Published on

November 24, 2023


KPIs for Data Analytics, Data Science & Engineering Teams in 2023

Discover the crucial KPIs for data analytics, data science, and data engineering teams. Then, generate meaningful reports to communicate them easily.
Julia Dunlea
VP of Marketing

Evaluating your data team's performance isn't just a box to tick; it's the cornerstone of making intelligent, data-led business choices. The secret sauce? Picking KPIs that actually matter.

This post will dissect the most crucial KPIs for data analytics, data science, and data engineering teams. It will demonstrate how to align these metrics with your business objectives, and offer tips to ace data visualization and foster a culture obsessed with data-driven decision-making.

Key Takeaways

  • KPIs are essential for measuring the performance of data teams, including insights generated per month, accuracy of predictions and speed of generating results.
  • Data visualizations can be used to track KPIs effectively. By utilizing the right visualization tools, such as Akkio or PowerBI, and creating actionable KPI dashboards, we can make tracking and interpreting data more efficient and intuitive.
  • Building a data-driven culture is critical for maximizing the impact of KPIs through promoting data literacy and encouraging decision making based on accurate information.

KPIs for Data Analytics Teams

a team sitting to discuss key performance indicators for data analytics

Data analytics teams generate insights for business growth. Tracking primary performance indicators including the following is crucial to their success.

  • Insights generated per month
  • Accuracy of predictions
  • Speed of generating results
  • Ease of data access

For Example, imagine a data analytics team working in a healthcare organization. Their job is to focus on the "Accuracy of Predictions" metric for their patient readmission risk prediction model.

Should the model's accuracy dip below 90%, the data team will need to take action by retraining the model with recent patient data or exploring different predictive modeling techniques. By closely monitoring this metric, the data team can ensure they provide the most accurate predictions possible, which in turn helps improve patient care.

Insights Generated per Month

The number of insights generated per month is a crucial KPI to measure the effectiveness of data analytics teams. It provides actionable information on productivity and efficiency and helps organizations identify areas for improvement and focus on generating impactful insights.

Accuracy of Predictions

Accurate predictions are vital for data analytics teams providing reliable insights. Metrics like forecast error, MAPE, and total error can assess prediction accuracy. Monitoring accuracy helps organizations align data analysis with strategic objectives, ensuring predictions contribute to goal attainment.

Speed of Generating Results

Analytics teams must generate results quickly to add value in fast-paced business environments. The velocity of generating results KPI reflects data team efficiency and effectiveness in analyzing data. Organizations can track this KPI to fine-tune analytics processes and ensure timely insights that propel business growth.

Ease of Getting Data

Data accessibility is a critical factor in the success of data analytics teams. It is measured using the data accessibility KPI, which assesses the effectiveness and availability of collecting more data for analysis. This evaluation enables organizations to enhance their data management processes and systems, thereby empowering users to make informed decisions based on accurate and up-to-date information.

KPIs Data Science Teams

KPIs for Data Science teams - cover image

Data science teams drive innovation and growth within organizations. Each KPI is a crucial metric that gauges success and optimizes processes. Effective KPI tracking enables teams to deliver valuable insights.

  1. Models developed per month
  2. Accuracy of models developed
  3. Time-to-insight
  4. Data quality score

These metrics help organizations evaluate their data science teams’ effectiveness and ensure that their models contribute to achieving their strategic objectives and business goals.

Models Developed per Month

To evaluate the productivity and efficiency of data science teams in developing new models, tracking the number of models built per month is a crucial key performance indicator. By monitoring this key performance indicator, organizations can identify areas where their team's productivity and efficacy can be optimized while also gaining valuable insights into their progress.

Establishing specific objectives, tracking progress over time, and utilizing data visualizations are recommended practices for monitoring the models developed per month KPI effectively.

Accuracy of Models Developed

Monitoring model accuracy is crucial for effective problem-solving. The ratio of correct predictions to total predictions is the primary measure of model accuracy. Other metrics, such as precision, recall, and the confusion matrix, should also be considered for a comprehensive evaluation of model performance.


