Precisely

MARCH 14, 2023

And the desire to leverage those technologies for analytics, machine learning, or business intelligence (BI) has grown exponentially as well. We optimize these products for use cases and architectures that will remain business-critical for years to come. What does all this mean for your business? Bigger, better results.

Data Integration 52

Data Integration High Quality Data BI Data 52

DataKitchen

SEPTEMBER 21, 2023

This proactive approach to data quality guarantees that downstream analytics and business decisions are based on reliable, high-quality data, thereby mitigating the risks associated with poor data quality. There are multiple locations where problems can happen in a data and analytic system.

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

Ascend.io

JUNE 8, 2023

Is it possible to treat data not just as a necessary operational output, but as a product that holds immense strategic value? Treating data as a product is more than a concept; it’s a paradigm shift that can significantly elevate the value that business intelligence and data-centric decision-making have on the business.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Monte Carlo

JULY 5, 2023

while overlooking or failing to understand what it really takes to make their tools — and, ultimately, their data initiatives — successful. When it comes to driving impact with your data, you first need to understand and manage that data’s quality. learn when and why data may be down.

High Quality Data 52

High Quality Data Data Software Engineer Software Engineering 52

Monte Carlo

MAY 23, 2023

The ACE comprises all types of data contributors, from analytics engineers to data engineers to business intelligence analysts, who collaborate to help the business make more strategic decisions using data. The platform has introduced a host of other data-related possibilities. Quick time to value 2.

Insurance 52

Insurance Business Intelligence Machine Learning Data 52

Monte Carlo

AUGUST 9, 2023

In fact, according to Gartner , data quality issues cost organizations an average of $12.9 From business intelligence to machine learning, reliable data is the lifeblood of your data products. Bad data in—bad data products out. And that puts data quality at the top of every CTO’s priority list.

Finance 52

Finance High Quality Data SQL Data 52

Monte Carlo

MARCH 28, 2024

For the past few decades, most companies have kept data in an organizational silo. Analytics teams served business units, and even as data became more crucial to decision-making and product roadmaps, the teams in charge of data pipelines were treated more like plumbers and less like partners.

Data 52

Data High Quality Data Machine Learning Accessible 52

Monte Carlo

JANUARY 23, 2024

Gen AI can whip up serviceable code in moments — making it much faster to build and test data pipelines. Today’s LLMs can already process enormous amounts of unstructured data, automating much of the monotonous work of data science. Those who don’t embrace it will be left behind. With the right prompt (this is key!),

Pipeline-centric 52

Pipeline-centric Database-centric Metadata Unstructured Data 52

Monte Carlo

AUGUST 17, 2021

Data informs every business decision, from customer support to feature development, and most recently, how to support pricing plans for organizations most affected during COVID-19. When migrating to Snowflake, PagerDuty wanted to understand the health of their data pipelines through fully automated data observability.

Insurance 40

Insurance Retail Data Pipeline Portfolio 40

Monte Carlo

MAY 17, 2023

Data Warehouse (Or Lakehouse) Migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Transition To A Data Mesh (Or Other Data Team Structure) 40. Prioritize Data Assets And Efforts 41.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Data warehouse (or Lakehouse) migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Transition To A Data Mesh (Or Other Data Team Structure) 40. Prioritize Data Assets And Efforts 41.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52