Data Analysis


data analysis

Data Analysis

A commitment to an innovative future

In recent years the world has come to be assisted by data, and so today, technology and information are rapidly changing the way organizations operate, where each management and decision is implemented based on the analysis of this information. In fact, one of the innovative advances in managing such situations is data analysis, due to its importance in understanding the problems an organization faces.

In this sense, the data analytics group of Omnicon S.A aims to provide information about the content of data analysis, in order to contribute to the general knowledge of people and even show the virtues of technology and science from our field. The approach is to provide solutions to industrial problems that can empower national and international organizations.

What is data analytics?

Data analytics is a technique that has been perfected over the years as a result of various procedures, allowing access and decryption of information with the aim of obtaining important validations when applying them within an organization. The study of the data contributes punctually to successful decisions making, in favor of the company’s improvement in its different areas.

In addition, in order to achieve the success of the data analytics process in the organizations, it is necessary to understand the components that integrate it, each of them will contribute its function to reach the goal. In this sense, joining each of these components will lead to a successful project, where a clear picture about the stage in which the organization is will be depicted, identifying what was done previously and what steps should be made to achieve the objective set by it. Primarily, this study focuses on increasing efficiency and improving process performance by discovering certain patterns in the data. On the other hand, to learn more about this technique, it is important to bear in mind that in order to understand what is involved in the management of advanced data analytics today, it is necessary to go a long way full of experiences and science methods in the industry.

Data analytics applications in industrial environments.

Data analysis has become a useful ally for different industrial sectors because it is focused on supporting the problems that may arise in the company and therefore generate the relevant solutions to optimize decision-making. Knowing the benefits of this advanced and innovative technique has led us, as a company, to focus on some applications or use cases within the field of industrial processing, as shown below:



Why use data analytics in the industry?

In essence, using data analysis in industrial environments can ensure a fundamental improvement in decision-making and operational control because this method allows the analysis of particular causes of some events, in relation to the historical data or databases of the organization. It also allows to understand the objectives and guidelines of the processes, providing insights from the information gathered in order to achieve a successful context for the organization. The objectives that are achieved with the data analysis in the industry become in:

  • Optimization of the industrial equipment performance.
  • Production quality improvement.
  • Product reliability improvement.
  • Equipment and/or product security.
  • Predictive maintenance on critical equipment.
  • Problems analysis from the source in relation to the data.
  • Among others.

What levels of maturity in data analysis can be achieved within an organization?

It is understandable that organizations want to innovate with data analytics, either because they have selected it as a developing target or have been commonly associated to recent news where the terms “analysis”, “predictive” and “prescriptive” are part of their environment. But how are these terms related? And how do they differ when moving a company forward? Four levels of data analytics have been considered:

  • Descriptive Analytics.
  • Diagnostic Analytics.
  • Predictive Analytics.
  • Prescriptive Analytics.

These levels provide a model (see image nº2), which helps to illustrate the relationship between these types of analysis, classifying them in terms of the added value and the difficulty of carrying out these processes in a company. It should be clarified, that this ascending linear relationship is a simplification of reality, which provides the most optimal way to carry out a data analytics project, in the margin of the emerging tools on industry 4.0.