CIO Business Intelligence

DECEMBER 20, 2024

Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects. Curate the data.

Data Architecture 122

Data Architecture Management Consulting Internet of Things 122

Smart Data Collective

OCTOBER 20, 2021

The data integration landscape is under a constant metamorphosis. In the current disruptive times, businesses depend heavily on information in real-time and data analysis techniques to make better business decisions, raising the bar for data integration. Why is Data Integration a Challenge for Enterprises?

Data Integration 133

Data Integration Machine Learning Big Data Statistics 133

CIO Business Intelligence

FEBRUARY 26, 2025

Agentic AI was the big breakthrough technology for gen AI last year, and this year, enterprises will deploy these systems at scale. According to a January KPMG survey of 100 senior executives at large enterprises, 12% of companies are already deploying AI agents, 37% are in pilot stages, and 51% are exploring their use.

IT 141

IT Enterprise Interactive Data Quality 141

David Menninger's Analyst Perspectives

NOVEMBER 19, 2024

Maintaining quality and trust is a perennial data management challenge, the importance of which has come into sharper focus in recent years thanks to the rise of artificial intelligence (AI). Like other data observability software providers, Bigeye could be making more of its applicability to support AI and GenAI use cases.

Data Quality 130

Data Quality Dashboards Data-driven Software 130

CIO Business Intelligence

NOVEMBER 19, 2024

The next phase of this transformation requires an intelligent data infrastructure that can bring AI closer to enterprise data. The challenges of integrating data with AI workflows When I speak with our customers, the challenges they talk about involve integrating their data and their enterprise AI workflows.

Management 116

Management Unstructured Data Deep Learning Metadata 116

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners. The problem is even more magnified in the case of structured enterprise data.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

CIO Business Intelligence

OCTOBER 9, 2024

The rise of generative AI (GenAI) felt like a watershed moment for enterprises looking to drive exponential growth with its transformative potential. As the technology subsists on data, customer trust and their confidential information are at stake—and enterprises cannot afford to overlook its pitfalls.

Strategy 143

Strategy Modeling Management Data Lake 143

David Menninger's Analyst Perspectives

MAY 15, 2025

For context, read this perspective by my colleague, Matt Aslett, on the importance of local data processing. Our research shows that more than half of enterprises (58%) have the majority of data platforms in the cloud, but a substantial portion is deployed on premises. Regards, David Menninger

Data Lake 130

Data Lake Data Warehouse Machine Learning Software 130

DataKitchen

APRIL 13, 2021

This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process.

Testing 304

Testing Machine Learning Consulting Data Science 304

CIO Business Intelligence

OCTOBER 24, 2024

Their top predictions include: Most enterprises fixated on AI ROI will scale back their efforts prematurely. The expectation for immediate returns on AI investments will see many enterprises scaling back their efforts sooner than they should,” Chaurasia and Maheshwari said.

ROI 127

ROI Data-driven Enterprise Experimentation 127

erwin

JANUARY 9, 2020

Enterprises are trying to manage data chaos. They also face increasing regulatory pressure because of global data regulations , such as the European Union’s General Data Protection Regulation (GDPR) and the new California Consumer Privacy Act (CCPA), that went into effect last week on Jan. GDPR: Key Differences.

Data Governance 144

Data Governance Modeling Metadata Unstructured Data 144

CIO Business Intelligence

APRIL 30, 2025

Agentic AI is the new frontier in AI evolution, taking center stage in todays enterprise discussion. As Xerox continues its reinvention, shifting from its traditional print roots to a services-led model, agentic AI fits well into that journey. And around 45% also cite data governance and compliance concerns.

IT 139

IT Sales Cost-Benefit Data-driven 139

CIO Business Intelligence

FEBRUARY 27, 2025

A high hurdle many enterprises have yet to overcome is accessing mainframe data via the cloud. Data professionals need to access and work with this information for businesses to run efficiently, and to make strategic forecasting decisions through AI-powered data models.

