CIO Business Intelligence

MARCH 21, 2022

What is a data scientist? Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. Data scientist salary. Semi-structured data falls between the two.

Unstructured Data 137

Unstructured Data Data Analytics Analytics Structured Data 137

IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. It also lets you choose the right engine for the right workload at the right cost, potentially reducing your data warehouse costs by optimizing workloads. Track models and drive transparent processes.

Risk 76

Risk Modeling Management Metadata 76

IBM Big Data Hub

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases.

Data Quality 52

Data Quality Data Architecture Strategy Data Lake 52

CIO Business Intelligence

NOVEMBER 22, 2022

Data fabric is an architecture that enables the end-to-end integration of various data pipelines and cloud environments through the use of intelligent and automated systems. The fabric, especially at the active metadata level, is important, Saibene notes.

Data Strategy 145

Data Strategy Strategy Enterprise Consulting 145

Rita Sallam

APRIL 2, 2023

We explored these questions and more at our Bake-Offs and Show Floor Showdowns at our Data and Analytics Summit in Orlando with 4,000 of our closest D&A friends and family. The first featured analytics and BI platform Gartner Magic Quadrant leaders while the other showcased high interest data science and machine learning platforms.

Optimization 52

Optimization Machine Learning Insurance Risk 52

AWS Big Data

MARCH 7, 2023

Sources Data can be loaded from multiple sources, such as systems of record, data generated from applications, operational data stores, enterprise-wide reference data and metadata, data from vendors and partners, machine-generated data, social sources, and web sources.

Analytics 68

Analytics Data Warehouse Data Lake Metadata 68

CIO Business Intelligence

JUNE 23, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses.

Data Lake 98

Data Lake Data Warehouse Machine Learning Cost-Benefit 98

AWS Big Data

AUGUST 15, 2023

Some examples include AWS data analytics services such as AWS Glue for data integration, Amazon QuickSight for business intelligence (BI), as well as third-party software and services from AWS Marketplace. Doing so can help unblock developers and data scientists so they can efficiently provide results and save time.

Recreation/Entertainment 84

Recreation/Entertainment Unstructured Data Business Intelligence Data-driven 84

Cloudera

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses.

Data Lake 92

Data Lake Data Warehouse Machine Learning Cost-Benefit 92

Jen Stirrup

OCTOBER 5, 2019

I’m particularly interested in learning more about metadata management. As I’ve written previously, I believe that metadata management is often overlooked in data science. It’s an important part of obtaining full value from data, and it should be part of the data maturity model.

Metadata 44

Metadata Data Science Risk Modeling 44

Cloudera

AUGUST 31, 2021

Today, modern data warehousing has evolved to meet the intensive demands of the newest analytics required for a business to be data driven. Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis. Consideration of both data & metadata in the migration.

Data Warehouse 84

Data Warehouse Cost-Benefit Metadata Data-driven 84

CIO Business Intelligence

JUNE 23, 2022

Only Cloudera has the power to span multi-cloud and on-premises with a hybrid data platform. We deliver cloud-native data analytics across the full data lifecycle – data distribution, data engineering, data warehousing, transactional data, streaming data, data science, and machine learning – that’s portable across infrastructures.

IT 97

IT Data Architecture Unstructured Data Big Data 97

CIO Business Intelligence

JUNE 6, 2023

A European Multinational Insurance company deployed CDP both on-prem and on Azure public cloud with Cloudera Data Science Workbench (CDSW) and Cloudera Machine Learning (CML). Portability only works if you are fully portable: not just workloads and data but everything that goes along with it. But here’s the thing.

Insurance 52

Insurance Metadata Data Processing Machine Learning 52

Domino Data Lab

APRIL 3, 2019

The top three items are essentially “the devil you know” for firms which want to invest in data science: data platform, integration, data prep. Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. Rinse, lather, repeat.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

AWS Big Data

MARCH 10, 2023

However, as data processing at scale solutions grow, organizations need to build more and more features on top of their data lakes. Additionally, the task of maintaining and managing files in the data lake can be tedious and sometimes complex. The Iceberg table is synced with the AWS Glue Data Catalog.

Data Lake 98

Data Lake Sales Data Warehouse Snapshot 98

AWS Big Data

MAY 7, 2024

Figure 1: Flow of actions for self-service analytics around data assets stored in relational databases First, the data producer needs to capture and catalog the technical metadata of the data asset. The producer also needs to manage and publish the data asset so it’s discoverable throughout the organization.

Metadata 72

Metadata Data Lake Data Processing Data-driven 72

Peter James Thomas

AUGUST 18, 2018

One of the early promises of a Data Lake approach was that – once all relevant data had been ingested – this would be directly leveraged by Data Scientists to derive insight. Over time, it became clear that it would be useful to also have some merged / conformed and cleansed data structures in the Data Lake.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Rita Sallam

JULY 22, 2016

By contrast, traditional BI platforms are designed to support modular development of IT-produced analytic content, specialized tools and skills, and significant upfront data modeling, coupled with a predefined metadata layer, is required to access their analytic capabilities. Research VP, Business Analytics and Data Science.

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

O'Reilly on Data

FEBRUARY 11, 2020

They don’t have the resources they need to clean up data quality problems. The building blocks of data governance are often lacking within organizations. These include the basics, such as metadata creation and management, data provenance, data lineage, and other essentials. And that’s just the beginning.

