Discover the 6 most in-demand roles in Data Analysis | Xyclos
In all professions or activities there are always different levels of knowledge and applicability of that knowledge, so a way is sought to categorize by assigning names to roles, functions and requirements.
The field of data analytics is no exception, so let’s explore the key roles it encompasses.
First, we can classify the actors involved into two categories: data creators and consumers.
Creators
This group includes business intelligence (BI) analysts, developers, and personnel responsible for architecting and maintaining BI infrastructure, classified according to their level of involvement in BI projects.
Data Architect
Function :
The data architect is responsible for designing the infrastructure and overall architecture that will organize the storage, processing, and flow of data. He or she ensures that systems are optimized to handle large amounts of data and that all components (databases, cloud systems, etc.) are properly integrated.
Define how data is collected, stored, and accessed in an organization.
Responsibility:
Design the database structure, choose the appropriate technologies and establish the long-term data management strategy.
Required knowledge:
Systems and database design.
Data regulations and standards.
Data security and governance.
Data Engineer
Function :
The data engineer builds the "pipes" that transport data from sources (such as applications or devices) to storage and analysis platforms. He or she is responsible for preparing the data so that it can be used in subsequent analysis.
Design, build and maintain the data architecture required for analysis.
Responsibility :
Create ETL (Extract, Transform, Load) processes, integrate diverse data sources, clean and transform data so that it is ready to be analyzed.
Required knowledge:
Experience with SQL and NoSQL databases.
Familiarity with Big Data processing tools (Hadoop, Spark).
Programming and scripting skills.
Data Scientist
Function :
The data scientist explores and analyzes data to extract useful knowledge that can be used for decision making. He uses advanced techniques of statistical analysis, predictive modeling, and machine learning.
Use advanced analytics, machine learning, and predictive modeling techniques to solve complex problems.
Responsibility :
Define analytical models and algorithms to find patterns, trends or anomalies in data, answering complex business questions.
Required knowledge:
Programming in Python, R or other languages.
Mastery of machine learning and deep learning algorithms.
Advanced statistics and mathematical modeling.
Machine Learning Engineer
Function :
The machine learning engineer takes the models developed by the data scientist and turns them into systems that can be used at scale. He or she develops and implements algorithms that enable systems to continuously learn from data.
Deploy and optimize machine learning models in production environments.
Responsibility :
Deploy predictive models into production, optimize their performance, and ensure that models run efficiently on live systems.
Required knowledge:
Software development and engineering principles.
Model optimization and large-scale data management.
Knowledge of MLOps.
Data analyst
Function:
The data analyst uses the processed data and the results of the models created by the data scientist to generate reports, descriptive analysis and visualizations that help decision makers understand what is happening in the business.
Responsibility:
Create BI reports and dashboards, interpret results, and provide data-driven recommendations for business action.
It focuses on collecting, processing and analyzing data to extract insights that support various areas of the organization and improve decision-making.
Interpret data and analyze results using statistical techniques.
Collect and organize data from different sources.
Provide ongoing reporting and develop dashboards that reflect key metrics.
Identify patterns, trends and opportunities in data.
Collaborate with multidisciplinary teams to understand information and data needs.
Ensure the quality and accuracy of data used for analysis.
Required knowledge:
Handling of tools such as Excel, SQL and visualization software (Tableau, Power BI, DAX, M Language).
Understanding basic statistics and analytical methods.
Communication skills to effectively present findings to technical and non-technical audiences.
Problem-solving and critical thinking skills.
Have knowledge of communication and graphic design
Important note:
Some roles may overlap. For example, a data scientist may perform data engineer duties, or a machine learning engineer may take on data scientist responsibilities. These roles may vary depending on the structure and size of the company, as well as the specialization of the team.
Okay, with the infrastructure ready, now let's see what the role of BI (Business Intelligence) information consumers consists of.
