CIO Business Intelligence

MARCH 24, 2023

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. The program must introduce and support standardization of enterprise data.

Data Governance 142

Data Governance Management Metadata Data Quality 142

Ontotext

JANUARY 29, 2024

In 2023, data leaders and enthusiasts were enamored of — and often distracted by — initiatives such as generative AI and cloud migration. LLMs can optimize several tasks, such as updating taxonomies, classifying entities, and extracting new properties and relationships from unstructured data.

Strategy 52

Strategy Management Metadata Data-driven 52

AWS Big Data

MARCH 7, 2023

This post provides guidance on how to build scalable analytical solutions for gaming industry use cases using Amazon Redshift Serverless. Flexible and easy to use – The solutions should provide less restrictive, easy-to-access, and ready-to-use data. They should also provide optimal performance with low or no tuning.

Analytics 71

Analytics Data Warehouse Data Lake Metadata 71

IBM Big Data Hub

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

AWS Big Data

JUNE 6, 2023

As most organizations, that have turned to Google Analytics (GA) as a digital analytics solution, mature they discover a more pressing need to integrate this data silo with the rest of their organization’s data to enable better analytics and resulting product development and fraud detection.

Analytics 55

Analytics Data Lake Testing Optimization 55

CIO Business Intelligence

NOVEMBER 22, 2022

“Failing to meet these needs means getting left behind and missing out on the many opportunities made possible by advances in data analytics.” The next step in every organization’s data strategy, Guan says, should be investing in and leveraging artificial intelligence and machine learning to unlock more value out of their data.

Data Strategy 145

Data Strategy Strategy Enterprise Consulting 145

Ontotext

MAY 2, 2024

Data leaders today are faced with an almost impossible challenge. Particularly those on the “the create side of the house” who are tasked to deliver insights and analytics. Such inconsistencies bring lowered trust in the outcomes analytics and insights leaders try to get. The best way to drive value is through use cases.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

Cloudera

MARCH 22, 2023

Cloudera Data Platform (CDP) is no different: it’s a hybrid data platform that meets organizations’ needs to get to grips with complex data anywhere, turning it into actionable insight quickly and easily. And, crucial for a hybrid data platform, it does so across hybrid cloud. As observability evolves, so will CDP.

Metrics 88

Metrics Data Governance Cost-Benefit Dashboards 88

Ontotext

DECEMBER 1, 2023

Atanas Kiryakov presenting at KGF 2023 about Where Shall and Enterprise Start their Knowledge Graph Journey Only data integration through semantic metadata can drive business efficiency as “it’s the glue that turns knowledge graphs into hubs of metadata and content”.

Metadata 78

Metadata Sales Consulting Enterprise 78

Alation

OCTOBER 5, 2021

Why is your data governance strategy failing? According to the Gartner report, The State of Data and Analytics Governance Is Worse Than You Think , approximately 80% of businesses readily acknowledge that high-quality data governance is essential to achieving long-term business goals, objectives, and outcomes.

Data Governance 96

Data Governance Strategy Data Quality Metrics 96

Timo Elliott

DECEMBER 14, 2022

The future is enabled by technology, but it’s not about the technical infrastructures: it’s about optimizing end-to-end processes, business capabilities, and business ecosystems. SAP BTP brings together data and analytics, artificial intelligence, application development, automation, and integration in one, unified environment.

Data Lake 105

Data Lake Recreation/Entertainment Metadata Data Warehouse 105

IBM Big Data Hub

APRIL 10, 2023

That is, it should support both sound data governance —such as allowing access only by authorized processes and stakeholders—and provide oversight into the use and trustworthiness of AI through transparency and explainability. A similar correlation holds true in leveraging data to drive leadership performance in the ESG areas.

Strategy 86

Strategy Data Architecture Cost-Benefit Reporting 86

Ontotext

JULY 6, 2023

This shift of both a technical and an outcome mindset allows them to establish a centralized metadata hub for their data assets and effortlessly access information from diverse systems that previously had limited interaction. There are four groups of data that are naturally siloed: Structured data (e.g.,

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

Cloudera

AUGUST 31, 2021

Data warehousing is the backbone of every data driven organization , providing mission critical analytics. Today, modern data warehousing has evolved to meet the intensive demands of the newest analytics required for a business to be data driven. Copyright © 2021 Accenture. All rights reserved.

Data Warehouse 81

Data Warehouse Cost-Benefit Metadata Data-driven 81

AWS Big Data

NOVEMBER 16, 2023

In the following sections, we discuss the most common areas of consideration that are critical for Data Vault implementations at scale: data protection, performance and elasticity, analytical functionality, cost and resource management, availability, and scalability. String-optimized compression The Data Vault 2.0

Enterprise 83

Enterprise Data Warehouse Snapshot Cost-Benefit 83

AWS Big Data

NOVEMBER 6, 2023

The following graph describes a simple data quality check pipeline using setup and teardown tasks. Airflow will cache variables and connections locally so that they can be accessed faster during DAG parsing, without having to fetch them from the secrets backend, environments variables, or metadata database.

Metrics 102

Metrics Metadata Snapshot Management 102

bridgei2i

MARCH 11, 2021

Creating an efficient data governance strategy means – Breaking down all sources of accumulated data across the organization. Recognizing the “right” data that can be optimized by AI-powered business analytics tools. Identify data errors and eliminate them from the system. in the system.

Key Performance Indicator 52

Key Performance Indicator Data Governance Data Lake Metadata 52

AWS Big Data

FEBRUARY 1, 2023

This introduces the need for both polling and pushing the data to access and analyze in near-real time. From an operational standpoint, we designed a new shared responsibility model for data ingestion using AWS Glue instead of internal services (REST APIs) designed on Amazon EC2 to extract the data.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

Jet Global

JULY 2, 2019

For example, Azure Data Lake includes all the capabilities required to make it easy for developers, data scientists, and analysts to store data of any size, shape, and speed—and do all types of processing and analytics across platforms and languages. Future-Proofing your Data.

