The advent of mass data storage systems and increased computing power has allowed companies to embrace data-driven decision making through business analytics, which provides an insight into a business’s operations and performance. Business analytics uses data mining to discover new relationships in existing management processes; it is used to find answers as to why existing processes are successful or failing, and allows companies to plan future improvements by using systematic, quantitative and data-driven processes that link the company data to models, analysis, predictions and optimal decisions. Predictive models can become a tool that drives automated decision selections.
What should you look for in a Business Analytics Program
To this end a business analytics software package should include:
- the capability to import data from files and existing databases, either local or distributed (and preferably reachable through the Internet)
- exploratory data analysis tools to rapidly navigate the data, visualize them and derive the relevant summaries; (typical suites include at least bar charts, bubble charts, pie charts, histograms, radar charts, line plots, and simple filtering options like parallel filters)
- model-building tools, including at least the standard polynomial fits with least-squares approximations, supervised training tools such as neural networks
- unsupervised training tools such as clustering (top-down and bottom-up), and self-organizing maps
- connectors to applications and models which are external to the business analytics software so that existing models can be integrated in a seamless manner into the business analytics system
- a suite of solvers (optimizers) matched to the business characteristics, for example, solvers acting on real-valued parameters, discrete parameters, mixed cases
- tools to manipulate data tables, such as the merging of more than one table, filtering data, creating data tables by accessing data on the web, etc.
- design of experiment (DOE) methods for creating input data to be used for testing your business (or your system)
- network analytics tools to analyze relationships between entities, like relationships between people in social networks
The role of Numerical Simulation and Optimization in Business Analytics
From a practical point of view, business analytics consists of statistical and quantitative data analysis, construction of interpolation models, studies of predictive models and classifiers. A vast variety of classical methods can be used to analyze data. When dealing with large databases, more sophisticated tools such as interactive navigation plots or self organizing maps (SOMs) can be really helpful. Moreover, all the activities connected with automatic clustering, classification, prediction and decision making algorithms can be considered as part of business analytics activities.
The basic scientific method of obtaining data corresponding to new situations and to cases not encountered in the current normal business operations is by defining and running experiments. In many cases real experiments are very costly and slow, and an alternative can be that of simulating the business processes so that “what-if” experiments can be run by software programs.
Once a computer-based simulator of the business is available, a very large number of experiments can be executed and, therefore, more alternatives tested before making decisions. Furthermore, the decision process can be helped by automated (or semi-automated) optimization strategies. An optimizer will try, in a strategic manner, many different inputs (many choices of parameters regulating a business) in order to identify the best configurations.
Why Business Analytics?
For its successful operation, every business needs to:
- understand its current business processes and their performance
- improve profitability
To satisfy both these needs and gain a competitive edge, business analytics can be used to:
- build models of the business, predictions about the effects of a decision and models that monitor the effects of decisions taken
- test “what if” scenarios based on models and predictions before any actual decision making takes place
- assess management decisions by comparing them to updated corporate business data so that corrective actions can be swiftly implemented to buffer any negative effects resulting from less than optimal decisions made
- identify problems and product defects, thus assuring a consistent product quality
Business analytics, once the job of multiple tiers of personnel with varying degrees of expertise is now in the direct reach of managers through self-service analytics tools. The steps involved in applying business analytics to your business will depend on whether your business is already making heavy use of databases to store your corporate data and whether that data is accessible to the user of the analytics tool. If your business processes and customer relationships are not yet stored in databases, the first step would be to embark on the digitization of such information. The introduction of business analytics to corporate management is intended to positively affect the value chain by making hidden opportunities visible and increasing the speed at which a business can respond to customer demand or market changes.