WHAT IS BUSINESS ANALYTICS?
Business Analytics (BA) refers to the skills, technologies, and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning.
Business analytics begins with a data set (a simple collection of data or a data file) or commonly with a database (a collection of data files that contain information on people, locations, and so on).
Analytics can be defined as a process that involves the use of statistical techniques (measures of central tendency, graphs, and so on), information system software (data mining, sorting routines), and operations research methodologies (linear programming) to explore, visualize, discover and communicate patterns or trends in data.
WHAT ARE THE TYPES OF ANALYTICS?
The three categories of analytics that the Institute of Operations Research and Management Sciences (INFORMS) organization suggests are descriptive, predictive, and prescriptive analytics.
The application of simple statistical techniques that describe what is contained in a data set or database is descriptive analytics.
The application of advanced statistical, information software, or operations research methods to identify predictive variables and build predictive models to identify trends and relationships not readily observed in the descriptive analysis is predictive analytics.
The application of decision science, management science, and operations research methodologies (applied mathematical techniques) to make the best use of allocable resources is prescriptive analytics.
WHAT ARE DESCRIPTIVE ANALYTICS?
Descriptive analytics is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis. Descriptive statistics are used in descriptive analytics.
Descriptive statistics, including measures of central tendency (mean, median, mode), measures of dispersion (standard deviation), charts, graphs, sorting methods, frequency distributions, probability distributions, and sampling methods are used in this type of analytics.
HOW IS IT USEFUL IN BUSINESSES?
The first stage of business analytics is descriptive analytics, which still accounts for the majority of all business analytics today. Sales, marketing, operations, and finance uses this type of analytics.
Behavioral analytics, Cohort Analysis, Collections analytics, Cyber Analytics, Enterprise Optimization, Financial services analytics, Fraud analytics, Marketing analytics, Pricing Analytics, Retail sales analytics, Risk & Credit Analytics, Supply Chain Analytics, Talent analytics, Telecommunications, and Transportation analytics are the various types of descriptive analytics.
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WHAT ARE THE STATISTICS INVOLVED?
Central tendency (mean, median, and mode), dispersion (including the range and quartiles of the data-set, and measures of spread such as the variance and standard deviation). The shape of the distribution may be expressed with statistical tools such as skewness and kurtosis.
Characteristics of a variable’s distribution may also be depicted in graphical or tabular format, including histograms and stem-and-leaf display. Cross-tabulations and contingency tables. Correlation, Regression, and Covariance. Conditional distributions.
WHAT ARE PREDICTIVE ANALYTICS?
Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.
HOW IS IT USED IN BUSINESSES?
In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.
The applications of predictive analytics in businesses include Analytical customer relationship management (CRM), Collection analytics, Cross-selling, Customer retention, Direct marketing, Fraud detection, Project risk management, Underwriting, Forecasting service dates, Reducing waste, Retaining staff, Improving payment moral, etc.
Predictive analytics is used in almost all fields such as Banking and in retail and manufacturing businesses.
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WHAT ARE THE STATISTICS INVOLVED?
The approaches and techniques used to conduct predictive analytics can broadly be grouped into regression techniques and machine learning techniques.
The statistics used are Linear regression, Logistic regression, Multinomial logistic regression, Probit regression, Cluster analysis, Analysis of variation (ANOVA), Chi-squared test, Survival or duration analysis, Classification and regression trees (CART), Correlation, Factor analysis, Association rules, Decision trees, Time series, Experimental design, Bayesian theory – Naïve Bayes classifier, Sampling, Matrix operations, K-nearest neighbor algorithm (k-NN), Discrete choice models, Multivariate adaptive regression splines, Ensemble models, Gradient boosting, Incremental response, Partial least squares, Principal component analysis, Cluster analysis, etc.
Machine learning techniques include Neural networks, Multilayer perceptron (MLP), Radial basis functions, Support vector machines, Naïve Bayes, k-nearest neighbors, Geospatial predictive modeling, etc.
WHAT ARE PRESCRIPTIVE ANALYTICS?
Prescriptive analytics is the third and final phase of business analytics. Prescriptive analytics uses mathematical and computational sciences and suggests decision options to take advantage of the results of descriptive and predictive analytics.
The relatively new field of prescriptive analytics allows users to “prescribe” a number of different possible actions and guide them towards a solution. In a nutshell, these analytics are all about providing advice. Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made.
At their best, prescriptive analytics predicts not only what will happen, but also why it will happen to provide recommendations regarding actions that will take advantage of the predictions. These analytics go beyond descriptive and predictive analytics by recommending one or more possible courses of action.
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HOW IS IT USED IN BUSINESS?
Sales, marketing, operations. Most management reporting – such as sales, marketing, operations, and finance – uses this type of post-mortem analysis. Prescriptive analytics can be used in two ways: Inform decision logic with analytics and Evolve decision logic.
In the oil industry, Prescriptive analytics software can help with both locating and producing hydrocarbons by taking in seismic data, well log data, production data, and other related data sets to prescribe specific recipes for how and where to drill, complete, and produce wells in order to optimize recovery, minimize cost, and reduce environmental footprint.
It is used in Unconventional Resource Development. In the realm of oilfield equipment maintenance, Prescriptive Analytics can optimize configuration, anticipate and prevent unplanned downtime, optimize field scheduling, and improve maintenance planning. Pricing.
WHAT ARE THE STATISTICS INVOLVED?
Prescriptive analytics requires high computing speed and complex mathematical algorithms. Statistical techniques used here include optimization techniques, simulation techniques, game theory, and decision-analysis methods.
CONCLUSION
Business analytics 3 types of analytics; descriptive, predictive, and prescriptive. Statistics has been the major and most powerful tool in business analytics. Statistics is a powerful tool for predicting future business behavior and prescribing the source of action.
Statistics is used in a number of ways in business fields. Business analytics is having exponential growth in recent years and near future with the availability of modern software and advanced statistical techniques.
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