Analytics – A Dynamic Part of the Enterprise Data Management Toolset

Analytics is the scientific, systematic analysis of quantitative or qualitative data. It’s used for the discovery, identification, interpretation, and creation of useful information. It also involves applying statistical principles towards effective judgment making. Analytics is the science of gathering, organizing, analyzing, and sharing quantitative information about the world and the people in it.


The science of analytics is a complex collection of techniques that combine traditional statistical methods with machine learning and artificial intelligence. These techniques apply theoretical knowledge to real world situations to improve the quality of predictions and to implement practical solutions to problems. Traditional statistical methods like binomial tree-ring analysis, density sampling, and panel data analysis can be effectively used to identify and predict relationships between observed and expected properties, trends, or future outcomes. Machine learning techniques, such as reinforcement learning and neural networks, can be effectively applied to generate and predict the most relevant, actionable data sets that can be used for taking actions to forecast the outcomes.

Large amounts of unstructured data can easily and quickly be gathered and processed through existing machine learning and analytic methods. A wide variety of software programs and hardware devices are available to support this large amount of analytics. There are a wide range of analytic techniques and models for large amounts of unstructured data. Some of these techniques include Bayesian, logistic, decision trees, and KISS (Kolmogor method). These techniques, when combined with other machine learning techniques, allow users to generate and predict large amounts of results, which can then be used to make decisions. Examples include optimization for production, operation, sales, distribution, cost, and consumption; and decision making and product optimization for financial services, supply chain, manufacturing, and healthcare industries.

Data science and analytics are two different disciplines that are integral to the modern business environment. The data science discipline refers to the mathematical programming of systems to analyze and interpret large amounts of unstructured data sets. Examples of such systems include databases, web services, and automated software. On the other hand, the analytical side of analytics refers to the identification, construction, implementation, and evaluation of statistical models. Both sides of the analytics ecosystem provide critical and necessary skills to solve problems and create new solutions for organizations.

Machine learning and analytics go hand-in-hand because they complement each other by allowing users to quickly and efficiently identify the relevant patterns from large amounts of unprocessed data. Data science helps users to quickly analyze large amounts of unprocessed data and apply advanced mathematical algorithms to transform the data into highly relevant information. The two sides of the analytics ecosystem provide users with the ability to quickly identify relevant patterns from large amounts of unprocessed data. This allows the data to be categorized and solved. Machine learning uses complex computer models and methods to rapidly find solutions to complex problems. Both sides of the analytics project provide critical and necessary skills in the data management and application development areas.

Organizations must determine what technologies they will use for analytics. Some of the more popular applications include complex event-triggered services, advanced analytics, artificial intelligence, and rich-text processing. These technologies can provide the organization with data management, problem solving, and sophisticated insight into business activities. When these technologies are combined with accurate, flexible analytics APIs, organizations have the tools they need to implement advanced analytics internally, while also leveraging the analytics ecosystem to provide external data management solutions to their customers.

Analytic technologies used by organizations in the healthcare industry can include historical data mining, health plan data mining, event-triggered services, remote sensing, and advanced analytics. historical data mining allows users to retrieve and analyze large amounts of historical data using advanced algorithms. This provides users with precise insights into how consumers interact with their healthcare brand and the tools they need to improve their experience. Health plan data mining provides detailed, granular information about how consumers manage their health plans.

Data mining is only one component of the enterprise-level application of advanced analytics. Other technologies that should be considered when building an advanced analytics descriptive programming language are predictive computing and deep learning. Predictive computing uses large data sets to function as a powerful computing infrastructure, while deep learning trains a computer to detect patterns from massive amounts of unprocessed data. Both technologies have the potential to dramatically increase productivity in organizations by helping the business achieve its objectives. Another application, called analytical computing, leverages analytics to provide a framework for advanced scientific research. The scientific community is leveraging analytic techniques in everything from weather prediction to natural resource management.