IBM Big Data Hub

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

Data Quality 52

Data Quality Data Architecture Strategy Data Lake 52

AWS Big Data

OCTOBER 10, 2023

Due to the volume, velocity, and variety of data being ingested in data lakes, it can get challenging to develop and maintain policies and procedures to ensure data governance at scale for your data lake. Data confidentiality and data quality are the two essential themes for data governance.

Data Quality 92

Data Quality Data Governance Data Lake Testing 92

CIO Business Intelligence

APRIL 11, 2024

In a cloud market dominated by three vendors, once cloud-denier Oracle is making a push for enterprise share gains, announcing expanded offerings and customer wins across the globe, including Japan , Mexico , and the Middle East. Oracle is helped by the fact that it has two offerings for enterprise applications, says Thompson.

Enterprise 124

Enterprise IT Data Processing Strategy 124

Alation

JANUARY 20, 2022

Several weeks ago (prior to the Omicron wave), I got to attend my first conference in roughly two years: Dataversity’s Data Quality and Information Quality Conference. Ryan Doupe, Chief Data Officer of American Fidelity, held a thought-provoking session that resonated with me. Step 2: Data Definitions.

Data Quality 94

Data Quality Data Governance Metrics Statistics 94

IBM Big Data Hub

NOVEMBER 4, 2022

Data is the new oil and organizations of all stripes are tapping this resource to fuel growth. However, data quality and consistency are one of the top barriers faced by organizations in their quest to become more data-driven. Unlock quality data with IBM. and its leading data observability offerings.

Data Quality 56

Data Quality Metadata Data Governance Data-driven 56

DataRobot Blog

MAY 10, 2022

Addressing the Key Mandates of a Modern Model Risk Management Framework (MRM) When Leveraging Machine Learning . The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States.

Risk 64

Risk Modeling Machine Learning Data Quality 64

IBM Big Data Hub

AUGUST 23, 2023

The recent success of artificial intelligence based large language models has pushed the market to think more ambitiously about how AI could transform many enterprise processes. However, consumers and regulators have also become increasingly concerned with the safety of both their data and the AI models themselves.

Data Governance 52

Data Governance Enterprise Modeling Risk 52

CIO Business Intelligence

MAY 3, 2022

Last year, Dow took a bold step to make better use of its data. With a goal of eliminating isolated islands of data and making better use of business intelligence as an enterprise asset, the company launched an internal organization that seamlessly integrated IT and the company’s global business units under one umbrella.

Data Quality 81

Data Quality Machine Learning Business Objectives Business Intelligence 81

AWS Big Data

FEBRUARY 29, 2024

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

Data Governance 96

Data Governance Unstructured Data Metadata Data Lake 96

O'Reilly on Data

JUNE 18, 2019

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Data integration and cleaning.

Machine Learning 264

Machine Learning Data Quality Statistics Modeling 264

Anmut

MARCH 22, 2023

Data maturity models are a crucial step for any organisation looking to improve their data, informing if your current data practices are helping, or holding back, your business. ? Click the links below to navigate to different sections What are data maturity models? Why do we need data maturity models?

Measurement 52

Measurement Modeling Data Quality Data Governance 52

AWS Big Data

NOVEMBER 16, 2023

As with all AWS services, Amazon Redshift is a customer-obsessed service that recognizes there isn’t a one-size-fits-all for customers when it comes to data models, which is why Amazon Redshift supports multiple data models such as Star Schemas, Snowflake Schemas and Data Vault.

Enterprise 85

Enterprise Data Warehouse Snapshot Cost-Benefit 85

CIO Business Intelligence

APRIL 16, 2024

You’re fast asleep aboard the USS Enterprise Star Trek. While warp speed is a fictional concept, it’s an apt way to describe what generative AI (GenAI) and large language models (LLMs) are doing to exponentially accelerate Industry 4.0. It’s Wednesday night. Captain James T. This creates barriers to Industry 4.0

Manufacturing 141

Manufacturing Digital Transformation Risk Dashboards 141

Ontotext

MARCH 8, 2023

Use Case #1: Customer 360 / Enterprise 360 Customer data is typically spread across multiple applications, departments, and regions. Each team and system need to keep diverse sets of data about their customers in order to play their specific role – inadvertently leading to siloed experiences. million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

TDAN

SEPTEMBER 14, 2021

In this article, I describe a method of modelling data so that it meets business requirements. Central to this method is that modelling not only the required data, but also the subset of the real world that concerns the enterprise.

Modeling 52

Modeling Enterprise Data Quality IT 52

CIO Business Intelligence

MAY 15, 2024

It seems anyone can make an AI model these days. Even if you don’t have the training data or programming chops, you can take your favorite open source model, tweak it, and release it under a new name. And these models, though they lag behind the big commercial ones, are improving quickly.

Modeling 136

Modeling Risk Software Enterprise 136

bridgei2i

JUNE 23, 2021

4 Key Elements of Enterprise AI Strategy. Enterprise AI harnesses advanced artificial intelligence techniques to deliver organizational data, knowledge, and information. Enterprise AI automates the end-to-end journey from data to value. Is your business infrastructure mature enough to embrace the AI model?

