Companies are amassing more data than ever before. This helps them identify emerging trends and gauge performance, make better decisions and, ultimately, create predictive models and other innovative products. However, raw data on its own provides little value. It needs to move from sources to destinations and needs cleaning and modeling to generate important insights. That’s where a data team comes in.
There are three critical roles companies hire to help with their data engineering and data science needs: data analysts, data engineers and data scientists. To facilitate a smooth progression to data maturity and innovative uses of data, it’s critical to understand the differences between each role’s core responsibilities and skill sets, as well as how each ultimately supports your organization’s data initiatives.
What is a data analyst?
Data analysts are the bread and butter of a data team. That makes them the first data hires an organization should make. Without analysts, an organization will likely struggle to systematically use data to support decision making. Analysts tend to have undergraduate degrees in quantitative disciplines, although such credentials are not strictly necessary and are not as important as a demonstrated ability to turn data into actionable insights.
Data analysts: Responsibilities and core tasks
The fundamental responsibility of a data analyst is to help an organization support decisions with data. Concretely, this often takes the form of building data models, visualizations, dashboards and reports to help stakeholders make sense of data. To this end, analysts must engage in the following process:
- Data collection: Analysts gather data from various sources, such as transactional databases, customer relationship management systems and social media platforms. The data is typically stored in a data warehouse —a structured, relational data repository optimized for reporting and analysis.
- Data integration: Once data is collected, it needs to integrate into a single source of truth. This involves combining data from multiple sources, cleaning and validating it and ensuring that it’s accurate and consistent. This also includes exploring the data to make sense of it.
- Data analysis: The next step is, as their title mentions, to analyze the data to uncover insights and trends by modeling it into assets that support the next step. Analysts may explore predictive analytics and machine learning but generally do not focus on these.
- Data presentation: The insights and trends are presented in a user-friendly format, such as charts, graphs and dashboards. This enables business users to easily access and understand the data to make informed decisions based on the insights.
- Actionable insights: Finally, the insights are used to inform business decisions, such as product development, marketing campaigns and supply chain optimization. By using business intelligence effectively, organizations can gain a deeper understanding of their operations, identify opportunities and risks — all ultimately to optimize their performance.
As an organization grows in complexity and size, analysts may specialize in specific domains and become attached to specific functional units within an organization. At the same time, there may be a core team of data analysts who directly serve the needs of the organization’s leadership. This structure is the hub-and-spoke model of organizing a data team.
Stats, spreadsheets and SQL
Analysts should have a strong understanding of statistics and numerical analysis. They should have familiarity with business intelligence platforms and data visualization. Their mainstay language is SQL, enabling them to easily navigate relational databases and data warehouses in order to produce analytics-ready data models. Analysts may also be familiar with scripting and computational languages such as Python or R. Ultimately, they must use data to tell a story and support or recommend a course of action.
Early in a company’s development, analysts may manually stitch data sets together (i.e. as spreadsheets) in order to create the data models they need to create reports or dashboards. They may also build custom data pipelines as needed. This is not a scalable, efficient or sustainable process, nor does it leverage data analysts’ main area of expertise. Modern-day data movement and integration tools offer a user-friendly, no-code approach to collecting data, freeing analysts to analyze and present data.
Which BI platforms do analysts use?
- Tableau: A modern BI platform that helps data scientists visualize large quantities of data and create more accurate forecasts.
- Looker: A big data analytics platform from Google Cloud that data scientists use to set up real-time dashboards, create data-driven workflows and more.
- Power BI: A BI tool from Microsoft that offers top-notch data visualization and built-in AI capabilities.
What is a data engineer?
A data engineer works behind the scenes to create and maintain the data infrastructure that an organization uses to manage data. They aim to make data easily accessible to all stakeholders and applications as needed.
Data engineers typically have educational backgrounds in computer science and software engineering.
As companies become more data-oriented, they need to build a solid foundation and systemic approach for collecting, analyzing and modeling data. Whether you’re a startup or an established business, data engineers are invaluable to continued, sustained growth.
Data engineers: Responsibilities and core tasks
A data engineer’s primary focus is to build and maintain a company’s data infrastructure — the systems that enable data consumption, storage and movement. This involves building and maintaining data pipelines that ingest data from external sources and move it to a repository, like a data warehouse or data lake. That also involves building pipelines to production applications and productionizing machine learning models built by data scientists (more on that later).
Data movement using an Extract, Load, Transform (ELT) process begins with raw data, meaning that it hasn’t been processed or organized yet. As a result, that raw data might be disorganized or contain errors. Data engineers may use languages like Python and Java as well as SQL to prepare this data for analysis.
Typically that process looks like:
- Extract: Raw data is pulled from a source, which can be any platform, repository or application the organization uses. This is accomplished programmatically either through an off-the-shelf tool or with a series of scripts written by the data engineer in a language like Python or Java.
- Load: The data is then routed to a destination. That destination serves as a repository for analytics like a cloud-based data warehouse, or an operational system like the backend database of an application.