Measuring the time-to-insight is a crucial key performance indicator for data science teams, as it determines how long it takes to generate meaningful insights from data. This KPI evaluation helps organizations assess the efficiency of their data science teams and identify potential growth areas.

Monitoring time-to-insight helps organizations ensure that their data science teams provide valuable insights promptly. This enables informed decision-making, propels business growth, and avoids delays.

Data Quality Score

To obtain more accurate insights, data science teams require more data. It is crucial to measure data quality using a quantifiable measure to ensure that the acquired data is reliable. This is where a data quality score KPI comes into play, taking into consideration various factors to assess the quality of data.

  • Completeness
  • Uniqueness
  • Freshness
  • Validity
  • Accuracy
  • Consistency

Evaluating the data quality score allows organizations to verify that their data science teams are working with top-tier quality data, leading to more precise insights, improved decision-making, and, ultimately, enhanced new customer satisfaction.

KPIs for Data Engineering Teams

Data Engineering cover, a man walking in a datacenter

Data engineering teams play a vital role in enabling data-driven decision-making within organizations by building and maintaining systems and data pipelines. To measure the effectiveness of these teams, key performance indicators include uptime of systems and data pipelines, successful delivery of changes and new features, as well as efficiency in data processing and storage.

Tracking these KPIs allows organizations to evaluate the reliability and performance of their data engineering teams, ensuring that they deliver the necessary infrastructure to support data-driven decision-making.

Uptime for Systems and Data Pipelines

The "uptime" KPI is a measure of the percentage of time that systems and data pipelines are available and operational. It is a key indicator of the reliability and performance of the data engineering team. Monitoring this KPI is essential to ensure that the data infrastructure is reliable and to prevent potential disruptions.

To effectively track and maintain optimal uptime, organizations can set up alerts for when uptime falls below a certain threshold, test the system regularly, and use automated tools to monitor the system.

New Features and Changes Delivered

Tracking the number of new features and changes delivered by the data team is crucial to measuring their productivity and pipeline improvement. It's a key performance indicator that allows organizations to evaluate their team's progress and performance in providing updates and enhancements to their data infrastructure.

Monitoring the delivery of new features and changes helps analyze areas for improvement and ensures that the data engineering team continues to innovate and drive the business forward.

Data Processing and Storage Efficiency

Efficient data processing and storage are critical KPIs for data engineering teams. By monitoring these metrics, organizations can optimize processes, cut costs, and ensure proficiency.

Increase efficiency by automating data processing and storage, compressing data, and utilizing cloud-based storage solutions.

Aligning KPIs with Business Goals

To ensure KPIs have the greatest business impact, they must align with the organization's business goals. Relevant metrics must be identified, communicated effectively across teams, and strategies adjusted if the KPIs do not deliver actionable insights for decision-making.

Aligning KPIs with business goals enables organizations to boost performance, concentrate on the correct KPIs, and attain their objectives.

Identifying Relevant KPIs

Choosing the right Key Performance Indicators (KPIs) is paramount in aligning them with business goals and ensuring they provide useful insights. To identify effective KPIs, it is essential to first understand the organization's overall objectives and then use them to guide the selection of appropriate KPIs.

Additionally, analyzing the data and determining which data points are essential for the organization's success is crucial. By identifying and selecting the most pertinent KPIs, organizations can ensure that their data teams are focusing on the right metrics and contributing to the achievement of their goals.

Communicating KPIs Across Teams

Clear and effective communication of KPIs across teams is essential to promote a shared vision and drive performance. Ensuring that KPIs To achieve desired results, it is important that all team members clearly understand and take ownership of Key Performance Indicators (KPIs). Regular meetings, email updates, and data visualizations can help to effectively communicate KPIs and their significance.

Leveraging data visualizations is a highly effective way to track Key Performance Indicators (KPIs). They play a crucial role in not only effectively tracking but also communicating KPIs, as they offer a straightforward and succinct means of observing key performance indicators data analytics and metrics.

