What are some examples of data analysis

Modern data analysis Gain valuable insights with ease

Digitalization is advancing rapidly around the world. The digital processes in companies generate huge amounts of data that are growing every day. Industry 4.0 and the Internet of Things (IoT) are also accelerating this development. In order to run a company successfully and to make the right decisions, detailed, deep insights into the many different processes are necessary. Companies are faced with the challenge of aggregating the large amount of data, managing it and evaluating it. With the help of modern business intelligence, patterns and trends can be found in the data and important information extracted. The knowledge gained serves as the basis for decision-making and management processes in the company. Executives and other stakeholders can make informed, data-based decisions. The data analysis offers appropriate solutions for information acquisition. In addition to supporting the decision-making process, data evaluations serve as the basis for numerous other applications such as certifications, compliance with compliance guidelines, compliance with legal requirements, controls, for regulatory purposes and much more. The most important goal of data analysis is to obtain the necessary knowledge or information from the available data by using various methods and analysis procedures. In the following article we will explain to you what is meant by data analysis and data evaluation. We introduce you to relevant methods and procedures for statistical analysis, illuminate the analysis process in practice and finally bring you closer to the advantages of modern data analysis software such as datapine. With this software you can evaluate data yourself and visualize analysis results.

1) What is data analysis or data evaluation?

The terms Data analysis or Data evaluation describe the process of obtaining the information required for the business from raw data. Various methods and statistical analysis procedures are used. The knowledge gained is clearly presented in the form of metrics and data visualizations.

Even if subtle differences can be found between the terms data analysis and data evaluation and some scientific sources describe the terms in separate definitions, data analysis and data evaluation are usually used synonymously in corporate practice. We, too, equate data analysis and data evaluation in the following definition. One more note before actually defining data analysis: Data analysis is often equated with data mining. This is actually not correct, as data mining is only a sub-area and method of data evaluation. Data mining uses statistical analysis methods, which in many cases can be assigned to exploratory data analysis.

But now to the definition of data analysis or data evaluation. The aim of data evaluation is to use various methods and statistical analysis methods to obtain information and findings from the available raw data, and to describe and present them. The statistical evaluation arranges and structures the data. For example, patterns or trends can be found in the data. The methodology and the procedures used are determined by the question to be answered and the type and quality of the data. Data evaluations are used in many areas of daily life. Typical applications are opinion polls, analyzes in science, clinical studies, reporting in companies or business intelligence. The knowledge gained through the statistical evaluation is presented in the form of numbers, facts, metrics or data visualizations such as diagrams. They can be made available to the respective target group via reports or dashboards. In this form, they support the company's management and differentiation processes or are used to meet a wide variety of requirements. In the dashboard shown below, we show you a practical example for presenting the results of an analysis of customer service:

We will present the relevant methods and statistical procedures for data evaluation in more detail in the next chapter.

2) Methods and statistical analysis procedures

To introduce the relevant methods and procedures for data evaluation, we would first like to briefly explain the difference between Business Intelligence and Business Analytics. This distinction helps you to better classify the various methods and procedures of data evaluation. Both business intelligence and business analytics are data management solutions that collect current and historical data occurring in a company, analyze it and provide insights into the various processes to support decision-making processes. However, they differ in their purpose and the way they do it and what information they provide.

Business Intelligence deals with the question: "What has happened in the past until today and why did it happen?". Business intelligence finds patterns and trends, but does not aim to predict future developments.

Business analytics focuses on the question: "Why did something happen and what does that mean for the future?". The responsible factors and causal relationships for the events and processes are determined. This uses business analytics to make predictions about future developments.

According to the distinction between business intelligence and business analytics, the relevant analysis methods can be divided into various superordinate categories. We subdivide into these five categories:

the descriptive analysis

the exploratory analysis

the diagnostic analysis

the predictive analysis

the prescriptive analysis

Starting with the category of descriptive analysis up to the category of prescriptive analysis, the complexity and effort of data evaluation increase, but also the added value for the company. In the following we will introduce you to the five categories in more detail.

a) Descriptive data analysis

Descriptive data analysis, also known as descriptive data analysis, focuses on data from the past. It arranges and structures empirical data. The data evaluation should answer the question: "What happened?". For example, it provides information such as sales in the last quarter or the type and number of service requests. To deliver these results, descriptive analysis can extract data from various sources and aggregate, order and structure the information. However, descriptive analysis does not provide answers to questions such as: "Why did something happen?". Descriptive data analyzes are often combined with other analysis methods.