Metadata 98

Metadata Data Lake Cost-Benefit Forecasting 98

erwin

JANUARY 17, 2020

What is Data Modeling? Data modeling is a process that enables organizations to discover, design, visualize, standardize and deploy high-quality data assets through an intuitive, graphical interface. Data models provide visualization, create additional metadata and standardize data design across the enterprise.

Data-driven 138

Data-driven Modeling Metadata Data Governance 138

CIO Business Intelligence

APRIL 8, 2025

I aim to outline pragmatic strategies to elevate data quality into an enterprise-wide capability. Key recommendations include investing in AI-powered cleansing tools and adopting federated governance models that empower domains while ensuring enterprise alignment. The patterns are consistent across industries.

Data Quality 102

Data Quality Data-driven Key Performance Indicator Metadata 102

David Menninger's Analyst Perspectives

APRIL 30, 2025

In an earlier Analyst Perspective , I discussed data democratizations role in creating a data-driven enterprise agenda. Building a foundation of self-service data discovery , data-driven organizations provide more workers with the ability to analyze and use data.

Data-driven 130

Data-driven Enterprise Metrics Dashboards 130

erwin

MAY 13, 2020

The role of data modeling (DM) has expanded to support enterprise data management, including data governance and intelligence efforts. Metadata management is the key to managing and governing your data and drawing intelligence from it. Types of Data Models: Conceptual, Logical and Physical.

Data Governance 93

Data Governance Enterprise Modeling Management 93

erwin

AUGUST 31, 2023

Q: Is data modeling cool again? In today’s fast-paced digital landscape, data reigns supreme. The data-driven enterprise relies on accurate, accessible, and actionable information to make strategic decisions and drive innovation. A: It always was and is getting cooler!!

Data-driven 98

Data-driven Modeling Enterprise Structured Data 98

AWS Big Data

OCTOBER 21, 2024

Data is the most significant asset of any organization. However, enterprises often encounter challenges with data silos, insufficient access controls, poor governance, and quality issues. Embracing data as a product is the key to address these challenges and foster a data-driven culture.

Sales 115

Sales Data-driven Data Processing Key Performance Indicator 115

CIO Business Intelligence

MAY 20, 2025

Use machine learning models to monitor behavioral anomalies, detect zero-day threats and trigger rapid incident response. Distributed ledgers can secure device identities, ensure data integrity and provide immutable audit trails. Is our IoT security governance aligned with our broader enterprise security strategy?

IoT 119

IoT Internet of Things Manufacturing Risk 119

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in London earlier this year. A typical data pipeline for machine learning.

Machine Learning 279

Machine Learning Technology Deep Learning Data Science 279

Ontotext

JULY 29, 2021

And what are the commercial implications of semantic technologies for enterprise data? The Semantic Web started in the late 90’s as a fascinating vision for a web of data, which is easy to interpret by both humans and machines. What can it do and how are enterprise knowledge graphs related to it? What is it?

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

CIO Business Intelligence

SEPTEMBER 6, 2024

Unfortunately, many IT teams struggle to organize and track sensitive data across their environments. A workaround that IT teams in many organizations practice is simply moving or copying data from one source system to another. It multiplies data volume, inflating storage expenses and complicating management.

Metadata 116

Metadata Enterprise Management Cost-Benefit 116

AWS Big Data

DECEMBER 4, 2024

They’re taking data they’ve historically used for analytics or business reporting and putting it to work in machine learning (ML) models and AI-powered applications. Amazon SageMaker Unified Studio (Preview) solves this challenge by providing an integrated authoring experience to use all your data and tools for analytics and AI.

Data Analytics 104

Data Analytics Analytics Data Lake Data Quality 104

CIO Business Intelligence

APRIL 25, 2024

“The challenge that a lot of our customers have is that requires you to copy that data, store it in Salesforce; you have to create a place to store it; you have to create an object or field in which to store it; and then you have to maintain that pipeline of data synchronization and make sure that data is updated,” Carlson said.

