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?

Testing 176

Testing Data Quality Predictive Modeling Metrics 176

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.

Insurance 176

Insurance Metadata Data-driven Data Quality 176

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 176

Testing Risk Data Processing Statistics 176

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 Modeling 130

DataKitchen

APRIL 28, 2021

Industry analysts who follow the data and analytics industry tell DataKitchen that they are receiving inquiries about “data fabrics” from enterprise clients on a near-daily basis. Forrester relates that out of 25,000 reports published by the firm last year, the report on data fabrics and DataOps ranked in the top ten for downloads in 2020.

Statistics 246

Statistics Optimization Technology Data Analytics 246

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.

Data-driven 130

Data-driven Testing Risk Cost-Benefit 130

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.

Software 130

Software Marketing Data Lake Testing 130

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

Testing Reporting Dashboards Visualization 130

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.

Analytics 130

Analytics Manufacturing Data-driven Data Analytics 130

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.

Testing 157

Testing Metadata Dashboards Statistics 157

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.

ROI 211

ROI Metrics Measurement Cost-Benefit 211

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

Testing Statistics Measurement Metrics 130

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

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. .

Data-driven 130

Data-driven Data Architecture Machine Learning Strategy 130

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.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

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.

Metrics 130

Metrics Data Analytics Analytics Measurement 130

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.

Experimentation 130

Experimentation Data Lake Marketing Predictive Modeling 130

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.

Testing 130

Testing Machine Learning Reporting Modeling 130

DataKitchen

JANUARY 23, 2023

What about DataOps Observability? A five-on-five comparison of data lineage vs. DataOps Observability Five on data lineage: Data lineage traces data’s origin, history, and movement through various processing, storage, and analysis stages. Data lineage can also be used for compliance, auditing, and data governance purposes.

Testing 130

Testing Data Governance Data Quality Data-driven 130

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.

Data Quality 100

Data Quality Testing Uncertainty Data Enablement 100

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.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

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.

Data Analytics 130

Data Analytics Analytics Cost-Benefit Data Quality 130

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.

Metadata 52

Metadata Cost-Benefit Data Quality Data Lake 52

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?

Testing 59

Testing Manufacturing Software Statistics 59

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.

Data Architecture 127

Data Architecture Data Warehouse Statistics Visualization 127

Octopai

OCTOBER 26, 2022

They’re managing the data; they’re governing the data; they’re so busy being on top of their data that they’re not actually using their data. Enter DataOps. How can DataOps help here? DataOps realizes that the purpose of having data is to use the data. So what is DataOps ? Six DataOps best practices.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

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.

Data Processing 95

Data Processing Cost-Benefit Optimization Interactive 95

DataKitchen

AUGUST 8, 2023

It alerts data and analytics leaders to issues with their data before they multiply. Finally, the article states that Data Observability can bring tremendous benefits and time savings by helping IT and business teams be more proactive in their data management and engineering activities. Let’s start with a scenario.

Data Quality 130

Data Quality Testing Snapshot Reporting 130

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.

Data Warehouse 103

Data Warehouse Testing Data Quality Reporting 103

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.

Optimization 76

Optimization Management Metrics Metadata 76

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.

Data Lake 79

Data Lake Data Architecture Data-driven Data Warehouse 79

Octopai

OCTOBER 31, 2022

They expect data analytics products to be self-serve, easy to find and deliver accurate and reliable results that can power smart business decisions. As a data analyst or engineer, you can make a significantly more direct impact on your company’s business success. Data consumers are no different. In a way, it’s exciting.

Experimentation 52

Experimentation Machine Learning Manufacturing Management 52

Jet Global

JULY 27, 2022

As Business Intelligence (BI) tools, data warehousing solutions, and enterprise data and application landscapes have advanced, it’s worth taking the time to rethink that old model, starting with the dichotomy between operational reporting (OR) and strategic analytics. Data Rich and Insight Poor. Download Now.

Operational Reporting 52

Operational Reporting Reporting Analytics Dashboards 52

AWS Big Data

FEBRUARY 1, 2023

Every day, Amazon devices process and analyze billions of transactions from global shipping, inventory, capacity, supply, sales, marketing, producers, and customer service teams. Storage and redundancy – Due to the heterogeneous data stores and models, it was challenging to store the different datasets from various business stakeholder teams.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. This, in turn, helps them to build new data pipelines, solutions, and products, or clean up the data that’s there. What is data profiling?

IT 52

IT Data Quality Metadata Data Governance 52

Alation

MAY 24, 2022

Downstream, it gives analysts confidence to build and share reports that drive the business. Business users must exit their workflows to verify data integrity – wasting time and resources and diminishing trust. Data quality is one of the primary signals behind whether or not a data asset or analytical report can be trusted.

Data Quality 52

Data Quality Metadata Reporting Metrics 52

Andrew White

JANUARY 11, 2021

It was titled, The Gartner 2021 Leadership Vision for Data & Analytics Leaders. This was for the Chief Data Officer, or head of data and analytics. What is unique about the D&A Leadership Vision is that it crossed over into business since for many organizations, the CDO reports into the CEO or COO (as examples).

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52