DataKitchen

JUNE 24, 2021

In May 2021 at the CDO & Data Leaders Global Summit, DataKitchen sat down with the following data leaders to learn how to use DataOps to drive agility and business value. DataOps is a Key Enabler of Business Agility. DataOps can mean different things to different organizations. Here’s a quick example.

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CIO Business Intelligence

JANUARY 21, 2025

Experienced CIOs know there is never a blank check for transformation and innovation investments, and they expect more pressure in 2025 to deliver business value from gen AI investments. Even simple use cases had exceptions requiring business process outsourcing (BPO) or internal data processing teams to manage.

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Cost-Benefit Data-driven Strategy Marketing 142

CIO Business Intelligence

DECEMBER 10, 2024

Still, CIOs have reason to drive AI capabilities and employee adoption, as only 16% of companies are reinvention ready with fully modernized data foundations and end-to-end platform integration to support automation across most business processes, according to Accenture. These reinvention-ready organizations have 2.5

Sales 143

Sales Metrics Data-driven Unstructured Data 143

DataKitchen

OCTOBER 5, 2022

This is the first post in DataKitchen’s four-part series on DataOps Observability. Observability is a methodology for providing visibility of every journey that data takes from source to customer value across every tool, environment, data store, team, and customer so that problems are detected and addressed immediately.

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DataKitchen

OCTOBER 11, 2021

A DataOps implementation project consists of three steps. First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. In DataOps, the definition of done includes more than just some working code. The final step is designing a data solution and its implementation.

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DataKitchen

SEPTEMBER 21, 2023

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream. One of the primary sources of tension? What is Data in Use?

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DataKitchen

OCTOBER 20, 2023

In today’s data-driven landscape, Data and Analytics Teams i ncreasingly face a unique set of challenges presented by Demanding Data Consumers who require a personalized level of Data Observability. Data Observability platforms often need to deliver this level of customization.

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DataKitchen

JULY 3, 2023

Data Journey First DataOps Putting Problems in Your Data Estate at the Forefront Welcome to the high-octane world of DataOps, a powerhouse that turbocharges data analytics development and management. Historically, automation has taken center stage in the theater of DataOps.

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DataKitchen

MAY 10, 2021

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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DataKitchen

DECEMBER 9, 2022

Query> DataOps. ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. The goal of DataOps is to help organizations make better use of their data to drive business decisions and improve outcomes.

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DataKitchen

JANUARY 2, 2021

This is similar to findings in a joint Eckerson-DataKitchen DataOps survey. In this report, Gartner outlines recommendations to effectively operationalize AI solutions that involve the core management competencies of ModelOps, DataOps, and DevOps. Figure 1: Operational AI Requires ModelOps, DataOps, and DevOps Practices.

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DataKitchen

NOVEMBER 28, 2022

This is the final post in DataKitchen’s four-part series on DataOps Observability. Observability is a methodology for providing visibility of every journey that data takes from source to customer value across every tool, environment, data store, team, and customer so that problems are detected and addressed immediately.

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DataKitchen

DECEMBER 6, 2022

We are excited that Gartner released its ‘Market Guide to DataOps’ ! The two things we are most excited about are: First, DataOps is distinct from all Data Analytic tools. How is DevOps different than DataOps ? DataOps Observability. We see teams do amazing things with our software.

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DataKitchen

FEBRUARY 21, 2023

By Robert Thanaraj , Ehtisham Zaidi , and 2 more. Gartner suggests in the article that successful Data Engineering teams have two crucial challenges. How to optimize Data Team Productivity – essentially that teams should avoid adding more bodies whenever they have more work that needs to be done.

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DataKitchen

OCTOBER 5, 2022

This is the first post in DataKitchen’s four-part series on DataOps Observability. Observability is a methodology for providing visibility of every journey that data takes from source to customer value across every tool, environment, data store, team, and customer so that problems are detected and addressed immediately.

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Testing Risk Data Processing Statistics 130

DataKitchen

NOVEMBER 18, 2022

This is the third post in DataKitchen’s four-part series on DataOps Observability. Observability is a methodology for providing visibility of every journey that data takes from source to customer value across every tool, environment, data store, team, and customer so that problems are detected and addressed immediately.

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DataKitchen

JANUARY 17, 2022

A large pharmaceutical Business Analytics (BA) team struggled to provide timely analytical insight to its business customers. However, the BA team spent most of its time overcoming error-prone data and managing fragile and unreliable analytics pipelines. . The Solution.

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DataKitchen

MARCH 8, 2023

These three objectives are interconnected and essential to the success of any data team. Achieving these objectives requires a well-coordinated effort from the team with a clear understanding of the goals and objectives. The hard part is doing them all at once. That is the single biggest challenge.

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DataKitchen

APRIL 18, 2024

Introducing DataKitchen’s Open Source Data Observability Software Today, we announce that we have open-sourced two complete, feature-rich products that solve the data observability problem: DataOps Observervability and DataOps TestGen. What is DataOps Observability? That’s what DataOps Observability promises.

