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You will see in this article:

Data Engineer
Data Architect
Data Analyst and BI
Data Scientist
Integration and Collaboration: The Path to Maximizing the Value of Data in Organizations

In the past decades, we have witnessed exponential growth in the technology sectors, driven by advancements in innovation, strategic investments, and an increasing demand for digital solutions. The implications of the so-called Industry 4.0, or the fourth industrial revolution, have been substantial, altering the dynamics of society.

With the emergence of technologies such as the Internet, smartphones, social media, virtualization, cloud computing, modern operating systems, artificial intelligence, and machine learning, we have transitioned in a few years from an early technological stage to one that generates massive amounts of data every day. According to Eric Schmidt, former CEO of Google, we currently generate in two days the amount of data it took us 10 years to generate until 2003.

All this demand for technologies that enable working with data—from collection, storage, processing, transformation, and historical data analysis to the creation of predictive models and decision-making—has driven the emergence of different professional areas in the data field.

In this article, we will discuss the main professions, their particularities, and the importance of each in the data lifecycle.

Data Engineer:

In the early 2000s, with the eminent need to develop tools and techniques to handle the processing of large volumes of data, engineers at Yahoo developed one of the first tools to meet this demand, Hadoop, marking a key point for the emergence of Data Engineering.

Transforming data into quality information that enables assertive decision-making required creating an adequate and optimized environment for it. In this context, Data Engineers are the professionals responsible for developing, implementing, and maintaining this environment, known as the data pipeline.

Main Functions:

• Connecting Data Sources: Identifying and integrating data from various sources such as databases, APIs, files, and external services.
• Developing Data Pipelines: Creating and maintaining data pipelines for data extraction, transformation, and loading (ETL), ensuring efficient and accurate integration.
• Data Cleaning: Identifying and correcting errors, inconsistencies, and duplications in the data to ensure quality and accuracy.
• Data Transformation: Applying necessary transformations to prepare the data for analysis, including aggregations, calculations, and normalizations.

Main Tools:

• Apache Hadoop: An open-source platform that uses the Hadoop Distributed File System (HDFS) and the MapReduce processing model to create a distributed data processing and storage environment.
• Apache Spark: A platform with high efficiency and data processing speed at a large scale and in real-time. It supports several languages such as Java, Python, R, and Scala, providing an interface for programming clusters with parallelism and fault tolerance.
• Databricks: A cloud solution offering a collaborative space for data analysis and big data development using Apache Spark. It provides resources for data engineering, data science, and machine learning, facilitating cluster management and data analysis.
• SQL (Structure Query Language): The main programming language used to manage and manipulate relational databases. It allows creating, querying, updating, and deleting data in database tables.
• Python: One of the most popular programming languages in the world. It can be used for data cleaning, transformation, and integration with libraries such as Pandas and NumPy, in addition to workflow automation and data pipeline management.

Data Architect: 

Alongside the need for Data Engineers to perform the integration, consolidation, and structuring of data systems, there emerged the need for a specialized area in the development and implementation of data management strategies, definition of best practices for data collection and storage, and ensuring the integrity and security of information in the data lifecycle.

Thus, the Data Architect is positioned as the professional who will formulate the organization's data strategies, enabling business requirements to become technical requirements. While the Data Architect plans and designs the data structure, the Engineer puts into action the construction of the organization's data infrastructure.

Main Functions:

• Developing Data Architecture: Designing the structure of data systems, including data modeling and defining how data will be organized and stored according to the organization's strategy.
• Scalability and Performance Planning: Designing scalable solutions, optimizing the performance of data systems, and implementing strategies for managing large volumes of data.
• Data Governance and Security: Defining access rules, data quality policies, and implementing controls to ensure data integrity and security.
• System and Technology Integration: Planning and designing how different data systems and technologies will integrate, ensuring effective communication between various data sources and platforms.

Main Tools:

• Apache Airflow: An open-source platform for workflow orchestration, designed to schedule, supervise, and manage data workflows.
• Cloud Solutions: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) are widely used by data architects to develop and manage scalable and integrated data structures.
• Apache Kafka: A distributed data storage platform optimized for data ingestion and streaming processing.
• Python: As with Data Engineers, Python is an indispensable programming language and database management tool in a data architect's daily work.
• MongoDB: A NoSQL database oriented towards documents, storing data in BSON (Binary JSON) format. Instead of using tables and rows like relational databases, MongoDB organizes data into collections and documents, offering a flexible and scalable structure.

Data Analyst and BI: 

With data ready for use, Data Analysts and BI (Business Intelligence) professionals are responsible for transforming large volumes of data into reports and applications that will be used to generate insights and guide decision-making.

The ability to transform massive amounts of data into clear visualizations and analytical applications for each sector is essential to improve the efficiency of business decisions.

From a statistical, comparative, descriptive, and retrospective process, Data Analysts and BI help identify patterns and trends, reduce risks, and discover new business opportunities.

