Octopai

MAY 15, 2023

By analyzing this information, organizations can optimize their infrastructure and storage strategies, avoiding unnecessary storage costs and efficiently allocating resources based on data usage patterns. Data integration and ETL costs: Large organizations often deal with complex data integration and Extract, Transform, Load (ETL) processes.

Data Quality 52

Data Quality Data Governance Data Integration Risk 52

CIO Business Intelligence

JANUARY 9, 2024

He and his team have created information decks, documents, and presentations that describe the various types of AI and how they can be used and explain how and where AI and machine learning may be useful — and why it’s not the solution to all the problems they have. What is our appetite for risk and how do we address it?

IT 140

IT Risk Cost-Benefit Enterprise 140

erwin

JANUARY 11, 2019

A company can’t effectively implement data governance – documenting and applying business rules and processes, analyzing the impact of changes and conducting audits – if it fails at data management. The problem usually starts by relying on manual integration methods for data preparation and mapping.

Data Governance 40

Data Governance Risk Metadata Management 40

Cloudera

JUNE 22, 2023

It ensures compliance with regulatory requirements while shifting non-sensitive data and workloads to the cloud. Its built-in intelligence automates common data management and data integration tasks, improves the overall effectiveness of data governance, and permits a holistic view of data across the cloud and on-premises environments.

Cost-Benefit 66

Cost-Benefit Digital Transformation Data Governance Unstructured Data 66

erwin

OCTOBER 8, 2020

No less daunting, your next step is to re-point or even re-platform your data movement processes. And you can’t risk false starts or delayed ROI that reduces the confidence of the business and taint this transformational initiative. The metadata-driven suite automatically finds, models, ingests, catalogs and governs cloud data assets.

Data Governance 145

Data Governance Metadata Testing Data Lake 145

IBM Big Data Hub

FEBRUARY 6, 2024

Through the development of cyber recovery plans that include data validation through custom scripts, machine learning to increase data backup and data protection capabilities, and the deployment of virtual machines (VMs) , companies can recover from cyberattacks and prevent re-infection by malware in the future.

Cost-Benefit 98

Cost-Benefit Testing Risk Strategy 98

erwin

APRIL 29, 2020

Many large organizations, in their desire to modernize with technology, have acquired several different systems with various data entry points and transformation rules for data as it moves into and across the organization. Business terms and data policies should be implemented through standardized and documented business rules.

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

erwin

JANUARY 24, 2019

In most companies, an incredible amount of data flows from multiple sources in a variety of formats and is constantly being moved and federated across a changing system landscape. An organization can achieve a more comprehensive and sustainable data governance initiative than it ever could with a homegrown solution.

Data Governance 71

Data Governance Metadata Data-driven Cost-Benefit 71

erwin

JANUARY 30, 2020

Without a comprehensive and automated data governance framework, enterprises put themselves at high risk of conducting business based on poor metadata management and data intelligence processes, introducing unnecessary slowdowns and inaccuracies in their analytics. Organizations still depend too much on manual data management.

Data Governance 145

Data Governance Metadata Digital Transformation Cost-Benefit 145

AWS Big Data

FEBRUARY 29, 2024

However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

Data Governance 93

Data Governance Unstructured Data Metadata Data Lake 93

Ontotext

APRIL 6, 2023

This model is used in various industries to enable seamless data integration, unification, analysis and sharing. More and more companies are using them to improve a variety of tasks from product range specification and risk analysis to supporting self-driving cars.

Manufacturing 83

Manufacturing Cost-Benefit Knowledge Discovery Optimization 83

O'Reilly on Data

DECEMBER 12, 2019

1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. That’s where model debugging comes in. Sensitivity analysis.

Machine Learning 190

Machine Learning Modeling Testing Risk Management 190

IBM Big Data Hub

SEPTEMBER 20, 2022

Alongside the significant brand reputation risk, there’s also a growing set of data and AI regulations across the world and across industries — like the upcoming European Union AI Act — that companies must adhere to. It’s not just about granting proper access to data science teams.

Metadata 57

Metadata Machine Learning Data-driven Modeling 57

IBM Big Data Hub

JANUARY 15, 2024

Whether they use the public cloud, private cloud or multicloud environment, they are removing the onerous step of maintaining their own data centers. Trend: Cybersecurity By virtue of moving many services online, organizations are at greater risk of digital attacks.

Digital Transformation 78

Digital Transformation Internet of Things IoT Consulting 78

CIO Business Intelligence

APRIL 27, 2022

All are ideally qualified to help their customers achieve and maintain the highest standards for data integrity, including absolute control over data access, transparency and visibility into the provider’s operation, the knowledge that their information is managed appropriately, and access to VMware’s growing ecosystem of sovereign cloud solutions.

Digital Transformation 109

Digital Transformation Metadata Risk Management 109

erwin

JANUARY 17, 2020

Otherwise, data-driven initiatives can stall. Thanks to organizations like Amazon, Netflix and Uber, businesses have changed how they leverage their data and are transforming their business models to innovate – or risk becoming obsolete. The Advantages of NoSQL Data Modeling. What is Data Modeling? SQL or NoSQL?

Data-driven 138

Data-driven Modeling Metadata Data Governance 138

Smart Data Collective

JULY 15, 2021

This means that they can be ideal for data cleansing and maintenance. Data catalogs also let employees see the changes in stored and utilized information sets over time. Staff charged with overseeing data integrity and viability can view life cycles of information sets. What Does a Data Catalog Consist Of?

Metadata 127

Metadata IT Data-driven Data Quality 127

erwin

MAY 2, 2022

I’m not proposing that business logic be held in the database, but it needs to be documented in the data model even if it will be implemented in application code. No matter where you implement it, you must fully document the business requirements in order to produce an effective final result. Webcast: Build better data models.

