Data plays an important role in most of the decision-making processes be it related to business or even Engineering processes. It was difficult in the past to set up and maintain the analytics stack easily for it to play a pivotal role in every process hence it was seldom used, however, the advent of cloud infrastructure completely changed the landscape of Data Analytics in Engineering. Cloud Data Warehouses, Self Serve BI tools became the building blocks for the modern analytics 

The Modern data stack led to new specializations and multiple niche roles within an organization to help tame the data beast and make it answer questions by the whip.

This article provides you a comprehensive overview of what Data Analytics in Engineering is, why it exists. How did we reach here, who owns the process within the organization and what are some of the ways it can be leveraged across Engineering practices? 

Understanding Data Analytics in Engineering

In simple terms, it is the practice of moving data along the journey from its rawest form (the transaction or event stream) to its end use either in a report, a machine learning model, or even a simple spreadsheet, etc. Data analytics is the study of breaking down raw data to make decisions about that data. Large numbers of the methods and cycles of data analytics have been automated into mechanical cycles and calculations that work over raw data for human utilization.

Gaming organizations use data analytics to set reward points for players that help them to keep most of the players active in the game. Content organizations utilize large numbers of similar data analytics to keep you clicking, watching, or re-organizing content to get another view or another snap.

Data analytics is significant on the grounds that it assists businesses with upgrading their exhibitions. Carrying out it into the business model means organizations can assist with reducing costs by distinguishing more efficient methods of working together and by putting away a lot of data. An organization can likewise utilize data analytics to settle on better business decisions and assist with breaking down client trends and satisfaction, which can prompt new—and better—products and services.

The Data Analytics in Engineering process includes four different steps. The initial step is to decide the data requirements or how the data is assembled. The second step in data analytics is the way the data is gathered. When the data is gathered, it should be coordinated so it may be dissected properly. The data should be cleaned up before examination. This implies it is scoured and checked to ensure there is no duplication or blunder.

Data Analytics in Engineering is a team effort between numerous data people, analytics Engineers, Data Analysts, Data Engineers, and data scientists with a single goal of producing accurate, timely, and understandable datasets.

Understanding the Need for Data Analytics in Engineering

Data Analytics in Engineering combines the best practices of Software Engineering and applies it to Data analytics so that we can move away from the older chaotic process of data maintenance and collaboratively work on rapid availability, creation of data sets. It lays out some key techniques that analytics teams can adopt 

  • Analytics code should be modular
  • Analytics code should have quality assurance
  • Analytics code should be designed for maintainability

Data Analytics in Engineering was made possible by multiple advances in technologies starting from early 2012. one forms a block in the modern data stack.

  • Fast, inexpensive, and scalable cloud analytics warehouses.
  • Reliable and low-cost data integration tools, for ingesting data from APIs into said warehouses.
  • Shift from traditional ETL processes to ELT to leverage the compute powers unleashed by the cloud warehouses.
  • Easy to use Self Serve BI Analytics tools

Data Analytics in Engineering Use Cases

1. Business Intelligence/Reporting 

Building static reports on the BI tool of choice like Tableau, Metabase, Looker which is used by a select few within the organization.


  • Constructing reports, creating charts, dashboards on the BI tool depending upon the capabilities of each.
  • Building a trusted, high-quality data storage layer upon which the reports work.


  • Static reporting to answer specific questions about the business
  • Track key performance indicators and metrics to identify business issues
  • Optimize business processes
  • Aid in business planning and decision making

2. Data Science 

Producing predictive data models to power applications or operational workflows


  • Building a raw data layer like a data lake to hold vast amounts of data in its native format including structured and unstructured data types.
  • Create sophisticated machine learning models to predict future scenarios by finding patterns in large historical data sets.


  • Customer Segmentation
  • Anomaly detection
  • Lifetime value prediction
  • Recommendation engines
  • Sentiment Analysis

3. Operational Analytics

It is a process of democratizing the data access and sharing the data back to the customer-facing teams.


  • Amalgamating data from multiple sources into a warehouse and generating insights from the same.
  • Pipe the data back from centralized warehouses to operational applications like CRM, Support tools to solve the last mile problems


  • Drive new sales opportunities
  • Prioritize customer tickets and issues
  • Lifetime value prediction
  • Granular Segmentation

4. Exploratory Analytics

This is often the beginning of a new project wherein we have multiple questions but no concrete answers or the requirements are still being finalized.


  • Exploratory Analysis tools, like notebooks and spreadsheets, are used and relied upon.
  • No concrete single data source or data models at this phase.


  • Analyze different data sets 
  • Try to identify initial data patterns
  • Understand data attribute characteristics

Data Analytics in Engineering within an Organization

Data Analytics is a collaborative process where multiple teams from an organization play different roles.

  • Data Engineering Team: They are responsible for ensuring the availability of data into a centralized data warehouse 
  • Analytics Engineers: They provide clean data sets to end users, modeling data in a way that empowers end users to answer their questions
  • Data Scientists: Apply Machine Learning Algorithms to derive value from the data. They mostly consume the Data Assets created by Analytics Engineers to create actionable insights.
  • Business Analysts: Business Analysts work closely with the business stakeholders to capture requirements for building Dashboards and reports that help in Decision Making.


This article gave a comprehensive overview of the Data Analytics in Engineering stream and also how that can be leveraged within an organization. It also highlighted the brief history of how we got here and what the roles look like in a Data team. Overall, Data Analytics in Engineering plays a pivotal role in any organization today due to the advancements in cloud technology as well as an increasing number of applications and customer digital touchpoints.

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Former Solutions Architect, Hevo Data

Jayesh is a proficient Solutions Architect who has been a key player in the dynamic landscape of real-time analytics and solution architecture. At Hevo Data, he has not only scaled global solutions teams, but has also guided customers on data engineering best practices and architectural patterns. He has broad skill set spanning Apache Pinot, AWS, GCP, and data engineering

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