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Roles and Responsibilities of a Data Analyst in a Modern Data Team

The Data Analyst role has long existed under different names throughout the evolution of companies ever since businesses realized at scale, data-informed decisions can be life or death to businesses. The role has evolved quite significantly as well, and while the contextual requirement has remained consistent, the requirement to understand new data stacks, languages, and other technological shifts have only grown. Because the Data Analyst role is often a bridge, with multiple checkpoints between business, data and technical teams, today's Data Analysts are more technical than ever, with exposure to storage solutions, visualization and analytics toolings, and AI.

When planning to hire or form your analyst team, it is crucial to understand how you set the team up for success. Of course, everyone would love to do that by hiring the best Head of Data available, but that we know is a luxury for most companies. So what do we need to understand about the modern Data Analyst to hire, onboard, and set the right expectations for success?

We will cover:

  • An exploration of the evolution of the Data Analyst role over the past couple of decades.
  • Understanding of the roles and responsibilities of a Data Analyst.
  • Interaction of a Data Analyst with other members of a Data team
  • Software and tools available for Data Analysts to meet business and technical requirements
  • How Kaldea supports Data Analysts

The Evolving Role of the Data Analyst

With the recent influx of increased storage and computing power, the data to be analyzed has increased exponentially. As a result, the cloud data warehouses, lakes, and the modern data stack came about to be.

Relational databases and spreadsheets changed the game by providing more efficient ways of gathering, storing, and processing significant quantities of data. But even these had their limits when it came to visualizing patterns and statistical outcomes from the information.

Today, what would have taken traditional data analysts several hours of data aggregation, cleansing, querying, and visualizing now technical takes minutes to execute when you fully understand the path. However, while technical execution from a machine’s perspective has improved drastically, the human side coordination and operation have not. The exponential growth of data and tools has given rise to a new set of problems which is both blessing and a curse for today’s analysts.

In more recent times, the application of Artificial intelligence, Data Science and Machine Learning allows for the capacity of Data Analysts to further their responsibilities to include the initial exploration stage of the creation of predictive models. A data analyst's role has changed dramatically from manual data entry and calculation to using modern computers and AI for predictive analysis.

Despite the rapid development of the data and analytics domain and the introduction of more data-related disciplines, such as Analytics Engineers, Data Scientists, Data Engineers, Machine Learning Engineers and MLOps Engineers, the demand for Data Analysts is still the dominant one.

AI Career Popularity Based on Google Search

AI Career Popularity Based on Google Search

How Data Teams have Transformed over Time

Big data and the advent of the digital era has transformed how medium, and large organizations prioritize team development, business processes, and company-wide goals. One of the primary ways big data has transformed organizational operations is by enabling data-driven decision-making. The availability of vast amounts of data allows organizations to make more informed and strategic decisions rather than relying on intuition, expert opinions or gut feelings.

Furthermore, utilizing big data and digital technologies has enabled organizations to optimize their business processes. Companies can identify and eliminate bottlenecks or inefficiencies by collecting and analyzing data on existing processes, resulting in improved efficiency and productivity. The digital age has also enabled organizations to provide personalized customer experiences. By analyzing customer data on preferences and behaviours, organizations can tailor their products, services, and marketing efforts to better meet individual customers' needs. The modern age of technology has made it easier for teams to collaborate. Videoconferencing and task management tools empower organizations to cooperate even when members are spread across the globe, allowing them to exchange ideas and information rapidly.

The current digital transformation initiatives have led to the automation and digitization of many manual processes, including paper-based record keeping, manual data entry, and physical document storage and inventory management. To support these changes, data teams have expanded in size to include new roles related to the needs of a data-driven organization and society. These efforts have allowed data analysts and data scientists to better support the demands of modern business and society through advanced analytical tools and techniques. The digitization of manual processes has also enabled organizations to more efficiently and effectively manage and analyze data, leading to improved decision-making and enhanced business outcomes.

The average data team comprises three prominent job roles: Data Engineers, Data Analysts, and Data Scientists. Some teams can even have distinct positions for more complex projects, such as a Data Project Manager or Analytics Engineer. All these members operate together under the oversight of an executive or manager to deal with every element connected to data management, from collecting and storing data to processing and analyzing that data to generate actionable insights. 

