Analytics combines both process and application of information science. Analytics is the systematic mathematical analysis of statistics or data. It is frequently used for the identification, interpretation, and reporting of meaningful patterns in external data. It also involves applying statistical data patterns to successful decision-making. In a broader sense, it can be seen as an empirical science whose object is to discover patterns from observed patterns.
There are four main types of analytics: traditional survey methods, behavioral survey techniques, quantitative methods, and web based services. Traditional survey techniques involve asking questionnaires or conducting interviews. In the case of surveys, companies can predict or infer segments of the target market by asking questions about product attributes or service preferences. These results are then used to construct descriptive statistics, which can forecast future trends. Companies can also use survey results to infer demographic characteristics of the target market, to test marketing strategies, and to forecast customer behavior. Surveys, however, are slow and time-consuming, and they are unable to provide precise predictions.
Behavioral analytics process insight from the existing sample of customers and competitors. It can detect and measure individual customer behaviors that can be correlated with product attributes and service preferences. Aggregators use sophisticated mathematical algorithms to combine consumer inputs and generate rankings of individual customers and groups of customers. The final step is to forecast future trends using the aggregated results of the behavioral analytics project.
Quantitative analytics explores the relationship between quantities of different variables. It can provide valuable insights for any specific situation by providing comparable data for all the variables that have been studied. This type of analytics has already provided valuable insights in many fields. Examples include understanding relationships between features and functions of products or services and customer preferences.
Another type of business intelligence analytics is data-driven decision making. Data-driven decisions refer to those that make strategic decisions based on available data. Analysts apply statistical techniques to accumulate and interpret relevant information about customer needs, business trends, and competitive conditions. The resulting model is then used to create decisions. These types of analyses can improve organizational performance through better management of resources, better product and service quality, more efficient operations, and ultimately increasing company profitability.
Both data-driven and analytical analytics can provide business intelligence solutions. But data-driven analytics is often considered the more practical and challenging of the two disciplines. Analytical techniques must first be developed and then tested against available data in order to provide information. In contrast, data-driven analytics techniques can be developed quickly and can provide immediate feedback.
Analytics techniques can also be visualized in a number of ways. Visualization is a popular practice among analysts. It enables analysts to visually communicate key metrics or key business intelligence information to a variety of audiences, including managers, team members, customers, or other analysts. A good visualization strategy will take into account the different types of visual information available and the appropriate representation for each. For example, if a data point is needed to represent the life expectancy of a group of people, then it would be better represented using bar charts, not pie charts. Likewise, if a data point is needed to show the sales volume of a particular product over a certain period of time, then it would be more appropriate to use bar charts instead of a multiple-period view of cumulative sales.
Another way to visualize analytics solutions is through data visualization tools. Many companies choose to visualize their data in such a way that they can easily chart and analyze it to reveal the underlying trends or patterns. Visualization allows you to focus on a select number of factors, allowing you to focus on the most important aspects of your business at a particular point in time. This allows you to focus your attention on those trends that are of utmost importance to your company’s bottom line. However, data visualization tools should not replace your analytics. They should be used to supplement your analytics efforts.