensely, prior to choosing a formal Data Management team and hiring a Data Analyst or Analytics Practitioner (DAP). This formal process is essential in developing Data Science skills and ensures that the topic is well understood and can be adequately researched and analyzed. Typically Data Science requires at least one year of graduate school and extensive fieldwork experience before becoming a professional. In addition to hiring the appropriate Data Analyst or Analytics Practitioner, data analysts must also learn how to develop, analyze, and communicate business intelligence (BI) models.

Many businesses, especially small and medium-sized businesses (SMBs), have traditionally had a difficult time making strategic decisions due to the lack of formalized process for decision-making. A business analytics process is designed to address the unique issues involved in SMBs. One of the most common issues involves what is business intelligence (BI) and how it differs from traditional statistical methods. Data analysts must therefore master the statistical language while explaining the decision-making process in a language that the customer understands.

The second step to begin a business analytics strategy is to choose a domain or topic for the research and analysis. This decision is often based on the size and scope of the businesses and can range from the simplest of queries to the largest databases containing tens of terabytes of information. The selection of the topic will usually dictate the research methodology, which will involve an abundance of data analysis tools and libraries.

The third step to create a business analytics strategy is to choose a set of data mining or machine learning technologies to leverage the existing data sets. Machine learning allows for the automatic generation of decision trees and optimization algorithms, while decision trees are designed to efficiently extract the relevant data for a specific query. These three techniques work together to create a framework or foundation upon which business analytics can be built. Data mining uses large databases to find trends, patterns, and anomalies in large amounts of unstructured data. It then applies these patterns to form a more complete representation of the business environment.

The fourth and final step in the process is to create a descriptive analytics strategy that explains how business analytics can be applied to solve business problems. The key concept here is to first analyze the data and extract its meaning. Next, the resulting data can be visualized using data visualization techniques such as data visualization graphs, heat maps, histograms, and text relationships. Finally, descriptive analytics strategies can be written to describe the results of the analysis in a way that a business analyst can understand. This final step is often called the predictive analytics strategy, because it describes the accuracy of a predictive method by expressing a mathematical expectation about the result set from a descriptive model.

To get the most out of the analytical process, however, you must combine all three methods. By collecting descriptive analytics data from many different angles – traditional market surveys, customer insight, internal systems, and statistical analysis – you can maximize your ability to provide your customers with real value and reduce your reliance on expensive ad campaigns. You’ll also make much more use of what’s already available to you in the form of historical data and machine learning technology. Ultimately, the best approach for businesses to take when it comes to big data and business analytics is a combination of traditional statistical analysis, data visualization, and predictive analytics strategies.