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DataKitchen’s Best of 2021 DataOps Resources

DataKitchen

Before we shut the door on 2021, we would like to share our most popular DataOps content in hopes that it can help you as you learn about and implement DataOps. We hope you and your family have happy holidays and we look forward to continuing your DataOps journey with you in the new year. The DataOps Vendor Landscape, 2021.

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Start DataOps Today with ‘Lean DataOps’

DataKitchen

Data organizations don’t always have the budget or schedule required for DataOps when conceived as a top-to-bottom, enterprise-wide transformational change. An essential part of the DataOps methodology is Agile Development , which breaks development into incremental steps. In short, Lean DataOps is the fastest path to DataOps value.

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Forrester – Chart Your Course To Insights-Driven Business Maturity

DataKitchen

As organizations strive to become more data-driven, Forrester recommends 5 actions to take to move from one stage of insights-driven business maturity to another. . Intermediates: Build on your successes and work to scale your IDB capabilities across the enterprise using agile and adaptive DevOps, DataOps, and ModelOps processes. .

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Implementing a Pharma Data Mesh using DataOps

DataKitchen

Below is our fourth post (4 of 5) on combining data mesh with DataOps to foster innovation while addressing the challenges of a decentralized architecture. As generic alternatives become available, the market enters the maturity phase where cost efficiency and margins become most important. Two data sets of physicians may not match.

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What Is ‘Equity As Code,’ And How Can It Eliminate AI Bias?

DataKitchen

Machine learning (ML) models are computer programs that draw inferences from data — usually lots of data. One way to think of ML models is that they instantiate an algorithm (a decision-making procedure often involving math) in software and then, at relatively low cost, deploy it on a large scale. Addressing AI Bias With DataOps.

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Data Mesh 101: How Data Mesh Can Be Used in an Organization

Ontotext

It requires a shift in mindset, changes in team structures, and maturity that doesn’t happen overnight. How data mesh is implemented varies depending on each organization’s maturity and capabilities. Then, identify potential domains and business units that are mature and ready to adopt this paradigm.

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Data Teams and Their Types of Data Journeys

DataKitchen

This fragmented ownership model complicates data updates and results in a constant influx of erroneous data, making it exceedingly difficult to maintain data quality. DataKitchen’s DataOps Observability can deliver these Data Journeys with little to no development and few, if any, changes to production processes.