Data & Analytics

Connect strategy, data, and technology to impact your customers’ experience.  Find the hidden potential in data and turn it into actionable knowledge to drive decision-making.


How to build a successful data project?

A structured, well-defined approach is vital to ensure success and efficiency in information management when approaching a data project. At Pragma, we approach it by following these steps:

  1. Understanding the need: In this stage, a business discovery is carried out to understand the customer’s needs and objectives. The data analysis must answer critical questions and how these results will impact decision-making.

  2. Data value journey diagnosis: It describes how an organization transforms data into tangible business value throughout multiple stages. This methodology focuses on making the most of the potential of data to obtain competitive advantages and improve decision-making.

  3. Value proposition: It defines the project implementation plan and scope, enabling the capabilities defined by the data value journey, engineering, architecture, or data science. 

  4. Continuous value delivery: It consists of choosing project management frameworks, including planning actions based on data.

Learn about the technologies that can enhance your project

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and move data for analysis in AWS services. It provides an environment where you can create and run ETL jobs without worrying about the underlying infrastructure.

Amazon S3 is a highly scalable and durable object storage service. It is designed to store and retrieve data from anywhere on the web. Objects are stored in S3 “buckets,” which act as containers to organize and manage data.

S3 is known for its high availability, durability, and performance. It also provides options for data encryption at rest and in transit, ensuring the security of your data stored in S3.

AWS Lambda is a serverless computing service that allows you to run code without managing servers. You can create Lambda functions that fire in response to events, such as changes to data stored in S3, direct invocations of an API, updates to databases, and more.

Lambda is highly scalable, and you only pay for the running time of your function, making it very efficient for intermittent or spike-in workloads. It supports multiple programming languages, allowing you to write your functions in the language you are most comfortable with.

Amazon DynamoDB is a fully managed and highly scalable NoSQL database. It is known for its low latency performance and high availability, making it ideal for applications that require fast response times and easy scalability.

DynamoDB uses the key-value storage model and can handle high-traffic, high-speed workloads. You can store and retrieve data from DynamoDB through the API, and you do not need to worry about underlying infrastructure management since AWS takes care of everything.

Amazon Athena is an interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL. Configuring or managing servers is unnecessary as it works on a “pay as you go” concept.

Athena is ideal for ad hoc analysis of large data sets stored in S3. You can run real-time SQL queries on your data and get fast results without loading them into a traditional database.

AWS KMS is a service that allows you to create and manage encryption keys securely. You can use these keys to protect sensitive data and other AWS resources, such as EC2 instances, EBS volumes, and data stored in S3.

KMS offers asymmetric and symmetric encryption options and allows you to control access to your keys through IAM (Identity and Access Management) policies. You can also audit and monitor the usage of your keys through AWS CloudTrail.

Amazon Redshift is a fully managed data warehousing service. It is designed to store and analyze large amounts of data with fast and scalable performance.

Redshift uses the columnar model for storage, enabling high compression and efficient performance for complex analytical queries. It supports standard SQL tools and languages, making it easy to migrate existing applications.

AWS Lake Formation is a service that helps you securely configure and manage a data lake on AWS. A data lake is a raw and processed data repository that allows for storing large volumes of information from various sources.

Lake Formation simplifies the data lake creation and configuration process by automating data access, classification, transformation, and encryption tasks. Additionally, it allows you to define access policies based on roles and permissions to maintain the security and compliance of your data.

Benefits of developing Data and Analytics projects with AWS tools

Efficiency and effectiveness thanks to AWS’s scalability, flexibility, and reliability.

Tools that provide a wide range of services for secure data storage, serverless execution, and analysis of large data sets.
Accelerated application implementations and deployments to take advantage of real-time business intelligence.
Ease of integration to quickly deploy innovation and deliver exceptional value to customers.

Real Impact, exceptional Outcomes

Join the companies that have already achieved their business objectives thanks to Pragma’s Data Analytics services.

puntos colombia


6.3 million Colombians accumulate points for their purchases.

We developed a scalable data architecture, built a data lake, and facilitated more than 1 billion records ingestion.

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img-rotador-caso-estudio-dislicores_Caso de estudio rotador dislicores

Dislicores logo

Customer loyalty and segmentation through Data-Driven strategies.

Discover how Dislicores built a centralized data repository to obtain strategies that use large amounts of information to impact B2B and B2C customer relationship management.

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