Data Quality 304

Data Quality Metadata Data Governance Publishing 304

Alation

FEBRUARY 13, 2020

This article is based on a podcast Ron Powell conducted with Sharon Graves, Enterprise Data and BI Tools Evangelist for GoDaddy, about data curation, data stewardship, and data catalogs. His focus is on business intelligence, analytics, big data, and data warehousing.

Digital Transformation 52

Digital Transformation Data-driven Business Intelligence Big Data 52

Peter James Thomas

AUGUST 29, 2018

On a day to day basis, we are aligned with the business units and the functional units so we have CDOs in all of these areas. Additionally I have a direct set of reports who drive the standard solutions around tooling, governance, quality, data protection , Data Ethics , Metadata and data glossary and models.

Data-driven 61

Data-driven Cost-Benefit Metadata Technology 61

AWS Big Data

SEPTEMBER 25, 2023

With Amazon Redshift , you can run a complex mix of workloads on your data warehouse, such as frequent data loads running alongside business-critical dashboard queries and complex transformation jobs. We also see more and more data science and machine learning (ML) workloads. 5 Used by the superuser queue.

Management 91

Management Data Warehouse Metrics Dashboards 91

IBM Big Data Hub

JANUARY 17, 2023

These new technologies and approaches, along with the desire to reduce data duplication and complex ETL pipelines, have resulted in a new architectural data platform approach known as the data lakehouse – offering the flexibility of a data lake with the performance and structure of a data warehouse.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. BTW, videos for Rev2 are up: [link]. On deck this time ’round the Moon: program synthesis.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Alation

JULY 19, 2022

As they attempt to put machine learning models into production, data science teams encounter many of the same hurdles that plagued data analytics teams in years past: Finding trusted, valuable data is time-consuming. Obstacles, such as user roles, permissions, and approval request prevent speedy data access.

Metadata 59

Metadata Data Quality Statistics Data Science 59

Cloudera

AUGUST 30, 2022

Data Lakehouse: Data lakehouses integrate and unify the capabilities of data warehouses and data lakes, aiming to support artificial intelligence, business intelligence, machine learning, and data engineering use cases on a single platform. Towards Data Science ). Forrester ).

Data Architecture 79

Data Architecture Data Lake Metadata Data Warehouse 79

Data Virtualization

JANUARY 18, 2024

Reading Time: 3 minutes Today, the most innovative and successful organizations leverage data to increase revenue, minimize expenses, and deliver products and services that meet the needs of their customers. To be truly “data-driven,” an organization must view data as more than a byproduct. The post How to Shop for Data?

Data-driven 59

Data-driven Data Integration Management Metadata 59

Alation

JANUARY 17, 2023

A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Data orchestration tools. Business intelligence (BI) platforms. These tools are used to manage big data, which is defined as data that is too large or complex to be processed by traditional means.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Business Intelligence 52

CIO Business Intelligence

MARCH 9, 2022

They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics. On the other hand, they don’t support transactions or enforce data quality. If only there were a best-of-both-worlds compromise. . Learn more at [link]. .

Data Lake 119

Data Lake Unstructured Data Data Warehouse Data Quality 119

Alation

APRIL 14, 2022

In today’s AI/ML-driven world of data analytics, explainability needs a repository just as much as those doing the explaining need access to metadata, EG, information about the data being used. The Cloud Data Migration Challenge. Pushing data to a data lake and assuming it is ready for use is shortsighted.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

IBM Big Data Hub

JULY 13, 2023

The more complete, accurate and consistent a dataset is, the more informed business intelligence and business processes become. Data science tasks such as machine learning also greatly benefit from good data integrity.

Data Quality 67

Data Quality Data Integration Metadata Cost-Benefit 67

Domino Data Lab

JANUARY 21, 2021

This blog explores the challenges associated with doing such work manually, discusses the benefits of using Pandas Profiling software to automate and standardize the process, and touches on the limitations of such tools in their ability to completely subsume the core tasks required of data science professionals and statistical researchers.

Statistics 106

Statistics Unstructured Data Data Science Predictive Modeling 106

IBM Big Data Hub

JULY 17, 2023

By supporting open-source frameworks and tools for code-based, automated and visual data science capabilities — all in a secure, trusted studio environment — we’re already seeing excitement from companies ready to use both foundation models and machine learning to accomplish key tasks.

Machine Learning 66

Machine Learning Data Warehouse Modeling Cost-Benefit 66

datapine

JANUARY 31, 2022

Using business intelligence and analytics effectively is the crucial difference between companies that succeed and companies that fail in the modern environment. Experience the power of Business Intelligence with our 14-days free trial! Why Is Business Intelligence So Important?

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Alation

SEPTEMBER 23, 2021

What Is a Data Catalog? A data catalog is a centralized storage bank of metadata on information sources from across the enterprise, such as: Datasets. Business intelligence reports. The data catalog also stores metadata (data about data, like a conversation), which gives users context on how to use each asset.

Enterprise 52

Enterprise Metadata Data Governance Visualization 52

Cloudera

JANUARY 21, 2021

With CDW, as an integrated service of CDP, your line of business gets immediate resources needed for faster application launches and expedited data access, all while protecting the company’s multi-year investment in centralized data management, security, and governance. Cost-optimization and ease-of-use .

Data Lake 95

Data Lake Data Warehouse IT Analytics 95

CIO Business Intelligence

SEPTEMBER 22, 2023

Others aim simply to manage the collection and integration of data, leaving the analysis and presentation work to other tools that specialize in data science and statistics. DMP vs. CDP Lately a cousin of DMP has evolved, called the customer data platform (CDP).

Management 105

Management Advertising Data Lake Sales 105