Choosing the Right Visualization Tools

Histogram and Bar Graph showing visits per day
Example visualization by Akkio

Selecting the right visualization tools for KPI tracking is essential, as it facilitates the effective and succinct representation of KPIs, thus facilitating easier comprehension and interpretation of the data. A variety of visualization tools are available, such as:

  • Bar charts: A bar chart can be used to compare the number of new features delivered by the data engineering team each month, allowing for easy comparison and tracking of productivity over time.
  • Line graphs: A line graph can be used to display the uptime for systems and data pipelines over a period of time. This helps in visualizing the reliability and performance of the data engineering team.
  • Pie charts: A pie chart can be used to illustrate the proportion of accurate predictions made by the data analytics team. This can help in understanding the reliability of their predictions.
  • Scatter plots: A scatter plot can be used to show the relationship between the number of models developed per month and their accuracy, helping the data science team to understand and improve their performance.
  • Heat maps: A heat map can be used to visualize the data quality score across various parameters like completeness, uniqueness, freshness, validity, accuracy, and consistency. This can help the data science team to understand and improve the quality of their data.

Tools like Akkio, Klipfolio, Geckoboard, Databox, Cascade, ClickUp, or Scoro are a few popular tools for tracking KPIs.

Effective communication and decision-making process in organizations are facilitated by clear and concise representation of KPIs through suitable visualization tools.

Best Practices for Designing KPI Dashboards

Generative Reports in Akkio

Designing effective KPI dashboards is an art. It requires careful thought and planning to ensure that the dashboards are not only visually appealing but also provide meaningful insights in an easy-to-understand manner. Here are some best practices to consider when designing KPI dashboards:

  1. Keep it Simple: A cluttered dashboard can be confusing. Stick to a clean, straightforward layout that makes it easy for users to find the information they need.
  2. Limit the Number of KPIs: Showing too many KPIs at once can overwhelm users and dilute the impact of the most important metrics. Limit the number of KPIs displayed to keep the focus on key insights.
  3. Use Visual Cues: Colors, sizes, and shapes can all be used to draw attention to significant data. Use them strategically to highlight the most important information.
  4. Organize Your Data: Group related data together and arrange it logically to make it easier to understand. This could mean organizing by category, time period, or any other method that makes sense for your data.
  5. Make it Actionable: Every KPI on your dashboard should provide actionable insights. If a metric isn't helping users make better decisions, it probably doesn't need to be on your dashboard.

These best practices are designed to help organizations create KPI dashboards that effectively communicate performance metrics, making it easier for teams to understand their performance and make data-driven decisions.

Building a Data-Driven Culture to Maximize KPI Impact

A data-driven culture is essential for organizations to fully leverage the power of KPIs and make informed decisions. Promoting data literacy and encouraging data-driven decision-making are key factors in building such a culture.

This part discusses the significance of nurturing data literacy and promoting a data-driven culture by inspiring team members to base their decisions on data and insights derived from KPIs.

Promoting Data Literacy

Data literacy is the ability to:

  • Understand, interpret, and use data effectively, often a skill honed by a data analyst
  • Develop data-handling skills
  • Develop data visualization skills
  • Gain knowledge of data sources

Fostering a culture of data literacy within an organization involves providing training and resources to help team members develop these skills and knowledge.

Cultivating data literacy empowers teams in organizations to make informed decisions rooted in accurate and current information, thereby driving business success and growth.

Encouraging Data-Driven Decision Making

Supporting a data-driven culture involves encouraging team members to base their decisions on data and insights derived from KPIs. This can be achieved by granting access to data and insights, fostering a culture of data-driven decision making, and recognizing data-driven decisions.

Encouraging data-driven decision making ensures that teams in organizations work with the most effective and accurate information, leading to more informed decisions and propelling the business forward with a positive business impact. By utilizing this approach, teams can make better decisions that contribute to the overall success of the organization.


Effective tracking and leveraging of Key Performance Indicators (KPIs) is crucial for organizations to drive performance, make informed decisions, and achieve their goals. By identifying effective KPIs for data analytics, data science, and data engineering teams, aligning them with business objectives, and using data visualizations for efficient KPI tracking, organizations can maximize the impact of their KPIs. Moreover, creating a data-driven culture by promoting data literacy and encouraging data-driven decision-making ensures that KPIs are fully leveraged to drive business success.

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