Data Analytics
Discover hidden patterns in your data

Enhance operational efficiency, pinpoint growth opportunities, and gain a competitive edge for your business

The Importance of Efficient Data Management in the Market

The key to standing out in any market sector lies in the ability to manage large volumes of diverse data at high speeds 

The significant challenge to overcome is to uncover intelligence within the data and apply the necessary treatments and processes to present information in web reports or mobile devices swiftly, securely, and reliably

With the expertise of ST IT Cloud and its specialized team, you can extract the maximum potential from your data. Intelligent analysis of your data will enhance strategic decision-making and achieve astonishing results for your business.

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Gráfico - Data Analytics

Sustainable Business Growth with Strategic Insights and Innovative Solutions

Revenue

Embracing a Data Analytics strategy enables harnessing vast volumes of data and transforming them into valuable insights for strategic decision-making.

In addition, it is also possible to generate new sources of revenue, such as identifying new markets, creating new products and improving operational efficiency. Invest in Data Analytics to boost your business and increase your revenue in a smart and strategic way.

Data Analytics

Decoding the future and revolutionizing business success

In an increasingly competitive business world, companies that utilize Data Analytics gain a distinct advantage in making smarter and more accurate decisions. From identifying patterns in large datasets to generating valuable insights, Data Analytics empowers businesses across all sectors to drive their success and stay ahead of the competition.

Unearth the intelligence within your data and unlock the true potential of your business.

Understand the types of Data Analytics analysis

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Descriptive Analysis

Provides an overview of what happened in the past, helping to understand how the business behaved in a given period of time.

Diagnostic Analysis

Seeks to understand the causes of certain events or situations in order to identify possible problems and solutions.

Predictive Analytics

It uses historical data and Machine Learning techniques to predict future events, providing valuable insights for strategic decision making.

Prescriptive Analytics

It goes beyond Descriptive and Predictive Analysis, it describes the past and predicts what may happen in the future, and suggests specific actions to achieve a certain goal in the future.

How to apply them

Product development

Descriptive Analysis seeks to describe and summarize data to understand trends and patterns. Identifying consumer trends and customer preferences, assessing product viability and adjusting the marketing strategy to achieve better results and launch success.

desenvolvimento de produtos
Multidisciplinar e Multisetorial

Multidisciplinary and Multisectoral

Predictive analysis can be employed across a spectrum of fields and sectors, including business, finance, healthcare, sports, technology, and beyond. This form of analysis facilitates the identification and prediction of behavioral patterns, enabling the detection of trends, fraud, performance assessment, and personalized user experiences. These are just a few instances illustrating how intelligent data analysis can add value and optimize processes for your company, regardless of your industry.

Predictive maintenance therefore makes it possible to implement an economical plan of action and maximize the lifespan and use of the equipment.

Customer retention

Diagnostic Analysis identifies the root causes of problems and failures within your processes, enabling corrective measures to be taken to enhance efficiency, quality, and customer satisfaction.

Retençao de Clientes
Eficiência Financeira

Financial Efficiency

Prescriptive Analysis uses data to improve operational efficiency and reduce costs, ensuring that the product is available when needed, preventing failures, providing agility and greater satisfaction for customers.

Success case

Bayer Pro Carbon:

ST IT Cloud and AWS are allies in the battle to reduce carbon emissions.

Challenge:

Helping Brazilian farmers to adopt smart agricultural practices and reduce carbon emissions in their consequences with the aim of achieving the commitment to reduce the emission of Greenhouse Gases (GHG) in the agricultural sector by 30% by 2030, the Pro Carbono project aims to central objective is carbon neutral agriculture.

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Frequently Asked Questions

The first step is to define what data will be collected and how it will be organized to be useful.
for decision making. It is important to be clear about the objectives of the data analysis and
identify which data are relevant to achieving these objectives. The collection of this data should
be automated where possible and be in line with the company's privacy policies and
the data protection laws in force.

The data collected must be relevant to your business, that is, it must be directly
related to the company's processes and activities. They must be organized into a template.
structured data, with well-defined fields and consistent values. This will facilitate analysis and
creation of reports and dashboards.

The choice of the ideal data analysis software or tool will depend on the needs and
company-specific goals. It is important to evaluate each option based on features, costs,
ease of use and integration with other tools used by the company.

To ensure data security throughout the process, it is important to follow some good
practices such as Encryption, Access Control, Authentication and Authorization,
access, Backup and recovery, Privacy policies, Compliance with data protection laws
data. 


It is important that data security is considered at all stages of the data process.
Analytics, from collection to analysis and sharing of results. ensure privacy
of data is critical to maintaining customer confidence and avoiding potential legal penalties.

To ensure that the team is prepared to use the Data Analytics solution in a
efficient and make the most of the insights generated, it is important to follow some practices such as:

● Appropriate Training
● Data Culture
● Easy Data Access
● Ongoing Support
● Knowledge Sharing
● Performance Metrics

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