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Data lineage should be an automated process and added to the “Business As Usual” operations. However, it has proven to be a task not so easily accomplished and not without its problems.
How do you know the data lineage is complete?
How do you fill in the gaps?
The team must manually confirm that the lineage is complete and address any gaps by going into the codebase and determining the proper stitching.
The Natural Lineage Processing can identify gaps where more code is expected. It can suggest likely connections by analyzing the lineage and recognizing similar paths.
Fill in the form above to let us know if you would like to learn more about how applied AI will drastically reduce labor cost, time, and improve data lineage accuracy.
Physical to Conceptual Mapping
Data lineage may be complete and accurate, but only accessible at a very technical level. The business needs to understand the data flows but cannot make sense of the large, highly-connected, and technical lineage.
A complete inventory of the physical data elements is compiled and a large effort is put together to create a complete data dictionary. An army of experts are asked to map the physical data elements to the concepts defined in the data dictionary schema.
Data properties, data statistics, and data flows feed into the Natural Lineage Processing engine which groups similar physical elements. These groups are checked with a smaller number of subject matter experts whose feedback is used to refine the groupings in a fast, iterative process.
Fill in the form above to let us know if you would like to learn more about how applied AI can improve understanding, the readability of your data and data lineage.
Data Quality Analysis and Assessment
The business needs to understand the quality of the underlying data, not just the flows. But How do you ensure that the data is of high quality?
How would your QA process catch high-quality data that is used incorrectly?
Teams of statisticians, analysts, and data scientists will crawl through databases manually or develop custom scripts to measure data properties. These processes are difficult to maintain and incomplete.
The Natural Lineage Processing toolkit can automate the data quality assessment. It includes standard statistical testing, but algorithmically determines the correct metrics based off of the inherent data properties coupled with the contextual information from the data lineage.
Fill in the form above to us know you would like to learn more about how applied AI will improve maintainability and higher quality QA.
Detecting and preventing fraud is a target for the business…. But how do you find fraud?
Subject matter experts set thresholds or develop tailored models for the specific fraud channel. Some rudimentary ML models may be developed.
More complex models are developed with little-to-no input required from subject matter experts. Data Lineage information is also fed into the models to capture more complex expressions of fraud.
Fill in the form above to us know you would like to learn more about how applied AI will improve accuracy and expand fraud sensitivity.
Data Flow Intelligence
Data Lineage is often utilized for compliance, but how can it feedback into IT, business, and data architecture decisions?
How can you use the data lineage to inform data architecture decisions?
How would you the catch data elements being pulled from multiple locations?
Data intelligence tools allows for a complete picture of the data architecture landscape which:
is fully searchable:
can be kept up-to-date easily
quantifies the data flows
However, insights about the data architecture require significant subsequent analysis by IT and business subject matter experts.
With our full Natural Lineage Processing toolkit, many common architectural insights are automated, such as:
identifying manual or QA-risk spots
finding duplicated or redundant data flows
analyzing the complexity of the data flows
Fill in the form above to let us know if you would like to learn more about how applied AI can provide IT and business architects a valuable, quantitative understanding of the landscape to help future design decisions, as well as legacy impact.