DataKitchen

FEBRUARY 1, 2021

Every DataOps initiative starts with a pilot project. DataOps addresses a broad set of use cases because it applies workflow process automation to the end-to-end data-analytics lifecycle. DataOps reduces errors, shortens cycle time, eliminates unplanned work, increases innovation, improves teamwork, and more.

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Business Objectives Cost-Benefit Measurement Metrics 245

DataKitchen

MAY 10, 2021

If you have been in the data profession for any length of time, you probably know what it means to face a mob of stakeholders who are angry about inaccurate or late analytics. Data Observability Component of DataOps. Observability is a characteristic of your system. We see data observability as a component of DataOps.

Testing 214

Testing Manufacturing Data Quality Statistics 214

DataKitchen

AUGUST 9, 2021

Below is our third post (3 of 5) on combining data mesh with DataOps to foster greater innovation while addressing the challenges of a decentralized architecture. We’ve talked about data mesh in organizational terms (see our first post, “ What is a Data Mesh? ”) and how team structure supports agility. DataOps Meta-Orchestration.

Testing 246

Testing Data Lake Metadata Publishing 246

DataKitchen

APRIL 26, 2021

In early April 2021, DataKItchen sat down with Jonathan Hodges, VP Data Management & Analytics, at Workiva ; Chuck Smith, VP of R&D Data Strategy at GlaxoSmithKline (GSK) ; and Chris Bergh, CEO and Head Chef at DataKitchen, to find out about their enterprise DataOps transformation journey, including key successes and lessons learned.

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Measurement Metrics Data-driven Dashboards 246

DataKitchen

SEPTEMBER 20, 2021

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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Testing Metrics Measurement Dashboards 246

DataKitchen

NOVEMBER 18, 2021

For see the entire results of the data engineering survey, please visit “ 2021 Data Engineering Survey: Burned-Out Data Engineers are Calling for DataOps.”. In addition, only one-third of companies have an established CDO role, and the average tenure of the CDO is only 2.5 Data engineers end up fixing the same problem over and over.

Testing 246

Testing Data Governance Measurement Software 246

DataKitchen

MAY 2, 2025

More often, it’s baked into team culture, shaped by tools that don’t make quality visible, roles that lack ownership, and a lack of shared language to even talk about what’s broken. Ask your team what it means to take ownership of data quality. Is Your Team in Denial of Data Quality? Is your team ready? Interested?

Data Quality 124

Data Quality Snapshot Testing Data-driven 124

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.

Testing 169

Testing Risk Data Processing Statistics 169

DataKitchen

JANUARY 10, 2022

Today, DataKitchen announced the release of the latest book in its groundbreaking DataOps series, Recipes for DataOps Success: The Complete Guide to An Enterprise DataOps Transformation. For example, how do you build support for DataOps? What is the best first project? And much more. About DataKitchen.

Enterprise 169

Enterprise Data Analytics Data Strategy Modeling 169

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. What is Data in Place?

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

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.

Testing 130

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DataKitchen

AUGUST 16, 2024

2024 Gartner Market Guide To DataOps We at DataKitchen are thrilled to see the publication of the Gartner Market Guide to DataOps, a milestone in the evolution of this critical software category. DataOps is at the intersection of many different product categories.

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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.

Machine Learning 130

Machine Learning Data-driven Optimization Data Analytics 130

CIO Business Intelligence

DECEMBER 10, 2024

Set clear, measurable metrics around what you want to improve with generative AI, including the pain points and the opportunities, says Shaown Nandi, director of technology at AWS. Zia, Zohos sales assistant, predicts deal-win probability, recommends who should sell what products, and improves customer communications.

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Sales Metrics Data-driven Unstructured Data 143

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.

Testing 130

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DataKitchen

DECEMBER 6, 2022

We are excited that Gartner released its ‘Market Guide to DataOps’ ! The document they wrote is exceptionally close to what we see in the market and what our products do ! This document is essential because buyers look to Gartner for advice on what to do and how to buy IT software. What is missing?

Software 130

Software Marketing Data Lake Testing 130

DataKitchen

OCTOBER 25, 2022

This is the second 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.

Testing 130

Testing Data-driven Visualization Dashboards 130

CIO Business Intelligence

JANUARY 21, 2025

What to bet on : Expect significant agentic AI hype in 2025 on one end and potential employee fears around autonomous agents taking their jobs on the other. What to bet on : Look for scalable departmental opportunities with complex business rules embedded in document processing and a mix of no-code, low-code, RPA, and BPO solutions in place.

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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. Data Teams Already Have A Ton of Things To Get Done.

Testing 130

Testing Risk Data-driven Statistics 130

DataKitchen

FEBRUARY 21, 2023

What is Gartner’s advice for a new data engineering lead like Marcus? Adopt DataOps Practices : “Successful data engineering teams are cross-functional and adopt DataOps practices.” Data team morale is consistent with DataKitchen’s own research. It’s not been going well. A better ETL tool? Pick some other hot tool?

