The amount, variety and complexity of data in analytical data platforms has grown exponentially over the past several years. The latest advancements in the automation of analytics with reporting, machine learning and artificial intelligence have led to fully automated data pipelines. However, with these advances, the challenge of ensuring that the data used for business intelligence comes from the correct sources and doesn't get corrupted in the process has grown. When data is improperly sourced or corrupted, subsequent business decisions will be faulty.
While other companies focus on organizational process and governance, we concentrate on a technical approach to data governance. In our experience, we have frequently seen organizational controls fail due to a lack of culture, insufficient attention, the demand of overly complex cross-departmental orchestration, an increase in manual efforts and plain human errors. Therefore, we take a practical approach to the problem, and use targeted automation and machine learning to ensure data correctness.
Use case: Find data location by description.
Example: A data analyst needs to discover where a customer address is stored, or find what attributes the customer has.
Solution:
1. Provide a self-service portal to users.
2. Enforce a column and dataset naming convention.
3. Augment columns with searchable descriptions.
Use case: Trace data origins.
Example: A data analyst discovers a broken dataset and needs to find where the data originally came from.
Solution:
1. Provide a self-service portal to users.
2. Implement tooling that collects data modification logs.
3. Ensure that tooling is connected with all data pipeline implementation technologies.
Use case: Detect data corruption and prevent bad data from propagation.
Example: A data source format changes unexpectedly, contaminating data in the system and spoiling executive reports.
Solution:
1. Implement statistics and machine learning to detect any data corruption.
2. Alert the support team in case there are issues.
3. Prevent the propagation of corrupted data in real-time.
Easily find any data in the platform and check its current quality status.
Provide deep insight for each dataset, such as schema, change log, metrics and more.
Show where the data came from, and what other datasets were generated from it.
Provide a knowledge base for datasets and a transparent nomenclature for data rules and policies.
Detect data corruption and prevent it from spreading.
If there is corruption, the support team is notified quickly.
Get outside of the data lake and thoroughly cover all source-of-record systems.
Implement anomaly detection and automate dataset metrics analytics with ML techniques.
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