Data Engineers are responsible for developing and maintaining the systems and infrastructure necessary to handle vast amounts of data, while Data Analysts use statistical and analytical techniques to extract insights from that data and present it in a meaningful way. Data scientists leverage advanced machine learning and statistical methods to build predictive models and algorithms based on data. These three roles and any specialized roles work in tandem to ensure that data is collected, processed, and analyzed, efficiently and effectively, enabling organizations to make informed, data-driven decisions.

Evolution of the data analyst

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The increasing volume and complexity of data have led to the evolution of data teams and the specialization of roles within those teams. Data Engineers are now responsible for building and maintaining effective and secure data pipelines, particularly in cloud environments. Data Analysts use a range of languages (e.g. Python, R, SQL) and analytical tools (e.g. notebooks, data studio) to extract and communicate insights from data to stakeholders.

On the other hand, data scientists focus on creating feature sets from data and training predictive models using statistical and machine learning techniques. The role of the data analyst, in particular, has evolved significantly over time as the importance of data in decision-making has grown. In addition to collecting and organizing data, data analysts now use statistical and analytical techniques to extract insights and communicate those insights to stakeholders. They must also be proficient in various tools and technologies, including data visualization and reporting tools, statistical software, and machine learning platforms. As data becomes increasingly central to business decision-making, the role of the data analyst is expected to evolve and expand in scope. 

Overall, data teams play a critical role in bridging the gap between the technical capabilities of the data stack and the organization's business needs, providing valuable insights to inform strategic decision-making and drive business success.

Data Analysts in Machine Learning and Data Science Teams

A data analyst on a machine learning team is responsible for a range of tasks related to the development and implementation of machine learning models and solutions. Some of the specific responsibilities of a data analyst in a machine learning project may include: collecting, processing, and presenting meaningful information extracted from the data. Let's take a closer look.

Data Collection

One of the data analyst's primary responsibilities is aggregating data from various sources that meet the business requirement. Data discovery and aggregation is a process directed by a problem statement. A problem statement typically describes the issue to be solved or any optimisation steps on the existing methods to reach the desired process. 

Machine learning models' accuracy metrics are partly governed by the availability of quality data to the models during the training phase. Data Analysts typically have to consider data quality from sourced data and the storage solutions to utilise for the data collected. Distinct considerations a data analyst has to make when prioritising data quality are the following:

  • Relevance: Does the data samples in the dataset correspond to the development of a solution detailed in the problem statement.
  • Completeness: Are there any missing elements in the dataset that would affect the accuracy of a solution or the completion of the downstream processes in a typical machine learning workflow?
  • Uniformity: Consideration of the coherence and uniformity of that data collected from multiple sources in terms of format, structure and content.
  • Accuracy and Reliability: Credibility of data source and confirmation of data accuracy.

Data collection is a process where Data Analysts collaborate with Data Scientists. Data Analysts, during data collection, focuses on ensuring the quality, integrity and other factors, as mentioned earlier, and require the expertise of Data Scientists to efficiently automate the sourcing of data and collaborate on appropriate prediction models and analysis technique that fits the data well.

Data Processing

Data processing is concerned with the steps taken to transform data from its raw state into one that can be utilised in downstream processes such as model training or visualisations in dashboards.\After data is collected, it needs to go through a process of filtering and cleaning to ensure incorrect values are removed, and the data integrity is maintained.

The process of examining and characterising data in every way possible before training machine learning models on that data is called exploratory data analysis (EDA). Some analysts also use statistical models like regression during EDA to study trends, patterns or correlations in their dataset.

Data Analysts collaborate with Data Engineers when working on data processing tasks and developing new data workflows. When data ingestion reaches a certain scale and is a component of critical systems, it’s necessary to build data pipelines that facilitate the efficient flow of data from one process to another, and, in conjunction, the output of these data pipelines is highly relevant and aligned to the business goals.

Data Engineers ingest data from multiple sources into a bronze layer and perform data transformations to take data into the silver and gold layers. The data analyst's knowledge and expertise contribute to understanding business goals and needs data teams must consider; this drives the data transformation, presentation and latency considerations at each of these layers. 