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DataKitchen

AUGUST 24, 2023

Since the first edition of the DataOps Cookbook in 2019, we have talked with thousands of companies about their struggles to deliver data-driven insight to their customers. And on top of that, they are all unhappy, stressed, and wondering what went wrong. In many ways, they all have the same problems.

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DataKitchen

OCTOBER 27, 2022

. Question: What is something the data industry is missing? I think it’s observability-led DataOps. The first step in solving that pain is to observe what’s happening with your data and analytics ‘estate’ and stick little thermometers at various points in the process and measure.

Dashboards 130

Dashboards Measurement Testing Modeling 130

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? What is DataOps TestGen?

Testing 117

Testing Data Quality Enterprise Data Analytics 117

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.

Testing 130

Testing Risk Data Processing Statistics 130

DataKitchen

JANUARY 5, 2021

The disparate toolchains illustrate how each group resides in its own segregated silo without an ability to easily understand what other groups are doing. In the data analytics market, this endeavor is called DataOps. Observability – Testing inputs, outputs, and business logic at each stage of the data analytics pipeline.

Testing 147

Testing Data Governance Metadata Visualization 147

DataKitchen

DECEMBER 1, 2022

Well, don’t worry because DataOps is here to help! In this post, we’ll discuss how DataOps Observability and Automation can relieve team stress and show you how to get started. All of those things can be enabled by DataOps. With DataOps, data teams can ship data analytics systems faster and more confidently.

Experimentation 130

Experimentation Data-driven Optimization Risk 130

CIO Business Intelligence

DECEMBER 22, 2022

What is DataOps? DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. DataOps goals According to Dataversity , the goal of DataOps is to streamline the design, development, and maintenance of applications based on data and data analytics.

Analytics 130

Analytics Machine Learning Data mining Advertising 130

DataKitchen

JUNE 16, 2021

Today, DataKitchen announced the release of the latest book in its groundbreaking DataOps series, Recipes for DataOps Success: The Complete Guide to An Enterprise DataOps Transformation. For example, how do you build support for DataOps? What is the best first project? And much more. About DataKitchen.

Enterprise 130

Enterprise Data Analytics Data Strategy Modeling 130

DataKitchen

DECEMBER 9, 2022

DataKitchen, the leading provider of DataOps solutions, has been named a Representative and “super cool, way out there, OP, world best” DataOps vendor in the December 2022 Gartner® Market Guide for DataOps Tools. What gives?” December 08, 2022, 08:00 ET | Source: DataKitchen.

Reporting 130

Reporting Marketing Software Testing 130

DataKitchen

JULY 12, 2023

While people can do what they want with language (and many often do), syntax helps ordinary language users understand how to organize words to make the most sense. What is a meaningful test for your business? What is the acceptable rate of change? In linguistics, Syntax is the study of sentence structure and grammar rules.

Data Quality 130

Data Quality Testing Manufacturing Finance 130

DataKitchen

MARCH 8, 2023

Data Observability must be a top priority, and the team must have robust processes to ensure the data is correct and free from corruption. DataOps Observability is an essential practice in modern data-driven organizations that ensures real-time insights into the manufacturing process. That is the single biggest challenge.

Analytics 130

Analytics Manufacturing Data-driven Data Analytics 130

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Ensuring Data Quality in New Data Sources (#1) Introduction to Data Evaluation in Data Observability Ensuring their quality and integrity before incorporating new data sources into production is paramount. And how can a data engineer give their provider a ‘score’ on the data based on fact?

Data Quality 124

Data Quality Interactive Dashboards Testing 124

DataKitchen

SEPTEMBER 9, 2024

What do you do when you have thousands of data pipelines in production? How do you understand what is happening to those pipelines? Is there a way that you can visualize what is happening in production quickly and easily? That is useful if you want to focus on what is slow. What is an effective way to see the trend?

Visualization 161

Visualization Enterprise IT 161

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. The Third of Five Use Cases in Data Observability Data Evaluation: This involves evaluating and cleansing new datasets before being added to production.

Metrics 124

Metrics Testing Data Quality Dashboards 124

DataKitchen

FEBRUARY 2, 2023

We’ve read many predictions for 2023 in the data field: they cover excellent topics like data mesh, observability, governance, lakehouses, LLMs, etc. What will the world of data tools be like at the end of 2025? What will exist at the end of 2025? Recession: the party is over. Can we do this cheaper?

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Metadata Testing Data Science Risk 130

DataKitchen

NOVEMBER 10, 2023

Adopt an iterative approach, characteristic of DataOps and Agile methodologies, to continuously improve data processes and systems. Solutions to Reign in the Chaos Implementing Data Observability Platforms: Tools like DataKitchen’s DataOps Observability provide an overarching view of the entire Data Journey.

Data Quality 136

Data Quality Testing Data Lake Data Integration 136

DataKitchen

MARCH 12, 2024

Key Success Metrics, Benefits, and Results for Data Observability Using DataKitchen Software Lowering Serious Production Errors Key Benefit Errors in production can come from many sources – poor data, problems in the production process, being late, or infrastructure problems. That was amazing for the team.” Databricks was all green.

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Metrics Software Cost-Benefit Testing 117