b) The exploratory data analysis

The aim of exploratory data analysis is to find connections in data and to generate hypotheses. Prior to the exploratory analysis, there is only limited knowledge about the relationships between the data and variables. A typical area of ​​application for exploratory data analysis is data mining. By uncovering connections with the help of exploratory data analysis, conclusions can be drawn about the causes of the processes.

c) The diagnostic data analysis

Diagnostic data analysis deals specifically with the question: "Why did something happen?". By comparing historical and other data, identifying patterns and uncovering connections, she finds causes or mutual interactions. With the help of diagnostic data analysis, companies can solve specific problems, as the causes are shown.

d) The predictive data analysis

The predictive data analysis, also known as predictive analysis or predictive analytics, allows a look into the future. The question is answered: "What will happen?". In order to make the correct predictions, predictive data analysis uses the results of the previously described descriptive, exploratory or diagnostic analysis methods as well as algorithms and methods of artificial intelligence (AI) and machine learning (ML). By finding connections, causes and temporal tendencies, future trends can be predicted. The probability and accuracy of the prediction depend largely on the quality of the data, the patterns, relationships and trends found, as well as the intelligence of the algorithms. For example, future sales can be forecast or customer behavior can be forecast.

e) The prescriptive data analysis

Prescriptive data analysis is the most complex and costly analysis category. But it provides companies with immense added value by answering the question: "Which measures can be used to eliminate problems, positively influence future developments or achieve the goals set?". Prescriptive data evaluations are based on historical and current data from internal and external data sources. They use the results of the previously described analysis categories. The forecasts are continuously updated. ML and AI algorithms, neural networks, simulations and business rules are used.

After you have got to know the higher-level categories of analysis methods, in the next section we will go into the statistical analysis processes and analysis methods for companies that are relevant for data evaluation. We limit ourselves to the description of the following six methods of statistical analysis:

1. The regression analysis

Regression analysis is concerned with how the value of a dependent variable changes when individual independent variables are changed while other independent variables remain the same. This shows which independent variables influence the examined dependent variable and which relationships exist. As a rule, regression analysis uses the statistical evaluation of historical data. It allows one to learn from the past in order to make better decisions for the future. Typical areas of application are business forecasts, for example, to predict future sales based on the regression analysis of the recorded sales, depending on certain market conditions with the aid of predictive analysis methods.

2. The cohort analysis

Cohort analysis examines and compares the behavior of different people over time and forms groups with similar characteristics or behaviors. With this analysis process, companies can, for example, create customer groups based on demographic or other characteristics such as the purchase of certain products. These customer groups can then be compared in terms of purchasing behavior. This enables conclusions to be drawn about how customer quality changes over time. Marketing can, for example, relate the results to the campaigns carried out and check their effectiveness.

3. The cluster analysis

The cluster analysis allows data elements to be grouped in such a way that the elements of one group behave more similarly with regard to certain criteria or variables than elements of another group. This process is also called clustering. Cluster analysis can be used to provide additional context for data or trends. Typical areas of application are the formation of target groups. Target groups can be certain customer groups, for example, which the cluster analysis summarizes into clusters depending on the available data.

4. The factor analysis

The factor analysis reduces a large number of influencing variables to a smaller number of factors. The resulting factors show less interaction with each other and contain the information from several related variables. The factor analysis provides a mathematical model that can be used to generate hypotheses. An example of the use of factor analysis is the customer evaluation of a product based on many different criteria. The evaluation criteria can then be summarized into a few factors that are decisive for the evaluation. This makes it easier to optimize the product, as not all criteria have to be considered individually, only the factors.

5. Neural networks

A typical area of ​​application of artificial neural networks is predictive data analysis. The neural network forms the basis for the intelligent algorithms of machine learning. The aim is to predict the development of a specific variable. The neural network is modeled on how the human brain works. Information flows into mathematical neurons and is processed by them. The neurons forward the results to the next neuron level until the result is output by an output neuron. Prediction engines can be implemented that deliver exact forecasts. Modern data analysis software such as the Predictive Analytics Tool from datapine enables users to generate predictions quickly and easily. You select the data to be processed by the predictive analytics tool and the software automatically calculates forecasts based on historical and current data. You can see an example of such a visualized prediction engine from datapine here:

6. Data mining

Data mining applies the exploratory statistical evaluation with its various procedures and methods to large amounts of data. The aim is to identify dependencies, relationships, data patterns and trends and thereby generate knowledge. A typical application example of data mining is intelligent data alarms. They ensure the independent recognition of relevant patterns and trends in databases or selected key performance indicators (KPIs). In the event of anomalies or deviations from targets, which are automatically identified with the help of artificial intelligence and machine learning, managers automatically receive a notification or an alarm on their dashboard.