Choose the ideal data professional for your company
Data engineer, analyst, scientist, or analytics engineer — each role solves different problems. How to identify which one to hire for your operation.
So you want to have a data-driven company, right?! 😉
This means you’ve probably already researched the advantages of launching a data operation, understood the main infrastructures that need to be implemented, know how to build your Modern Data Stack, and want to get started right away… But how do you choose the ideal data professional to leverage your strategy?
In this blog, we’ll explain who the main data professionals are and how each of them can help at every stage of your operation’s maturity.
What is your company’s data maturity level?
What if we told you that every company is at some step of its data-driven journey, even if it doesn’t know it yet? That’s why it’s important to understand your company’s data maturity level. There are a few ways to do this:
What results is your organization aiming to achieve using its data?
First of all, every strategic operation in your company needs to start with solid planning. After all, how can you know who to hire and where to implement without having a complete view of the situation and understanding where you want to go? At this stage, you need to combine a clearly defined picture of what is happening with knowledge of what needs to be implemented. That’s why company leadership needs to be aligned with whoever is leading the data strategy, creating a detailed project plan.
But there’s no need to walk this journey alone! If you’re starting this project from scratch, your company can rely on strategic partners or a data engineering and BI team, who will indicate the best implementations for your current stage.
Data professionals
At every stage of your company’s data maturity, it’s important to have the right professionals on your team. So, we’ll explain what each one does and how they can help your company move forward in its data-driven journey according to each maturity phase.
To understand the different phases of your company’s data maturity, Erathos created its own framework to help identify which phase your business is currently in:
1) Understand your past, with Data Engineering and BI
In this first stage of the data-driven journey, the goal is to find a way to ensure your data is ready to be analyzed, available when needed, and updated quickly. In other words: unify your data sources by moving them into an analytical model, which helps prevent information from being segregated across different areas and systems—in other words, the so-called data silos.
By implementing the necessary structure to process already collected data, it becomes possible to gain a solid perspective of everything that has been done in the company and build dashboards with historical series to track metric evolution over time.
Other important points to work on at this stage are your company’s data literacy and implementing ways to improve data culture across all departments, bringing your team members into the strategy.
2) Track your present, using Analytics and BI
When your company has strong data infrastructure, BI, and Analytics in place, it becomes easier to analyze past scenarios and have the arguments needed to understand and act when something deviates from expectations, or to identify new opportunities based on what has already been implemented, opening room for even more innovation.
Once you open access to data and the opportunities unlocked by your data infrastructure, it becomes much easier to implement your Analytics and BI structure to gain a clearer picture—with ad hoc analyses, the use of statistics, and more robust analyses such as RFM and MBA (Market Basket Analysis), the establishment of realistic long-term goals, and reinforcement of results.
At this stage, your company has already moved past the basic implementation phase, can already perform more robust analyses, and is already able to think about automating certain tasks. When the organization is ready to implement more specialized computational technologies, it means it is reaching the third phase. #
3) Predict your future, with the use of Artificial Intelligence
Many entrepreneurs, when thinking about launching a data strategy, want this process to become increasingly modern, automated, and powered by artificial intelligence, machine learning, and deep learning at multiple levels.
However, this is not something that happens overnight. When your company is ready to implement technologies based on artificial intelligence, it means it is quite advanced in its data maturity, and at this stage it is necessary to have the right professionals to use them.
Throughout the data analysis that takes place in the company’s other stages, it is possible to understand the problems, recurring scenarios, and optimization opportunities in order to minimize these errors through the implementation of automation and artificial intelligence.
Important notes on data maturity
Although we have outlined data maturity in a framework, it’s important to keep in mind that this is not a linear process. For example: a company may have points that can be worked on simultaneously, as is the case with some areas that already have a highly data-focused approach, or data infrastructure that is already scalable, making it possible to apply BI and Artificial Intelligence in a short time…
This is an issue that varies from organization to organization and can be measured in many different ways. Only a careful analysis of your company’s data maturity can define what will be necessary to progress through each of these stages. Therefore, it is important to have strong BI, Analytics, Engineering, and Data Science professionals nearby to support each phase.
Data Engineer
Within companies, the data engineer is the professional mainly responsible for collecting and processing data, leaving it in a standard that is suitable for use across all areas of the organization and within an analytical database. They are also responsible for the mechanisms used to update this data.
This is the professional who will extract raw data and model it in a useful way so that analysts and data scientists can generate smarter reports and insights for your company’s stakeholders without wasting time collecting and processing information.
Data Analyst
These professionals are responsible for analyzing data and information with the objective of solving problems within the organization: whether by implementing new structures, helping formulate new strategies, or even translating data into formats that are understandable to all stakeholders within an organization. The Analyst is the one who will implement and analyze the famous dashboards in BI tools and provide complete reports on the company’s situation.
Data Scientist
Throughout the process of implementing a data strategy, this professional is important for identifying problems and opportunities arising from data analysis, understanding and determining the main variables and datasets, collecting structured and unstructured data, cleaning and validating databases to ensure efficiency, auditing data, and identifying patterns and trends in the available information set.
With the work of a data scientist, it is possible to reach important conclusions and identify opportunities to guide decision-making.
Conclusion
For each stage of your data-driven journey, there is a data professional who can help your company reach even higher levels and transform your strategy. Every professional on your data team plays a fundamental role in your data-driven journey—whether a data scientist, analyst, or engineer—but we understand that it is costly to hire an entire team and still apply the necessary tool and infrastructure implementations; however, that is not the only way to ensure this process…
Another alternative, which has been widely used in the market, is hiring specialists who offer BI services, with outsourcing of a complete data team to guide your company through all stages of data maturity. Companies like Erathos offer this service through their own highly scientific methodology, helping you generate value from your data in record time.
