It’s intriguing how data teams spend their days quantifying everything, but find it difficult to quantify their own performance. Investments, whether in budget, technology or team growth requires justification. The imperative of showcasing not just any impact, but a significant, measurable influence on the business is simply non negotiable.
To address this challenge, we’ve developed a comprehensive glossary of key performance metrics. These metrics are specifically designed to help you easily assess your data engineering team’s performance and return on investment (ROI).
Through our glossary, we hope to support you in better understanding and communicating the effectiveness of your data engineering initiatives within your organisation.
B
Business Impact score: Assesses the tangible and intangible benefits derived from data engineering efforts.
C
Catalogue usage: Tracks the usage metrics and interactions with a data catalogue, including search queries, views, downloads, and user engagement, to assess its adoption and effectiveness within an organisation.
Change deployment speed: The time taken to move changes from development to production.
Cost optimisation ratio: Represents the ratio of cost savings achieved through optimisation efforts.
Cost per processed unit of data: Calculated as the cost incurred per unit of data processed or stored.
Cost per use case: Assesses the financial investment required to implement and maintain individual use-cases within a system or project.
D
Data accessibility rate: The data accessibility rate is a measure that shows how dependable and consistent our access to information is within the organization. It is an indicator of how often we can count on getting to our data when we need it most. In simple terms, it tells us the percentage of time our data is within reach and ready for us to use.
Data accuracy: The percentage of records that match the expected values or formats.
Data catalogue coverage: This metric represents the extent to which our data assets are documented and searchable within our data catalogue or repository.
Data completeness: This term describes the proportion of records that contain all necessary information, ensuring that our datasets are thorough and comprehensive.
Data consistency: Measures the percentage of records that maintain consistent values across different systems. It shows how well data remains synchronised across various repositories.
Data engineering: Data engineering covers a sequence of tasks comprising, designing, building, and maintaining the systems and infrastructure for the collection, storage, and analysis of vast amounts of data on a large scale.
Data engineering performance metrics: Data engineering performance metrics are indicators used to evaluate the effectiveness, efficiency, and reliability of data engineering processes and workflows.
Data engineering task time: Refers to the time dedicated to fundamental data engineering activities, encompassing tasks such as data transformation, cleansing, integration, and optimisation.
Data error rate: The percentage of errors detected within the data, including missing values, duplicates and inconsistencies.
Data flow orchestration: The process that ensures that all tasks are successfully completed. It coordinates and continuously tracks data workflows to detect and fix data quality and performance issues.
Data governance maturity level: The data governance maturity level assesses how advanced an organisation’s data governance framework, policies, processes, and controls are in managing and overseeing its data assets.
Data ingestion: Data ingestion is the process of collecting, importing, and loading data from various sources into a system or storage infrastructure for further processing, analysis, or storage.
Data integration complexity: Data integration complexity quantifies the complexity of data integration workflows, taking into account factors such as the amount of data sources, the number of transformations, the mappings required, and the dependencies involved in the process.
Data latency: The time it takes to process a single record or batch of data.
Data pipeline: A data pipeline combines tools and operations that automate the movement and transformation of data from various sources to storage or processing destinations. Data pipelines can be architectured in many different ways. Mainly, there are batch-based data pipelines, real-time streaming pipelines, or a mix of both.
Data pipeline availability: The percentage of time it is operational and functional.
Data pipeline development time: Measures the duration taken to design, implement, test and deploy data pipelines.
Data pipeline efficiency: Measures the efficiency of data pipelines in terms of resource usage, throughput, and latency.
Data pipeline failure rate: This metric reflects the percentage of data pipeline runs that result in errors or failures.
Data pipeline uptime and reliability: This indicator monitors the accessibility and dependability of data pipelines.
Data product adoption rate: This measurement assesses the number or percentage of users within a given organisation who actively access and use data products.
Data quality score: This score evaluates the quality of the data through factors such as accuracy, completeness, consistency, and reliability.
Data retention rate: Evaluates the percentage of data retained and accessible over a specified period, ensuring historical data availability.
Data storage costs: Data storage costs encompass the expenditures related to storing and managing data, covering various elements such as infrastructure expenses, costs of storage systems, fees for cloud storage services, and maintenance expenses.
Data throughput: The amount of data processed per unit of time.
Data timeliness: The time difference between the data creation and the data availability.
F
FTE count: The total number of full-time equivalents (FTEs) dedicated to data engineering tasks within the team.
I
Infrastructure costs: Refers to the costs incurred for the physical and virtual infrastructure necessary to support data engineering operations.
L
Lineage completeness: Indicates the extent to which the lineage of data, including its origins, transformations, and destinations, is fully documented and understood within a data ecosystem.
M
Mean time to failure: Indicates the average time between system failures.
Mean time to recovery: Represents the average time taken to restore the system after a failure.
P
Personnel costs: Includes the expenditures related to the human resources involved in data engineering tasks.
R
Replication lag: Measures the delay or latency in replicating data across distributed databases or systems, assessing the consistency and timeliness of data synchronisation.
Resolution time: Monitors the frequency and severity of errors encountered during data processing.
Resource allocation and utilisation: Measures the effectiveness of resource allocation processes to support pipeline development and deployment, while tracking the utilisation rate of resources.
Resource availability: Assesses the availability of resources necessary for pipeline development and deployment.
Resource scalability assessment: Evaluates the ability of resources to scale up or down dynamically in response to changing requirements.
Resource utilisation cost: Represents the costs associated with resources consumption.
S
Scalability and performance: Designing systems that can handle increasing data volumes and optimising query performance.
Self-service accessibility: Examines the degree of self-service capabilities afforded to data engineering teams.
Self-service adoption rate: Tracks the adoption rate of self-service platforms among data engineering team members.
Self-service provisioning time: Measures the time taken for data engineering teams to provision, configure and manage resources autonomously through self-service platforms.
Skill proficiency: Assesses the proficiency and expertise of team members in relevant technologies.
Software costs: These are the expenses associated with the acquisition, licensing, development, customization, maintenance, and support of software tools, platforms, applications, and systems used in data engineering processes.
System downtime: The amount of time data systems are unavailable due to maintenance or unexpected issues.
T
Task duration breakdown: Involves breaking down the effective data engineering task time into specific tasks such as data transformation, data loading, data cleaning and other data engineering activities.
Technology effectiveness: Evaluates the efficiency and user satisfaction of tools used in data engineering processes.
Total cost of data engineering: Provides a holistic view of the financial investment required to establish and maintain an effective data engineering environment.
Tool usage percentage: The percentage of team members actively using specific technologies in their data engineering work.
U
User feedback score: Gathers feedback from data sources regarding the usability, accessibility and effectiveness of data for their analysis.
If your goal is to transform your data engineering team into a strategic asset, the above key performance metrics can be truly useful in helping you measure the value generated by your team and its contributions to the organisation as a whole.