Consumers
Function :
It is the end user, such as: managers, executives, employees and other stakeholders, who use the information processed and available from BI in Reports and Dashboards
Responsibility:
Identify trends, deviations and insights to make data-driven decisions.
What does it take to be a good information consumer?
Application
Understanding and handling the BI tool (Tableau, Power BI, etc.): It is essential that information consumers are familiar with the BI tools used in their organization. Although they do not need to know how to create reports from scratch, they should be able to navigate dashboards, apply filters, interact with visual objects (visualizations) and extract the information relevant to their needs. This allows them to make the most of the available resources and make informed decisions.
Business
Business knowledge and the ability to understand and create objectives and KPIs according to the industry or business: A good information consumer must have a deep understanding of the processes, objectives and challenges of their business area. This understanding allows them to contextualize the data, interpret the insights correctly and align them with business strategies. In addition, contributing to the definition and adjustment of KPIs ensures that the metrics are relevant and actionable.
Statistics and mathematics
Fundamentals for understanding and applying analytical techniques: Although consumers are not expected to be experts in advanced statistics, having basic knowledge helps them interpret data more accurately. Understanding concepts such as averages, medians, standard deviation, correlations, and trends allows them to evaluate the significance of insights and avoid misinterpretations.
Teamwork
Knowing how to work as a team with information creators: Collaboration between consumers and creators is essential to maximize the value of information. Consumers must communicate their needs, provide feedback, and work hand-in-hand with BI analysts and developers to ensure that reports and dashboards meet their requirements. This also means being open to feedback and willing to participate in iterations to improve BI solutions.
Communication
Communicating insights effectively: The ability to convey findings and conclusions clearly and persuasively is crucial. Consumers must be able to tailor their communication to the target audience, whether internal teams, managers or external stakeholders. This includes presenting data in a visually appealing way, telling stories with the data and highlighting the practical implications of the insights, which will ultimately be the result of having worked as a team with the creators.
Critical thinking
It is important for information consumers to apply critical thinking when analyzing data. This means questioning the veracity and relevance of information, identifying potential biases or anomalies, and validating data sources before making decisions based on them, i.e. staying in touch with the creators in case they detect something that seems anomalous. Be vigilant and contribute.
Curiosity and continuous learning
The BI environment is constantly evolving. Effective BI consumers are proactive in learning new tools, techniques, and best practices that can improve their ability to interpret and use information.
Ethics and data privacy
Understanding and complying with privacy policies and regulations related to data handling is essential to avoid breaches and maintain confidence in the use of information.
Training with Xyclos Academy
Are you interested in developing as a Data Analyst ?
At Xyclos Academy, we offer a learning path designed to take you from the fundamentals to advanced levels of data analysis.
To start, it is essential that you master Excel at an advanced level, which you can achieve with our Intermediate Business Excel Basic and Advanced Business Excel courses. These skills are the cornerstone for handling large volumes of data efficiently.
Once you have a solid grasp on Excel, you'll be ready to learn Power Query, with our Power Query Consolidation of Tables and Files course, which will allow you to automate and optimize ETL (Extract, Transform, and Load) processes.
The next step is to master Power BI at an intermediate level, with the Power BI Interactive Business Analytics course, which will prepare you to perform advanced visual analysis.
Finally, with all the knowledge acquired, you will be able to perfect your mastery of Power BI at an advanced level by taking the Power BI Business Management with KPIs course, aimed at data analysis and management for strategic decision-making.
This clear and meticulously designed learning path will not only help you understand the fundamental concepts of data management, but will also allow you to apply them through practical exercises and real-life cases.
All of this is part of a comprehensive learning process that guarantees solid and in-depth training in data analysis.
#DataAnalysis #DataRoles #DataScientist #DataEngineer #DataAnalyst #DataArchitect #MachineLearning #BusinessIntelligence #DataProfessionals #CareerInData #DataAnalysis #DataRoles #DataScientist #MachineLearningEngineer #xyclos
Comments