Main Functions:

• Manipulation and Queries in Relational Databases: Primarily using SQL, analysts will constantly interact with relational databases, developing queries to extract, modify, and update data.
• Reporting, Dashboard, and Analytical Applications Development: Creating reports, dashboards, and analytical applications that allow users to explore and visualize data in real-time to support decision-making. Graphs, KPIs (Key Performance Indicators), and other optimized visualizations are developed, aiming to create an analytical environment that supports all business needs.
• Data Analysis and Validation: Identifying patterns, trends, and possible inconsistencies in the data, ensuring the integrity, accuracy, and reliability of information for precise decision-making.
• Metrics and Results Monitoring: Continuous tracking of indicators and other metrics to assess progress against established goals and identify areas of opportunity and improvement. This involves regular analysis of results to ensure that operations and decisions are aligned with defined goals.

Main Tools:

• Power BI: The leading business analysis solution on the market. Used for creating interactive visualizations and customized reports, it allows integration with a series of data sources, real-time data monitoring functionalities, and sharing mechanisms.
• QlikSense: Like Power BI, QlikSense is also a leading cloud solution for business analysis focused on creating interactive visualizations and customized reports. Unlike Power BI, QlikSense uses an associative data model that allows exploring relationships between different data sets interactively and instantly, without the need for a query-based architecture.
• SQL: As with Data Engineers and Data Architects, Data Analysts and BI will certainly need to use a lot of SQL, as they will always be querying relational databases.
• Excel: A classic tool for data analysis, being intuitive and easy to use, supporting some daily tasks of a Data Analyst and BI well. Although it is not a versatile tool that supports complex operations, it is a basic prerequisite for any analyst.
• Python: Although it is not the main tool in a data analyst's daily routine, having knowledge of Python can be essential for performing more versatile analyses, with the Pandas library being one of the main ones for Data Analysts and BI.
  

Data Scientist: 

Just like Data Analysts and BI, Data Scientists also play a crucial role in data analysis and decision-making guidance.

However, while Data Analysts and BI use data to understand trends and patterns to answer business questions based on historical data—that is, what the data tells us about past behavior and how this helps us guide our decisions—Data Scientists primarily work with predictive and prescriptive analyses.

They seek to find trends that are not easily noticeable, by creating statistical and machine learning models to understand the likelihood of a particular future action being beneficial or not for the organization.

Main Functions:

• Data Collection and Manipulation: Although primarily a function of Data Engineers and Architects, knowing how to extract data from different sources, as well as having prior knowledge of how to ensure data accuracy and consistency, is a necessary skill for a data scientist to perform consistent analyses and create predictive models.
• Creating Statistical and Machine Learning Models: From all the data produced by the organization, data scientists will create statistical and machine learning models such as Linear Regression, Neural Networks, and Decision Trees to find patterns and make predictions about possible future scenarios.
• Predictive and Prescriptive Data Analysis: From the results created by predictive models, data scientists, aligned with business interests, will outline a future action strategy that has a higher probability of generating returns for the company.
• Strategic Alignment with Business Areas: In addition to functions that require technical skills, the data scientist will be in constant collaboration with the business area to understand their respective needs and guide decision-making.
  

Main Tools:

• Python: One of the main programming languages used for both creating machine learning models and analyzing trends and patterns. Libraries such as Pandas, Matplotlib, NumPy, and Scikit-Learn are widely used.
• Snowflake: A cloud platform for storing structured and unstructured data, allowing data scientists to work with models and machine learning algorithms iteratively, in an environment designed for processing large volumes of data, providing high-level scalability, security, and flexibility.
  
• R: Like Python, R is also one of the most widely used programming languages by data scientists for statistical analysis. Packages such as RODBC, dplyr, caret, h2o, and ggplot2 are among the most used by professionals and bring all the necessary elements for the development of a data science project.
• Databricks: An optimized and highly scalable environment for developing machine learning models and data analysis. It uses Spark technology and allows programming in Python, R, Scala, and SQL, as well as development in self-managing clusters with infrastructure designed for machine learning.
  

Integration and Collaboration: The Path to Maximizing the Value of Data in Organizations

As we have seen, all the areas addressed play fundamental roles in the data lifecycle, each with development particularities that are crucial in the process of transforming raw data into strategic information to guide decisions within an organization.

It is important to emphasize that all these professionals must have a solid understanding of the data lifecycle and understand their role as an agent that will primarily solve business problems. In an increasingly dynamic and data-driven corporate environment, success is not achieved by a single isolated function but by the integration and cooperation of all professionals involved in the data process. A collaborative approach allows organizations to make the most of their resources, solve complex problems, and make informed decisions that drive growth and innovation.

NCS Consultancy has a multidisciplinary team of highly skilled professionals with expertise in all stages of the data process, ready to offer customized analytical solutions to boost your organization with the latest advancements in the technology market.

Contact us so that our Data & Analytics team can help your organization extract the most from your data and position itself competitively and strategically in the job market.