Data maturity: Definition, frameworks and Use cases
What is Data maturity?
Data maturity is a measurement that demonstrates the level at which a company makes the most out of their data. To achieve high data maturity, data must be integrated into all aspects of the business and used to drive decision-making and activities.
Why is Data maturity important?
Data maturity can serve as a useful tool for businesses to measure where they are along the data journey, and identify the next steps and potential challenges or roadblocks.
- 1st phase 1: gaining access to data and discovering its potential applications
- 2nd phase 2: conducting an assessment and scoring to evaluate the Data system (data quality, data infrastructure, etc.,)
- 3rd phase: guiding businesses in effectively utilizing their data resources
Framework to measure Data maturity
In this blog, explore two popular frameworks for data maturity, assessing its levels and components
Framework 1: Snowplow data maturity model (level of data maturity model)
In this framework, data maturity is involved in 5 stages as below:
Data Aware: Companies are in the early stages of defining their data strategy, relying on ad-hoc spreadsheets and tools like Google Sheets and Google Analytics for reporting. Limited resources and undefined data goals pose challenges.
Data Capable: Businesses begin to warehouse backend data and utilize an analytics platform for broader reporting and analysis. Challenges remain in data preparation, accuracy, and the current analytics platform’s limitations.
Data Adept: The data team grows, focusing on joining multiple data sources and modeling data organization-wide. Challenges arise in data governance, compliance, infrastructure, and establishing a shared data strategy.
Data Informed: Companies experience substantial data team growth and leverage data for product development. Challenges include data quality, governance, compliance, infrastructure, and optimizing the data strategy.
Pioneers: Data Pioneers harness big data for personalized experiences through real-time machine learning. Challenges revolve around skilled individuals, custom data infrastructure, and data governance and security concerns.
Framework 2: organization maturity framework (OMF) score work
Source: phData
This framework evaluates the different components of the data platform based on industry standards and criteria, using a combination of hands-on investigation and rubrics.
To establish a solid foundation for your data platform, the OMF defines four essential operational pillars:
Strategy: Aligning your data platform operations team with your core business objectives is paramount to success. The Strategy pillar ensures that your team is on the right path to drive your data transformation initiatives and achieve your organization’s goals.
Center of Excellence (CoE): Building and executing an effective operations strategy across your organization is a complex task. The CoE pillar aims to codify best practices, providing guidance on how to leverage new technologies and optimize your platform. By promoting thought leadership and facilitating automation and education, the CoE enables you to achieve operational excellence.
Core Operations: Managing a data platform involves a multitude of processes. The Core Operations pillar focuses on executing these processes seamlessly, ensuring that your platform is managed efficiently and effectively.
Team: The success of your data platform heavily relies on the skills, roles, and responsibilities of your operations team. The Team pillar defines the necessary skill sets, knowledge management practices, and personnel coverage needed to operate and scale a modern data platform successfully.
Within each pillar, the OMF employs specific criteria to assess the performance of your data platform. These criteria are categorized into three levels: “Best in class,” “Achieve,” and “Aspire.” Each level provides a clear status of your platform’s performance within the given criteria.
Data maturity applied at Amanotes
Main Pipeline
Data system at Amanotes is organized through 4 stages (Data generation, Ingestion, Transformation, Serving) and 1 backbone (Undercurrents)
Ingestion: Data is collected through partner data push or active API pulling, centralized in a Data lake on GCS, and then loaded into a cleansed and standardized Data warehouse (DWH) on BigQuery.
Transformation: Data transformed through DWH layers using BigQuery, Dataproc, and DBT for templating and metadata enrichment. Stored in BigQuery, segregated into zones with distinct concepts and semantics.
Serving: There are many use cases and perspectives to consume data from DWH: building dashboard, ad hoc query for analysis, predict and forecast, AB testing to confirm assumption and hypothesis
Data Management: Airflow orchestrates data pipelines, Datahub manages metadata and enables visualization and reporting, Cloud Logging centralizes operation logs, and Cloud Monitoring with ELK stack visualizes and monitors data and system health.
Data governance
Data governance at Amanotes is actually in early stage and we’re currently focusing on 3 key components:
Business Glossary: Establishing a standardized vocabulary for clear communication of data-related terms and concepts, with alignment among business units on the definition and use case of each term.
Data Lineage: Tracking data origin, transformation, and movement to enhance transparency and informed decision-making.
Data Stewards: Assigned custodians responsible for data integrity, privacy, and compliance.
Hereby is a demo how Amanotes hosts projects on Datahub for comprehensive oversight of every phase, facilitating streamlined workflows and informed decision-making.
Amanotes utilizes the Datahub platform to host its business glossary, providing a centralized and accessible repository for standardized data-related terms and concepts.
Data observability
Let’s explore a real-world example of data observability in action by looking at some of our demo data observability in the system.
Amanotes has developed its own data system based on a standard Proof of Concept (POC) as there are limited available tools to build a data system in the market. The data pipeline flow in Amanotes, depicted in the upper half of the chart, includes the collection and processing of data until it is uploaded to Metabase. In order to ensure data observability, Amanotes leverages various techniques such as monitoring the BigQuery audit log, Airflow log, and BigQuery schema. These techniques help ensure the data being processed is accurate, complete, and consistent.
To further customize the system, we utilize the Kabina ingest pipeline, which allows for the extraction of more insightful and actionable data.
The table indicates a significant decrease in job success rate on the 28th, implying the presence of a bug in the system. This tool allows us to drill down the root cause of the issue and conduct a thorough investigation and identify the specific jobs that are encountering errors.
More details on this topic, visit our website.
Cost management
One key practice is the implementation of tagging, which involves assigning specific labels to different data assets, enabling better categorization and tracking of costs. By utilizing tags, organizations can gain insights into resource allocation and make informed decisions about budget allocation. After that, anomaly detection techniques are employed to identify unusual spending patterns or unexpected cost spikes, allowing organizations to promptly address issues and optimize their budget utilization.
Conclusion
In summary, embracing data maturity allows businesses to gain a competitive advantage, improve customer experiences, and achieve long-term growth in a data-driven world. This blog provides a concise overview of data maturity and practical insights for its application in real-world scenarios. We hope this serves as a valuable reference to assess your organization’s data maturity and develop an effective strategy to elevate your data capabilities.
