The Steepest Concern
Steep changes in data behavior are likely the most egregious and concerning changes that can happen. Owl constantly observes and monitors every cell's behavior and automatically re-trains over time. This is how Owl provides a defensible data quality program that is not dependent on rules.
Goodbye anomalous data
Outliers exist in all datasets. The challenge is finding the interesting and high value outliers. Owl's proprietary algos are fully distributed and context rich. Owl learns the usage, nature and business context of the data during its outlier processing. Owl provides you the outliers that require attention and removes the noise from the equation.
Distributed duplication detection with sophisticated fuzzy matching
Whether you are creating a single client view or simply deduping faulty records, Owl's duplicate detection can help in seconds. Written from the ground up to be fully distributed using concepts like block-indexing distance calculations Owls' duplicate detection is both fast, semantically accurate.
Increase your models and business analytics accuracy by more than 15%
What's your data cleansing and repair strategy? During the model building process a data scientist will spend his or her time trying to lower the error rate and increase the model accuracy. However the best way to increase both the quality of your business and the model accuracy is to repair the data before training the model. Owl does not edit the authoritative source of the data instead it computes a qualityScore then shows data errors with .vizErrors and finally offers cleaning solutions with .clean or simple type .autoClean and Owl will do all the steps for you with the lowest error rate.
Well Rules aren't all bad if you apply them correctly. Issues arise when an enterprise starts with rules, then quickly realize how time consuming, ineffiecient, and inaccurate rules become as the conditions start to overlap or interfe with each other. Owl takes a different approach; Data Science First and Rules Second. By layering the problem in this order we first establish a statistical baseline about the data and provide the intelligence of thousands of rules without having to write even one. We then only layer in the domain specific or complex rules that are required.
Our elite team is dedicated to one thing, making the data that flows through your organization perfect. Owl regularly works with fortune 500 companies to put a defensible data quality program in place. Many managers and data stewards lose sleep over the unknowns lurking in their data. Let our team show how to monitor and answer the unknowns for good.
Multiple time Data Science competition winner. Distributed compute expert.
Spark Analytics Engineer - over a decade of experience working with streaming and semi-structured data.
Big Data and Compute Expert - devops and infrastructure expert, certified on all major hadoop vendor platforms
Full Stack Developer – Cybersecurity certified, expert in relational data structures and developing software for business intelligence and automated analytics
Owl's services team is comprised of passionate data scientists who have spent their careers dealing with challenging data problems.
Additional Owl services include an Owl Data Steward who can assist remote or on-site and guarantee successful alerting, follow ups and repair to your data.
Use embedded graph technology to link datasets and processes together.
Visualize how a single critical column or data point can affect business processes, determine how critical a dataset is to the business.
Assess business impact from every data point. Quickly identify data quality issues that lead to downstream model output issues.
Create dynamic data quality pipelines that can blend machine learning & rules. Chain actions together to suppress false positives
Batch Load File/Table
Quality Score -> vizErrorsAssess Quality
Clean or Repair Strategy built into your businessSuppress False positives
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*All enterprises are unique and have varying data goals. The prices and services above act as a guideline.
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