Strategy 52

Strategy Enterprise Key Performance Indicator ROI 52

DataRobot Blog

MAY 26, 2022

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

Risk 52

Risk Modeling Metrics Business Objectives 52

Rocket-Powered Data Science

FEBRUARY 15, 2023

While generative AI has been around for several years , the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

bridgei2i

NOVEMBER 8, 2021

Powering Enterprise Revenue Growth with AI. As enterprises navigate through the spectrum of new technologies powered by the Internet, cloud computing, mobile applications, and artificial intelligence, the landscape of how B2B enterprises engage and transact with their customers is undergoing a dramatic transformation.

Enterprise 52

Enterprise B2B Sales Cost-Benefit 52

O'Reilly on Data

FEBRUARY 11, 2020

We suspected that data quality was a topic brimming with interest. The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. Key survey results: The C-suite is engaged with data quality. Data quality might get worse before it gets better.

Data Quality 311

Data Quality Metadata Data Governance Publishing 311

Alation

JUNE 2, 2022

These include tracking, documenting, monitoring, versioning, and controlling access to AI/ML models. Currently, models are managed by modelers and by the software tools they use, which results in a patchwork of control, but not on an enterprise level. And until recently, such governance processes have been fragmented.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

CIO Business Intelligence

JANUARY 24, 2024

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

Data Strategy 136

Data Strategy Strategy Unstructured Data Data Governance 136

CIO Business Intelligence

MARCH 24, 2023

It encompasses the people, processes, and technologies required to manage and protect data assets. The Data Management Association (DAMA) International defines it as the “planning, oversight, and control over management of data and the use of data and data-related sources.”

Data Governance 142

Data Governance Management Metadata Data Quality 142

CIO Business Intelligence

APRIL 1, 2024

Data preparation, including anonymizing, labeling, and normalizing data across sources, is key. You’ll also institute guardrails for data governance, data quality, data integrity, and data security. Right-size your model(s). 2024 2 The State of Generative AI in the Enterprise, Deloitte, Jan.

Data Lake 131

Data Lake Consulting Uncertainty Risk 131

CIO Business Intelligence

SEPTEMBER 1, 2023

More usefully for enterprises, he also helped develop Dynamics 365 AI for Market Insights, a feature for Microsoft’s ERP and CRM platform that scans search data to provide enterprises with information about emerging trends in social interest and sentiment around their brands. Enterprise Applications, SAP

IT 105

IT Reporting Forecasting Data Quality 105

CIO Business Intelligence

APRIL 17, 2024

Salesforce’s reported bid to acquire enterprise data management vendor Informatica could mean consolidation for the integration platform-as-a-service (iPaaS) market and a new revenue stream for Salesforce, according to analysts. The enterprise data management vendor reported a total revenue of $1.5 billion and $1.6

Cost-Benefit 128

Cost-Benefit B2B Marketing Enterprise 128

Ontotext

MARCH 18, 2020

According to an article in Harvard Business Review , cross-industry studies show that, on average, big enterprises actively use less than half of their structured data and sometimes about 1% of their unstructured data. Why Enterprise Knowledge Graphs? The third challenge is how to combine data management with analytics.

Enterprise 59

Enterprise B2B Unstructured Data Machine Learning 59

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

Metrics 81

Metrics Machine Learning Statistics Risk 81

O'Reilly on Data

NOVEMBER 28, 2023

And everyone has opinions about how these language models and art generation programs are going to change the nature of work, usher in the singularity, or perhaps even doom the human race. In enterprises, we’ve seen everything from wholesale adoption to policies that severely restrict or even forbid the use of generative AI.

Enterprise 279

Enterprise Modeling Testing Reporting 279

Octopai

JUNE 9, 2024

They invested heavily in data infrastructure and hired a talented team of data scientists and analysts. The goal was to develop sophisticated data products, such as predictive analytics models to forecast patient needs, patient care optimization tools, and operational efficiency dashboards.

IT 52

IT Data-driven Data Strategy Predictive Analytics 52

CIO Business Intelligence

APRIL 25, 2024

With more than 1 billion users globally, LinkedIn is continuously bumping against the limits of what is technically feasible in the enterprise today. Few companies operate at the scale that LinkedIn does or have access to similar troves of data. Here, retrieval augmented generation (RAG) is a typical work-around.

IT 139

IT Experimentation Cost-Benefit Metrics 139

CIO Business Intelligence

DECEMBER 8, 2023

But as the numbers of new gen AI-powered chatbots grow, so do the risks of their occasional glitches—nonsensical or inaccurate outputs or answers that are not easily screened out of the large language models (LLMs) that the tools are trained on. Continually upgrade data quality. In AI parlance, they’re called hallucinations.

Risk 121

Risk Finance Data Quality Modeling 121

DataKitchen

OCTOBER 2, 2023

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

Data Quality 100

Data Quality Testing Uncertainty Data Enablement 100

CIO Business Intelligence

APRIL 25, 2022

Enterprises are betting big on machine learning (ML). But despite all the money flowing into ML projects, most organizations are struggling to get their ML models and applications working on production systems. . IDC’s numbers look even worse, with only 31% of enterprises surveyed saying that they have AI functioning in production.

Machine Learning 131

Machine Learning Enterprise Deep Learning Cost-Benefit 131

IBM Big Data Hub

JUNE 23, 2022

Data governance – Some of the most exciting governance capabilities of the IBM Data fabric include automatically applying metadata to new datasets using machine learning as well as auto-generated data quality assessments and scoring and AI-based dataset recommendations. Providing the semantic.

Enterprise 52

Enterprise Metadata Data Governance Machine Learning 52

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