Data Lake 73

Data Lake Data Warehouse Big Data Machine Learning 73

O'Reilly on Data

JULY 28, 2020

These measures are commonly referred to as guardrail metrics , and they ensure that the product analytics aren’t giving decision-makers the wrong signal about what’s actually important to the business. If this sounds fanciful, it’s not hard to find AI systems that took inappropriate actions because they optimized a poorly thought-out metric.

Marketing 363

Marketing Experimentation Metrics Testing 363

Alation

JUNE 2, 2022

In part two of this series, I’ll dive more deeply into AI/ML and data lineage, in the cloud and otherwise, and discuss how data governance and security can support these critical use cases, wherever they reside. Data management is not yet a solved problem, but modern data management is leagues ahead of prior approaches.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

Alation

SEPTEMBER 7, 2021

Now that you have empowered data scientists and analysts to access the Snowflake Data Cloud and speed their modeling and analysis, you need to bolster the effectiveness of your governance models. But governance is a time-consuming process (for users and data stewards alike). Find Trusted Data.

Data Governance 59

Data Governance Risk Data Quality Dashboards 59

Ontotext

MARCH 8, 2023

Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. Use Case #1: Customer 360 / Enterprise 360 Customer data is typically spread across multiple applications, departments, and regions. Several factors are driving the adoption of knowledge graphs.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

O'Reilly on Data

MARCH 31, 2020

You might have millions of short videos , with user ratings and limited metadata about the creators or content. Job postings have a much shorter relevant lifetime than movies, so content-based features and metadata about the company, skills, and education requirements will be more important in this case.

Management 339

Management Machine Learning Experimentation Metrics 339

Alation

SEPTEMBER 16, 2021

As an emerging software category, data intelligence solves a range of crucial use cases, including: Search & Discovery. Data Privacy. Data Quality. Metadata Management. Self-Service Analytics. Data intelligence runs in the background, learning first and optimizing second.

Machine Learning 52

Machine Learning Data Governance Software Metadata 52

DataKitchen

SEPTEMBER 21, 2023

L1 is usually the raw, unprocessed data ingested directly from various sources; L2 is an intermediate layer featuring data that has undergone some form of transformation or cleaning; and L3 contains highly processed, optimized, and typically ready for analytics and decision-making processes. What is Data in Use?

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

AWS Big Data

JULY 20, 2023

One key component that plays a central role in modern data architectures is the data lake, which allows organizations to store and analyze large amounts of data in a cost-effective manner and run advanced analytics and machine learning (ML) at scale. To overcome these issues, Orca decided to build a data lake.

Data Lake 87

Data Lake Analytics Snapshot Optimization 87

Alation

MARCH 21, 2023

Today, modern travel and tourism thrive on data. For example, airlines have historically applied analytics to revenue management, while successful hospitality leaders make data-driven decisions around property allocation and workforce management. What is big data in the travel and tourism industry?

Data Analytics 52

Data Analytics Analytics Data-driven Big Data 52

Ontotext

MARCH 27, 2024

For example, GPS, social media, cell phone handoffs are modeled as graphs while data catalogs, data lineage and MDM tools leverage knowledge graphs for linking metadata with semantics. RDF is used extensively for data publishing and data interchange and is based on W3C and other industry standards.

Modeling 69

Modeling Metadata Data Quality Enterprise 69

IBM Big Data Hub

JANUARY 17, 2023

However, most of their raw data remains unused and trapped in data silos, making extracting insights difficult. The rise of cloud object storage has driven the cost of storage down and new technologies have evolved to access and query the data stored in there more efficiently.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

erwin

FEBRUARY 4, 2021

Without an accurate, high-quality, real-time enterprise data pipeline, it will be difficult to uncover the necessary intelligence to make optimal business decisions. So what’s holding organizations back from fully using their data to make better, smarter business decisions? Data Governance Bottlenecks. Regulations.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

IBM Big Data Hub

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

Cost-Benefit 76

Cost-Benefit Metadata Optimization Management 76

IBM Big Data Hub

OCTOBER 12, 2023

Once trained and deployed, models also need reliable access to historical and real-time data to generate content, make recommendations, detect errors, send proactive alerts, etc. Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model.

Cost-Benefit 76

Cost-Benefit Metadata Data Governance Modeling 76

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data.

Data Strategy 68

Data Strategy Strategy Data Warehouse Prescriptive Analytics 68

Andrew White

JANUARY 9, 2022

On Thursday January 6th I hosted Gartner’s 2022 Leadership Vision for Data and Analytics webinar. – In the webinar and Leadership Vision deck for Data and Analytics we called out AI engineering as a big trend. So in summary they are very similar concepts but data fabric seems to be the more rounded of the two.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Cloudera

JUNE 29, 2023

Look for ways to integrate predictive analytics and ML into liquidity risk management — for example, by monitoring intraday liquidity, optimizing the timing of payments, reducing payment delays and/or dependence on intraday credit. Apply emerging technology to intraday liquidity management. Enhance counterparty risk assessment.

Data Architecture 85

Data Architecture Risk Management Risk Management 85

AWS Big Data

NOVEMBER 15, 2023

DataBrew is an excellent tool for data quality and preprocessing. You can use its built-in transformations, recipes, as well as integrations with the AWS Glue Data Catalog and Amazon Simple Storage Service (Amazon S3) to preprocess the data in your landing zone, clean it up, and send it downstream for analytical processing.

Metadata 83

Metadata Sales Data Lake Big Data 83