A key activity for data teams in the supply chain industry is supply chain and inventory optimization. The optimization techniques that are leveraged include machine learning and statistical approaches that make the movement of goods from one place to another as efficient and cost-effective as possible. Data engineers in this scenario are responsible for creating the data infrastructure to ensure efficient data flow amongst supply chain applications used to track item delivery, journey and cost, whilst data analysts are responsible for identifying areas of optimization in data processes based on business needs, statistical and analytical examinations of data.

How Kaldea Meets The Demands of Modern Data Analysts

While cloud based machine and software capabilities have grown, the operational management and knowledge requirement of the latest stacks have also grown. Oftentimes, fast adoption of multiple stacks to scale your data and insight is becoming a curse of its own, slowing down your company’s data operation. The data space today is convoluted with tools and cross-disciplinary collaboration. In the broader sense, to streamline data science teams efficiently, we've over-engineered, and created scenarios where tool selection fatigue, non-transfer of tribal knowledge and technical requirement over complication are common hurdles data analysts have to tackle.

Kaldea is a platform that solves the common problems that data analysts and data teams experience.


To meet the demands of modern data analysts, Kaldea provides a platform that centralizes all analytics from discovery to reporting. With Kaldea, analysts can easily collaborate with stakeholders, share iterations of data analysis across all stages (from discovery to reporting), and see the full context of data – making it easier to explore and understand problems. Centralizing information and knowledge on Kaldea also ensures that everyone uses the same tools, making working with data more efficient for team members.


Kaldea is a platform that helps data analysts and data teams work more efficiently. Data is centralized on Kaldea, which makes it easy for analysts to collaborate with stakeholders and share data analysis iterations across all stages. Additionally, Kaldea provides an intuitive workspace where analysts can easily organize their reports and charts. This organization prevents data silos from forming. Writing SQL queries is a primary requirement for Data Analysts traversing through databases for data and insights. Therefore, having a record of commonly used queries that can be shared across teams and organisations promotes a culture of collaboration and gets the modern data team back to productivity.


Kaldea easily integrates with frequently used data storage solutions like BigQuery, PostgreSQL and Snowflake. Constantly monitoring and managing data sources can be extremely demanding, especially as data requirements increase. Kaldea offers a platform where Data Analysts have a bird's eye view of all available data sources. Our software also makes it easy to schedule jobs that automate the process of gathering aggregated data from various sources. And if you need visual representations of your findings, Kaldea provides plotting and charting capabilities.

Benefits of Kaldea for Data Analysts

  • Kaldea features that solve common problems faced by data analysts associated with data discovery, collaboration, and communication
  • Kaldea centralizes all analytics and makes it easier for analysts to explore data
  • Kaldea also helps data analysts organize their reports and charts, preventing data silos from forming.


Data analysts are crucial in determining an organization's success or failure. Though their value is significant, it comes at the cost of a substantial effort. Additionally, data analyst roles are constantly changing and progressing; within a short period, this field has seen extensive evolution. Given that data production worldwide is increasing rapidly, there will likely be high demand for data analysts in the future.

Roles and Responsibilities of a modern Data Analyst

  • Data analysts ensure data quality before it is used in downstream processes.
  • Data analysts are responsible for transforming data from its raw state into one that can be utilised in downstream processes.
  • Data Analysts work with data scientists to develop models and with non-technical leaders to make high-level decisions.
  • Data analysts play a critical role in understanding data and exploring solutions.
  • Data analysts collaborate with data scientists to efficiently automate data sourcing.
  • Analysts use exploratory data analysis to study dataset's trends, patterns, and correlations.

Data Analysts have different roles and responsibilities depending on the company or industry they work for. Some places of business require analysts to be technically experienced and able to communicate well with Data Engineers and Scientists to construct data pipelines, machine learning models, etc. Other companies might want their analysts to focus more on domain-specific knowledge that can help make crucial decisions when combined with the organizations' data.

The versatility of the Data Analyst role is born out of the ever-evolving field of data and AI. With the additional ambiguity of role definition within the field, it is safer for a Data Analyst to cater to the fluidity in skillset and expertise the field demands.

Kaldea is a state-of-the-art analytics tool that fills the needs of dynamic data analysts. With its unique knowledge recording capabilities and data processing job scheduling all in one platform, Kaldea provides value for both technical and business analysts.