Modeling 98

Modeling Insurance Cost-Benefit Data Warehouse 98

erwin

APRIL 27, 2022

Best Data Governance Solution (erwin Data Intelligence). erwin Data Intelligence by Quest combines data catalog and data literacy capabilities for enterprise-wide visibility of available data assets, guidance on their use, and guardrails to ensure data policies and best data practices are followed.

Data Governance 52

Data Governance Digital Transformation Metadata Data Warehouse 52

Andrew White

NOVEMBER 14, 2022

How is it that trying to be aware of all your data is the first step to working out what data (or analytic) actually needs to be governed first? Catalog are useful in a small number of data governance programs only. If not, a data catalog won’t be needed for at least 9 months. By documenting an organizations data issues.

Data Integration 52

Data Integration Technology Data Governance Analytics 52

Octopai

JUNE 1, 2020

Data mapping is the cornerstone of many important business intelligence processes: Data Integration – Even if systems are compatible, combining two disparate data repositories still requires meticulous data mapping.

Metadata 52

Metadata Data Warehouse Data Integration Enterprise 52

erwin

APRIL 4, 2022

While everyone will agree that added connectivity and expanded modeling techniques for NoSQL allows for increased collaboration, improved application quality, shorter development time, less re-work and increased support/documentation – the big news in this release is DevOps capability. DevOps GitHub integration via Mart.

Modeling 52

Modeling Data Processing Data-driven Big Data 52

erwin

MAY 14, 2021

Accelerating the retrieval and analysis of data —so much of it unstructured—is vital to becoming a data-driven business that can effectively respond in real time to customers, partners, suppliers and other parties, and profit from these efforts. Business data is business data, with the main concerns being its veracity and value.

Modeling 80

Modeling Digital Transformation Data-driven Optimization 80

FineReport

APRIL 26, 2023

MongoDB MongoDB, which gained popularity in 2010, is a NoSQL document-oriented database platform that is free and open-source. It uses collections and documents to store a high volume of data, with documents consisting of key-value pairs as the basic unit. It supports master-slave replication for data backup.

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 84

Metadata Data Governance Digital Transformation Data Quality 84

Ontotext

MARCH 8, 2023

However, this information is typically stored in disparate locations, often hidden within departmental documents or applications. Use Case #4: Financial Risk Detection and Prediction The financial industry is made up of a network of markets and transactions. Which financial institutions have filed similar risk compliance issues?

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Jet Global

MAY 1, 2020

In Gartner’s report, an analyst goes to great pains to say that there is “much more risk associated to non-technology issues than there is to deploying the infrastructure, tools, and apps.”. We can almost guarantee you different results from each, and you end up with no data integrity whatsoever. Risk to the business.

Data Warehouse 64

Data Warehouse Data Quality Risk Reporting 64

Ontotext

NOVEMBER 2, 2023

Achieving this advantage is dependent on their ability to capture, connect, integrate, and convert data into insight for business decisions and processes. This is the goal of a “data-driven” organization. We call this the “ Bad Data Tax ”.

IT 69

IT Cost-Benefit Data-driven Technology 69

Smart Data Collective

SEPTEMBER 26, 2019

As the name suggests, Big Data deals with huge data volumes mainly generated from various different sources like business processes, machines, social media platforms, human interactions, networks, etc. Role of Software Development in Big Data. So, how can software development helps you take advantage of Big Data?

Big Data 80

Big Data Software Unstructured Data Data Integration 80

Cloudera

AUGUST 31, 2021

The centralized complete views of verified and data-quality validated source system data within the Data Fabric helped the client streamline both security and data integration efforts across their internal application footprint. This document is produced by consultants at Accenture as general guidance.

Data Warehouse 82

Data Warehouse Cost-Benefit Metadata Data-driven 82

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

CIO Business Intelligence

JUNE 7, 2022

Because of this, only a small percentage of your AI team will work on data science efforts, he says. The rest of the team will identify the problem being solved, help explain the data, help organize the data, integrate the output into another production system, or present the data in a presentation-ready manner.”.

Machine Learning 145

Machine Learning Data Science Consulting Metrics 145

IBM Big Data Hub

JANUARY 9, 2024

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. This may also entail working with new data through methods like web scraping or uploading.

Enterprise 80

Enterprise Data Integration Data Quality Contextual Data 80

O'Reilly on Data

MARCH 20, 2019

Like many others, I’ve known for some time that machine learning models themselves could pose security risks. Data integrity constraints: Many databases don’t allow for strange or unrealistic combinations of input variables and this could potentially thwart watermarking attacks. Disparate impact analysis: see section 1.

Modeling 221

Modeling Machine Learning Predictive Modeling Consulting 221

Jet Global

OCTOBER 7, 2020

There are five categories of data entities based on their functions and the type of data that they serve: Parameter (e.g., Document (e.g., When it comes to using data entities, you have two choices: out-of-the-box or custom data entities. General Ledger). Reference (e.g., Tax Codes). Master (e.g., Customers).

Data Warehouse 90

Data Warehouse OLAP Reporting Finance 90

Ontotext

FEBRUARY 26, 2024

The answers to these foundational questions help you uncover opportunities and detect risks. We bundle these events under the collective term “Risk and Opportunity Events” This post is part of Ontotext’s AI-in-Action initiative aimed to empower data, scientists, architects and engineers to leverage LLMs and other AI models.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

IBM Big Data Hub

JANUARY 17, 2024

Strong encryption algorithms like AES effectively transform plaintext into ciphertext, ensuring that even if an unauthorized party gains access, they won’t be able to decrypt sensitive data without access to the authorized users’ encryption key. Data integrity Cryptography is also used to ensure the integrity of data.

Data Integration 91

Data Integration Management Consulting Dashboards 91