Monte Carlo

JANUARY 5, 2024

The 5 key layers of data lakehouse architecture Storing structured and unstructured data in a data lakehouse presents many benefits to a data organization, namely making it easier and more seamless to support both business intelligence and data science workloads. This starts at the data source.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

The 5 key layers of data lakehouse architecture Storing structured and unstructured data in a data lakehouse presents many benefits to a data organization, namely making it easier and more seamless to support both business intelligence and data science workloads. This starts at the data source.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Databand.ai

JULY 17, 2023

By adopting a set of best practices inspired by Agile methodologies, DevOps principles, and statistical process control techniques, DataOps helps organizations deliver high-quality data insights more efficiently.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data BI 52

Monte Carlo

AUGUST 31, 2023

Data is a priority for your CEO, as it often is for digital-first companies, and she is fluent in the latest and greatest business intelligence tools. What about a frantic email from your CTO about “duplicate data” in a business intelligence dashboard?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Edureka

FEBRUARY 27, 2023

Business Intelligence Trends: Business intelligence (BI) is becoming an ever more critical element in the success of a business. We’ll also look into ways that businesses can successfully incorporate BI into their practices to gain competitive advantages. What is Business Intelligence?

Business Intelligence 52

Business Intelligence BI Data Mining Data Warehouse 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.

High Quality Data 52

High Quality Data Data Software Engineer Software Engineering 52

Maxime Beauchemin

JANUARY 20, 2017

I joined Facebook in 2011 as a business intelligence engineer. By the time I left in 2013, I was a data engineer. Instead, Facebook came to realize that the work we were doing transcended classic business intelligence. I wasn’t promoted or assigned to this new role.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Monte Carlo

AUGUST 17, 2021

Hotjar’s data engineering team supports over 180 stakeholders and their data needs, from deploying models and building pipelines to keeping tabs on data health. Their artificial intelligence data-driven platform relies on high-quality data to make coverage recommendations for customers.

Insurance 40

Insurance Retail Data Pipeline Portfolio 40

Striim

APRIL 30, 2024

Striim’s Solution Kramp adopted Striim for its powerful, mature real-time data integration, seamlessly connecting diverse databases like Oracle, Microsoft, and Postgres, to ensure continuous, high-quality data replication essential for forecasting and order management.

Google Cloud 52

Google Cloud High Quality Data Business Intelligence Data Warehouse 52

Monte Carlo

APRIL 24, 2023

While we won’t get into the minutia of every consideration for every level of the data stack, it’s important to recall these five considerations as they’ll nonetheless steer the direction of our conversation. Your data team and your customers will thank you. When should you invest in business intelligence?

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 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 70

Raw Data Data Business Intelligence High Quality Data 70

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. Requirements for such a tool included: 1.

Insurance 52

Insurance Business Intelligence Machine Learning Data 52

Monte Carlo

AUGUST 9, 2023

From the hundreds of hours we’ve spent talking to customers, it’s clear that data quality issues are some of the most pernicious challenges facing modern data teams. In fact, according to Gartner , data quality issues cost organizations an average of $12.9 Bad data in—bad data products out.

Finance 52

Finance High Quality Data SQL Data 52

Cloudera

AUGUST 21, 2021

million customers worldwide, recognized how the immense volume of data they maintained could provide better insight into customers’ needs. Since leveraging Cloudera’s data platform, Rabobank has been able to improve its customers’ financial management. Rabobank , headquartered in the Netherlands with over 8.3

Banking 101

Banking Raw Data High Quality Data Business Intelligence 101

Monte Carlo

JANUARY 23, 2024

For example, “Maybe I spend a lot of time in Apache Superset doing data visualization and dashboarding, and if AI is helping me in that context within the tool that I use, it can show me what it’s doing,” he says. “It Embedding conversational AI capabilities into business intelligence products is an example of a good starting point.

Pipeline-centric 52

Pipeline-centric Database-centric Metadata Unstructured Data 52

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

MARCH 28, 2024

This model works well if your organization is growing fast and needs to move quickly, but can lead to duplication and repeated efforts on the embedded analysts’ part without solid alignment with the centralized data team. A data observability tool is a key way to monitor and maintain high-quality data in your pipelines.

Data 52

Data High Quality Data Machine Learning Accessible 52

Monte Carlo

APRIL 1, 2021

Prior to joining ThoughtSpot, Cindi served as a VP at Gartner and lead author of the Magic Quadrant for Analytics and Business Intelligence. She also created the BI Scorecard, one of the first industry frameworks for evaluating business intelligence solutions. ” Now there are some things that have to be perfect.

Building 52

Building BI Business Intelligence Data 52

Monte Carlo

MAY 17, 2023

Keeping these revenue generators online and accurate is a common data observability use case. Move Your Generative AI Strategy From Pitch Deck To Reality If generative AI is a gold rush, high quality data is the pickaxe. We didn’t have good visibility into the data health of the organization.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Keeping these revenue generators online and accurate is a common data observability use case. Move Your Generative AI Strategy From Pitch Deck To Reality If generative AI is a goldrush, high quality data is the pickaxe. We didn’t have good visibility into the data health of the organization.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52