Data Integration 119

Data Integration Data Lake Data Warehouse Metadata 119

AWS Big Data

MARCH 13, 2025

From within the unified studio, you can discover data and AI assets from across your organization, then work together in projects to securely build and share analytics and AI artifacts, including data, models, and generative AI applications.

Analytics 113

Analytics Data Lake Data Warehouse Data-driven 113

O'Reilly on Data

DECEMBER 19, 2018

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.

Machine Learning 279

Machine Learning Software Metadata Testing 279

erwin

JUNE 4, 2020

Because of its holistic view of an organization, enterprise architecture and mergers & acquisitions (M&A) go hand-in-hand. The Financial Times reported Google, Amazon, Apple, Facebook and Microsoft have made 19 deals so far this year, according to Refinitiv, the London-based global provider of financial market data.

Enterprise 96

Enterprise Cost-Benefit Strategy Modeling 96

CIO Business Intelligence

JANUARY 11, 2024

CIOs are under increasing pressure to deliver AI across their enterprises – a new reality that, despite the hype, requires pragmatic approaches to testing, deploying, and managing the technologies responsibly to help their organizations work faster and smarter. The top brass is paying close attention.

Enterprise 128

Enterprise Uncertainty Reporting Risk Management 128

O'Reilly on Data

NOVEMBER 13, 2018

Considerations for a world where ML models are becoming mission critical. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations.

Machine Learning 277

Machine Learning Risk Management Statistics 277

erwin

MAY 27, 2020

Data modeling supports collaboration among business stakeholders – with different job roles and skills – to coordinate with business objectives. Data resides everywhere in a business , on-premise and in private or public clouds. A single source of data truth helps companies begin to leverage data as a strategic asset.

Modeling 98

Modeling Metadata Data Governance Visualization 98

AWS Big Data

DECEMBER 17, 2024

Industry-leading price-performance Amazon Redshift offers up to three times better price-performance than alternative cloud data warehouses. Amazon Redshift scales linearly with the number of users and volume of data, making it an ideal solution for both growing businesses and enterprises.

Data Lake 98

Data Lake Data Warehouse Data-driven Optimization 98

O'Reilly on Data

FEBRUARY 11, 2019

The resource examples I’ll cite will be drawn from the upcoming Strata Data conference in San Francisco , where leading companies and speakers will share their learnings on the topics covered in this post. AI and machine learning in the enterprise. AI and machine learning in the enterprise. Foundational data technologies.

Big Data 271

Big Data Technology Machine Learning Deep Learning 271

AWS Big Data

JULY 26, 2023

Many AWS customers have integrated their data across multiple data sources using AWS Glue , a serverless data integration service, in order to make data-driven business decisions. Are there recommended approaches to provisioning components for data integration?

Data Integration 98

Data Integration Snapshot Testing Visualization 98

CIO Business Intelligence

AUGUST 16, 2023

Many, if not most, enterprises deploying generative AI are starting with OpenAI, typically via a private cloud on Microsoft Azure. The Azure deployment gives companies a private instance of the chatbot, meaning they don’t have to worry about corporate data leaking out into the AI’s training data set.

Modeling 98

Modeling Enterprise Cost-Benefit Data Science 98

David Menninger's Analyst Perspectives

MAY 6, 2025

Large language models have allowed BI providers to accelerate the delivery of functionality to convert natural language questions into analytic queries and generate summarizations and recommendations from data and charts. This has long been a goal of many enterprises, but few have achieved it.

Analytics 130

Analytics Business Intelligence Interactive Data-driven 130

CIO Business Intelligence

MARCH 19, 2025

A majority of organizations have relied on mainframe systems in some form or another to house vast amounts of transactional data many of which have been around for decades. When considered within the context of AI initiatives,42% of surveyed leaderssaid they considered mainframe data to be a viable option for enriching insights.

Analytics 52

Analytics Enterprise Risk Management Strategy 52