Data Observability for Data Sharing

Ensure that the Data Sharing KPIs are met

How do you ensure that the data exchanged by companies meets the criterias defined in the contract?

Define Data Quality rules on the Data Provider side to proof that you deliver what you promised. Define Data Quality rules when you are a data consumer to verify that you received valid data on time.

Shared Data Quality Rules

Shared Data Quality Rules

As a Data Provider, define Data Quality rules and ensure that you deliver the data that meets the contractual requirements.

DQO.ai Data Quality rules will be defined as a simple, easy to read text file. The Data Provider shares the file with the customer to proof that those rules are monitored. Run the Data Quality checks by processing the check in the specification file to ensure that the rules are always met.

  • Data Quality rules defined in a simple way and can be shared with the consumer
  • Data consumer and data provider can check the same set of rules, even if they store the data on different database platforms
  • Data Quality rules are ensured and monitored

External Data Verification

External Data Verification

As a Data Consumer, verify that the data received from an external party meets the quality requirements.

Define Data Quality checks for received data and monitor the Data Quality through a DQO.ai Data Observability platform. Data format changes of manually generated files will be detected through multiple types of quality checks: range checks, consistency checks.

  • Verify the quality of received data
  • Detect missing days or partitions
  • Provide valid feedback to the Data Provider (your external vendor) which quality rules were violated and which data is missing

Data received on time

Data received on time

Track the timeliness of the data. Check that the data was received within an accepted delay or according to an agreed data delivery schedule.

DQO.ai can detect both the data lag, how fresh is the data (the date of the most recent row). DQO.ai can also analyze the timeliness over the time to detect that longer delays happen more often.

  • Check the freshness of data (the lag of data delivery)
  • Create custom timeliness checks that can compare the data delay to a data delivery schedule, for example: data is received on every second Thursday of a month
  • Observe and detect inconsistencies in the data delay by comparing the data delay to the average delay

Real world accuracy checks

Real world accuracy checks

Compare the data received from an external party with other verified data source.

Accuracy Data Quality checks can compare summaries of data between the data received from the Data Provider and another source which could be your finance department. Accuracy checks are executed as Ground Truth Checks that compare aggregated summaries, grouped by business relevant dimensions (country, state, etc.).

  • Compare the data from an external party with your own data
  • Compare your data with the information from an external party, for example a market research agency
  • Detect discrepancies between your data and the external data at a business relevant grouping level

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