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DataKitchen

MARCH 16, 2023

How to measure your data analytics team? So it’s Monday, and you lead a data analytics team of perhaps 30 people. But wait, she asks you for your team metrics. Like most leaders of data analytic teams, you have been doing very little to quantify your team’s success. Introduction.

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DataKitchen

JULY 27, 2023

Azure Databricks Workflows : An Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. Azure Databricks Workflows : An Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. You can use it for big data analytics and machine learning workloads.

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DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Mastering Data Production (#3) Introduction Managing the production phase of data analytics is a daunting challenge. Is the business logic producing correct outcomes? Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs.

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DataKitchen

MAY 10, 2024

The Critical Need for Data Evaluation Adding new data sets to production environments without proper evaluation can lead to significant issues, such as data corruption, analytics based on faulty data, and decisions that may harm the business. Type Validation: Confirming data types are consistent and appropriate for each column.

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CIO Business Intelligence

SEPTEMBER 17, 2024

On the other hand, enterprise architects often struggle to deliver business outcomes, and many CIOs find themselves communicating the function’s importance to executive stakeholders repeatedly. What agile leadership looks like Redefining how architects collaborate with agile teams is one way to improve business-IT collaboration and outcomes.

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CIO Business Intelligence

AUGUST 19, 2024

This means CIOs are increasingly viewed as responsible not only for traditional IT roles such as managing IT departments and infrastructure but also driving innovation—the kind that fuels business growth (80% of business executives agree, according to a Deloitte survey ). GenAI is one of those innovations. AI, of course, isn’t new.

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CIO Business Intelligence

OCTOBER 13, 2023

Data architect role Data architects are senior visionaries who translate business requirements into technology requirements and define data standards and principles, often in support of data or digital transformations. Data architects are frequently part of a data science team and tasked with leading data system projects.

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DataKitchen

APRIL 11, 2023

He drew from his twenty-five years of experience in business analytics, pharmaceutical brand launch strategy, and project management. James emphasized the importance of delivering value quickly, even if the solution is only 70% complete, rather than aiming for 100% completeness at the cost of significant delays.

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DataKitchen

OCTOBER 2, 2023

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. Journeys provide a context for understanding and observing complex elements across and down a data analytic system.

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AWS Big Data

NOVEMBER 9, 2023

Building a data platform involves various approaches, each with its unique blend of complexities and solutions. It does this by helping teams handle the T in ETL (extract, transform, and load) processes. Use case The Enterprise Data Analytics group of a large jewelry retailer embarked on their cloud journey with AWS in 2021.

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Alation

AUGUST 3, 2021

DataOps and DevOps are two distinctly different pursuits. But where DevOps focuses on product development, DataOps aims to reduce the time from data need to data success. At its best, DataOps shortens the cycle time for analytics and aligns with business goals. What is DataOps? What is DevOps?

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Testing Manufacturing Software Statistics 59

AWS Big Data

MARCH 13, 2023

AnyCompany has a DataOps team that has been operating self-managed Kafka clusters in their data centers for years. AnyCompany wants to continue using the DataOps team to manage the MSK cluster on behalf of the development team. They must decide between provisioned or serverless cluster types.

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AWS Big Data

AUGUST 11, 2023

Customers are implementing data and analytics workloads in the AWS Cloud to optimize cost. Both options minimize the undifferentiated heavy lifting activities like managing servers, performing upgrades, and deploying security patches and allow you to focus on what is important: meeting core business objectives.

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AWS Big Data

AUGUST 6, 2024

The Amazon Global Transportation Technology Services (GTTS) team owns a set of products called INSITE (Insights Into Transportation Everywhere). These products are user-facing applications that solve specific business problems across different transportation domains: network topology management, capacity management, and network monitoring.

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Alation

MARCH 22, 2022

DataOps has emerged as an exciting solution. As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. As pressures to modernize mount, the promise of DataOps has attracted attention. People want to know how to implement DataOps successfully.

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Alation

SEPTEMBER 28, 2022

What exactly is DataOps ? The term has been used a lot more of late, especially in the data analytics industry, as we’ve seen it expand over the past few years to keep pace with new regulations, like the GDPR and CCPA. In essence, DataOps is a practice that helps organizations manage and govern data more effectively.

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Cost-Benefit Data Quality Manufacturing Testing 52

Cloudera

JULY 26, 2021

Business and Technology Forces Shaping Data Product Development. A growing desire for self-service analytics among internal data consumers and knowledge workers, external partners and clients. hybrid or public, multi-cloud) and advanced analytical frameworks (e.g., PoS and transaction data to optimize supply chain operations).

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Strategy Data Science Unstructured Data Marketing 119

Cloudera

SEPTEMBER 15, 2022

The gathering in 2022 marked the sixteenth year for top data and analytics professionals to come to the MIT campus to explore current and future trends. I spoke to Mark Ramsey of Ramsey International to unpack what this all means and how it can help teams create an architecture that delivers business use cases faster.

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