Data Analytics Processes

Analytics is the systematic mathematical analysis of statistics or data. It is widely used for the discovery, identification, and sharing of meaningful trends in complex data sets. It also involves applying mathematical models to successful decision making. Analytics draws upon many methods including mathematical programming language like MLQ, applied research applications in statistics and information systems, natural language processing (MLP), domain knowledge and statistical computing. Analytics also utilizes a variety of different software solutions and data visualization tools.

Analytics can be categorized into two main categories, namely prescriptive analytics and descriptive analytics. Prescriptive analytics deals with setting up rules for generating, interpreting and utilizing results from data analysis. The main objective of this type of analytics is to provide easy and accurate financial measures and forecast future trends. A good example of a prescriptive analytics tool is a ticker tape directory, which provides financial information about stock picks that investors have purchased using specific algorithms.

On the other hand, descriptive analytics deals with attempting to predict patterns and relationships among variables. One example of a descriptive method is lagging indicators, which attempt to predict market direction by past price action. Others include moving averages and volume indicators. Data analysts in many industries rely on these methods to make better-informed decisions on where to invest.

There are many different ways how an analytics company can make more money through smarter decisions with their analytics. Data presentation is a major factor in how people react to new information. Persuasive behavior in humans is influenced by both the quantity and quality of information they receive. By presenting findings from analytics in a clear and concise manner, data presentation allows people to form new judgments and form better opinions.

Proper analytics is very important for any business to ensure that it meets its business goals and objectives. However, business managers often overlook some aspects of analytics because they don’t see it as a core component of business management. If this is the case, then a lack of proper analytics will prevent a company from achieving its goals. By paying attention to these three areas, a business manager will ensure that all components of analytics are incorporated into a comprehensive approach. Below are a few of the key areas that all companies should consider when it comes to proper analytics:

The first aspect of a data analytics process is to determine what types of analytical queries or reports will be useful and actionable. In terms of descriptive analytics, all reports must provide relevant context to current or historical data. Actionable insights would entail analyzing the results of a query based on the business’s goals and objectives. In the context of data analytics process, a goal could be “to reduce customer service calls,” “to increase profitability,” or “to increase company satisfaction.”

Another significant area of focus in a data analytics effort is technical translation. This refers to techniques that help a company to translate observed data into language that can be understood by an engineer or analyst without the assistance of a data scientist. Examples of such techniques include statistical analysis, machine learning, natural language processing, or domain knowledge extraction. Some of the popular translation techniques used by some of the biggest names in the industry include language technology, visual analytics, web experience design, user experience modeling, and web site mapping. While most companies utilize some or all of these techniques at one point or another, they can be applied in different ways to achieve more insightful insights from the collected data.

Lastly, business managers should pay close attention to the predictive capabilities of Analytics. Like most technology, Analytics has both “soft” and “hard” capabilities. Some examples of “hard” analytics techniques include machine learning, natural language processing, and web experience design. “Soft” analytics techniques include event detection, behavioral forecasting, or predicting consumer preferences.

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