So you want to have a data-driven company, right?! 😉
This means you’ve probably already researched the advantages of launching a data operation, understood the main infrastructures that need to be implemented, know how to build your Modern Data Stack, and want to get started right away… But how do you choose the ideal data professional to leverage your strategy?
In this blog, we’ll explain who the main data professionals are and how each of them can help at every stage of your operation’s maturity.
What is your company’s data maturity level?
What if we told you that every company is at some step of its data-driven journey, even if it doesn’t know it yet? That’s why it’s important to understand your company’s data maturity level. There are a few ways to do this:
What results is your organization aiming to achieve using its data?
First of all, every strategic operation in your company needs to start with solid planning. After all, how can you know who to hire and where to implement without having a complete view of the situation and understanding where you want to go? At this stage, you need to combine a clearly defined picture of what is happening with knowledge of what needs to be implemented. That’s why company leadership needs to be aligned with whoever is leading the data strategy, creating a detailed project plan.
But there’s no need to walk this journey alone! If you’re starting this project from scratch, your company can rely on strategic partners or a data engineering and BI team, who will indicate the best implementations for your current stage.
Data professionals
At every stage of your company’s data maturity, it’s important to have the right professionals on your team. So, we’ll explain what each one does and how they can help your company move forward in its data-driven journey according to each maturity phase.
To understand the different phases of your company’s data maturity, Erathos created its own framework to help identify which phase your business is currently in:
1) Understand your past, with Data Engineering and BI
In this first stage of the data-driven journey, the goal is to find a way to ensure your data is ready to be analyzed, available when needed, and updated quickly. In other words: unify your data sources by moving them into an analytical model, which helps prevent information from being segregated across different areas and systems—in other words, the so-called data silos.
By implementing the necessary structure to process already collected data, it becomes possible to gain a solid perspective of everything that has been done in the company and build dashboards with historical series to track metric evolution over time.
Other important points to work on at this stage are your company’s data literacy and implementing ways to improve data culture across all departments, bringing your team members into the strategy.
2) Track your present, using Analytics and BI
When your company has strong data infrastructure, BI, and Analytics in place, it becomes easier to analyze past scenarios and have the arguments needed to understand and act when something deviates from expectations, or to identify new opportunities based on what has already been implemented, opening room for even more innovation.
Once you open access to data and the opportunities unlocked by your data infrastructure, it becomes much easier to implement your Analytics and BI structure to gain a clearer picture—with ad hoc analyses, the use of statistics, and more robust analyses such as RFM and MBA (Market Basket Analysis), the establishment of realistic long-term goals, and reinforcement of results.
At this stage, your company has already moved past the basic implementation phase, can already perform more robust analyses, and is already able to think about automating certain tasks. When the organization is ready to implement more specialized computational technologies, it means it is reaching the third phase. #
3) Predict your future, with the use of Artificial Intelligence
Many entrepreneurs, when thinking about launching a data strategy, want this process to become increasingly modern, automated, and powered by artificial intelligence, machine learning, and deep learning at multiple levels.
However, this is not something that happens overnight. When your company is ready to implement technologies based on artificial intelligence, it means it is quite advanced in its data maturity, and at this stage it is necessary to have the right professionals to use them.
Throughout the data analysis that takes place in the company’s other stages, it is possible to understand the problems, recurring scenarios, and optimization opportunities in order to minimize these errors through the implementation of automation and artificial intelligence.
Important notes on data maturity
Although we have outlined data maturity in a framework, it’s important to keep in mind that this is not a linear process. For example: a company may have points that can be worked on simultaneously, as is the case with some areas that already have a highly data-focused approach, or data infrastructure that is already scalable, making it possible to apply BI and Artificial Intelligence in a short time…
This is an issue that varies from organization to organization and can be measured in many different ways. Only a careful analysis of your company’s data maturity can define what will be necessary to progress through each of these stages. Therefore, it is important to have strong BI, Analytics, Engineering, and Data Science professionals nearby to support each phase.
Data Engineer
Within companies, the data engineer is the professional mainly responsible for collecting and processing data, leaving it in a standard that is suitable for use across all areas of the organization and within an analytical database. They are also responsible for the mechanisms used to update this data.
This is the professional who will extract raw data and model it in a useful way so that analysts and data scientists can generate smarter reports and insights for your company’s stakeholders without wasting time collecting and processing information.
Data Analyst
These professionals are responsible for analyzing data and information with the objective of solving problems within the organization: whether by implementing new structures, helping formulate new strategies, or even translating data into formats that are understandable to all stakeholders within an organization. The Analyst is the one who will implement and analyze the famous dashboards in BI tools and provide complete reports on the company’s situation.
Data Scientist
Throughout the process of implementing a data strategy, this professional is important for identifying problems and opportunities arising from data analysis, understanding and determining the main variables and datasets, collecting structured and unstructured data, cleaning and validating databases to ensure efficiency, auditing data, and identifying patterns and trends in the available information set.
With the work of a data scientist, it is possible to reach important conclusions and identify opportunities to guide decision-making.
Conclusion
For each stage of your data-driven journey, there is a data professional who can help your company reach even higher levels and transform your strategy. Every professional on your data team plays a fundamental role in your data-driven journey—whether a data scientist, analyst, or engineer—but we understand that it is costly to hire an entire team and still apply the necessary tool and infrastructure implementations; however, that is not the only way to ensure this process…
Another alternative, which has been widely used in the market, is hiring specialists who offer BI services, with outsourcing of a complete data team to guide your company through all stages of data maturity. Companies like Erathos offer this service through their own highly scientific methodology, helping you generate value from your data in record time.