- Transform: The data must be properly structured and clean before it’s used for analytics or operations. Data engineers will create and apply formatting rules to data sets to remove redundant and unusable data. Though data analysts frequently transform data as well, they generally don’t do so for operational uses. Transformations may be performed using SQL within the destination, or in Python prior to loading (i.e. in ETL).
In short, data engineers ensure that a company’s architecture will support the analytical and operational requirements of the company they work for.
What languages does a data engineer use?
Data engineers use a variety of languages to build data architectures, develop modeling processes and create reliable data flows. These include:
- Python: A high-level programming language that helps data engineers build efficient data pipelines, script and automate behaviors and perform data analysis.
- Java: Another high-level, object-oriented programming language that’s used to develop desktop and mobile applications. Data engineers use this language to build data systems.
- Scala: A general-purpose language that data engineers use to structure, transform and serve data.
What orchestration and workflow management tools do data engineers use?
Data orchestration is at the center of data infrastructure. It involves collecting data from disparate sources and organizing it into one cohesive system. Data engineers use the following data orchestration tools:
- Airflow: Originally developed by Airbnb, an open-source data orchestration tool used to schedule, organize and monitor batch-oriented workflows.
- Luigi: A Python package, originally by Spotify, that creates more advanced pipelines to accomplish batch jobs.
What big data frameworks do data engineers use?
Big data allows organizations to gather meaningful insights from their data and make informed business decisions based on those insights. Data engineers use their engineering skills with the following big data frameworks to process large amounts of data:
- Hadoop: An open-source framework capable of processing large data sets across clusters of computers using simple programming models.
- Spark: An open-source analytics engine built for large-scale data processing. Like Hadoop, it splits up workloads across different nodes.
What is a data scientist?
Like analysts, data scientists use analytics and reporting tools to identify and extract meaningful insights from large amounts of data. Unlike analysts, data scientists also create predictive models to produce forecasts based on historical data, as well as prototype data-driven, automated systems.
Their work enables companies to leverage artificial intelligence (AI) and machine learning (ML). Data scientists typically rely on data engineers to build and maintain data infrastructure and productionize their machine learning models.
In addition to mining big data for insights, data scientists may present their findings to non-technical stakeholders – a duty often shared with analysts. This means they require proficiency with data visualization tools and must have strong communication skills.
Data scientists: Responsibilities and core tasks
Data scientists are responsible for prototyping artificial intelligence and machine learning models. At its simplest, this includes predictive and prescriptive analytics.
Predictive learning depends on algorithms to predict future behaviors based on historical data. They are also sometimes trained to ingest and respond to new data over time. For example, a company might use predictive models to estimate the chances of a lead converting.
More advanced data science may include sophisticated products that incorporate artificially intelligent agents, recommendations, automated decision-making and more. Data scientists often depend on data engineers to support their efforts with the appropriate data infrastructure and to productionize their prototypes.
What languages do data scientists use?
Data scientists use many languages to build data models, create AI/ML systems and solve complex problems. These include:
- Python: A high-level programming language that offers data science libraries like NumPy, Scikit-learn and Matplotlib to build data models, create visualizations and more.
- Java: An object-oriented programming language used to create statistical models, train machine learning algorithms and more.
- R: An open-source programming language that data scientists use to create statistical models and data visualizations.
- Scala: A general-purpose language that supports object-oriented and functional programming. It’s designed to take advantage of big data processing.
What big data frameworks do data scientists use?
Like data engineers, data scientists rely on big data frameworks when they work with large data sets. These frameworks include:
- Hadoop: An open-source software framework that enables data scientists to perform data operations on large data sets across clusters of computers.
- Spark: A data processing engine capable of performing batch processing and stream processing.
What computing platforms do data scientists use?
Computing platforms include a suite of technologies and tools for data management, advanced analytics and machine learning. Data scientists use the following tools:
- Jupyter: A web-based development environment that lets data scientists build and arrange workflows for machine learning projects.
- R notebook: An R Markdown document that data scientists use to orchestrate an end-to-end data analysis workflow.
Like analysts, data scientists use BI platforms to perform advanced statistics and predictive analytics on their data sets.
Hiring data analysts, data engineers and data scientists
Each of the three data professionals perform critical roles within an organization. To plan a hiring roadmap, consider consulting the data hierarchy of needs:
While artificial intelligence and machine learning models are the pinnacle of data science, you must build a strong foundation before you can effectively leverage them.
The foundation of a robust data operation involves hiring analysts and equipping them with a modern data stack, consisting of a data pipeline, cloud-based data warehouse, transformation tool and business intelligence platform. These allow your data team to produce regular reports, provide dashboards to functional units and departments across your organization, promote data democratization and scale.
As your organization’s data operations expand, it will eventually make sense to hire data engineers to design a robust data architecture by productionizing custom processes that you can’t easily purchase off the shelf.
Finally, you will be ready to hire data scientists to explore and prototype innovative uses of artificial intelligence and machine learning based on the data your organization produces and collects.