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By now, we all know how valuable data can be for enterprises of all sizes and fields of activity. But, do we understand why Data Analytics is so important?
Data analytics means examining data to draw conclusions and obtain information using statistical methods and tools.
Every day, a large volume of data is generated and needs to be processed and analyzed to extract valuable information.
With the use of this data, it will be possible to understand markets, and customers, anticipate trends, define strategies, and make decisions based on solid evidence.
The main question is, how to transform the collected data into actionable knowledge capable of driving growth and ensuring competitive advantages? That is what Data Analytics aims to do.
What is Data Analytics?
Data Analytics, or Data Analysis, encompasses the process of examining and categorizing a set of data.
Derived algorithmically or mechanically, the analysis will allow for the extraction of information, the generation of ideas, the identification of patterns, and the making of strategic decisions based on the collected information.
Although it is a term that has gained popularity recently, the core is already a well-known and studied field: statistics.
However, it is the growing importance, volume, and intensity of processing that makes data analysis so essential for companies.
Currently, data is collected by various means and media. The main issue in data analytics lies in the use of intelligent tools for data search, storage, and processing.
Data alone is just a collection of values or a sequence of symbols. It is their interpretation that transforms them into useful and important information for the business.
What differentiates Data Analytics from Data Science and Big Data?
The three terms refer to working with data. However, they have different focuses and methodologies and are complementary within organizations.
In a very practical way, the definition of each can be understood as:
- Big Data: uses methods and technologies to handle large volumes of varied data generated at high speed;
- Data Science: extracts knowledge and ideas from structured and unstructured data. For this, it uses scientific methods, systems, processes, and algorithms;
- Data Analytics: the process of examining data to extract useful insights and support decision-making.
As you can see, although similar, each treats data differently. While Big Data focuses on storing and processing large volumes of data, the other two analyze and interpret this data.
Between Data Science and Data Analytics, the difference is that the former is broader and involves the use of advanced modeling and machine learning techniques. The latter, as we have discussed, deals with traditional statistical and exploratory analysis techniques.
It can be said that Big Data deals with the necessary infrastructure to work with data on a large scale. That is, even in large volumes, without being interpreted, they are not usable.
The role of Data Science and Data Analytics is necessary to extract value and obtain knowledge and information for the decision-making process.
Types of Data Analysis
Analyses vary according to the origin, size, and other factors related to the data. However, what will define the approach are the business needs or objectives. That is, what do I want to know with this data?
Even though there are a vast number of possibilities, commonly, 4 types of data analytics are most used:
- Predictive Analysis: the act of anticipating possible effects of a given decision. Using statistical models, the diagnosis establishes cause-and-effect relationships of what may happen and the probability. This analysis can answer questions like the expenses of a particular input for the coming months if production is increased. Or, the impact on the final product if there is an increase in a specific resource for production;
- Prescriptive Analysis: more complete and complex. It aims to provide an answer on what should be done in each situation. That is, what alternatives can maximize or minimize one of the business objectives? It uses mathematical and statistical resources. It solves questions involving the most economical alternative, such as minimizing the expenses of a particular input, among others;
- Descriptive Analysis: refers to understanding what is happening at the present moment, to understand the company’s current performance. It explains, through data, what is happening. It presents data in graphical resources and reports, such as total sales, customer satisfaction, productivity, costs, sales, and revenues;
- Diagnostic Analysis: detects, in the current context, the explanation and reasons for something happening. It is used by companies that have already gone through a complex period and want to learn from the experience so that it does not repeat in the future. Unlike descriptive analysis, here, the goal is not to understand what is happening, but why a particular event occurred.
How Data Analytics works and how to apply it in practice?
Applying this type of data analysis consists of collecting relevant data and using software capable of identifying patterns. Finally, the obtained information is used to make decisions that can optimize processes and strategies.
Indeed, it is not the amount of data that gave rise to data analytics. But, the evolution of resources capable of processing, organizing, and structuring them.
Currently, specialized programs can perform data analytics, providing information in reports, dashboards, and graphs, making the knowledge useful and accessible.
And, this is the case with the Fusion Platform’s Central Analytics.
Fusion Platform’s Central Analytics
Neomind’s Fusion is a platform for managing processes, documents, and indicators. Therefore, the data is centralized in one place, facilitating its collection, processing, and analysis.
In Central Analytics, data is categorized, analyzed, and presented in a way that makes interpretation easy and quick with the use of panels and graphs that have updated data and can be monitored in real time.
Being complete and customizable, each business can define which information it wants to obtain. Thus, a panel can contain data about customer behavior or process productivity.
The knowledge obtained in Central Analytics helps identify business opportunities and improvements.
Data can be extracted from various sources, whether structured or not, such as spreadsheets or databases. By enabling integration, Fusion Platform can collect data from other software that was previously disconnected.
After processing and modeling the data, the solution communicates the obtained information with reports, and interactive dashboards, among others.
As it does not only deal with data but is a complete process management tool, documents, and indicators, with Neomind’s solution, it is possible to develop action plans to implement alternatives that promote continuous and complete improvement throughout the organization.
In addition to generating valuable insights, all actions can be continuously reviewed and adjusted. Thus, your business is free to use the obtained information according to the project’s or organization’s internal goals and strategies.
Try Fusion Platform and use data analytics to enhance your business’s results and overall success!
