CIO Business Intelligence

JULY 28, 2023

Data privacy is the control of data harvested, stored, utilized, and shared in compliance with data protection regulations and privacy best practices. Data privacy encompasses controlling data from unauthorized access, obtaining consent from data subjects as required, and ensuring data integrity.

Risk 98

Risk Data Governance Enterprise Data Integration 98

CIO Business Intelligence

FEBRUARY 26, 2025

The problem is that, before AI agents can be integrated into a companys infrastructure, that infrastructure must be brought up to modern standards. In addition, because they require access to multiple data sources, there are data integration hurdles and added complexities of ensuring security and compliance.

IT 141

IT Enterprise Interactive Data Quality 141

CIO Business Intelligence

NOVEMBER 18, 2024

According to the study, key areas where banks are currently focusing on gen AI include: Transactional use cases: Three out of five (61%) banks use the technology for transactional use cases such as credit analysis, portfolio management, risk assessment, legal contracts, offers, tenders, and pitch documents.

Digital Transformation 141

Digital Transformation Consulting Cost-Benefit Marketing 141

Business Over Broadway

SEPTEMBER 27, 2018

Data silos in business are good examples of these types of data sets. pharmacogenomics) and risk assessment of genetic disorders (e.g., Data Integration as your Customer Genome Project. Data Integration is an exercise in creating your customer genome. genetic counseling, genetic testing). Summary.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

IBM Big Data Hub

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. In short, yes.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

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. 9] See: Teach/Me Data Analysis. [10] Sensitivity analysis.

Machine Learning 282

Machine Learning Modeling Testing Risk Management 282

AWS Big Data

DECEMBER 23, 2024

About Fitch Group and their need for multi-region resiliency As a leading global financial information services provider, Fitch Group delivers vital credit and risk insights, robust data, and dynamic tools to champion more efficient, transparent financial markets.

Data-driven 95

Data-driven Management Risk Big Data 95

AWS Big Data

JANUARY 9, 2025

Simplified data corrections and updates Iceberg enhances data management for quants in capital markets through its robust insert, delete, and update capabilities. These features allow efficient data corrections, gap-filling in time series, and historical data updates without disrupting ongoing analyses or compromising data integrity.

Metadata 111

Metadata Snapshot Cost-Benefit Optimization 111

Data Virtualization

FEBRUARY 15, 2024

Reading Time: 3 minutes Often, our fascination with technology leads us to focus on the details of our work rather than its essence; we risk seeing technology not as a means to an end but as the goal, and this is especially true when.

Data Integration 59

Data Integration Technology Risk Management 59

David Menninger's Analyst Perspectives

DECEMBER 31, 2024

Integrating with various data sources is crucial for enhancing the capabilities of automation platforms , allowing enterprises to derive actionable insights from all available datasets. This ability facilitates breaking down silos between departments and fosters a collaborative approach to data use.

Cost-Benefit 130

Cost-Benefit Data-driven Enterprise Business Objectives 130

CIO Business Intelligence

JUNE 5, 2023

Though we know who’s paying your income taxes this April (sorry to rub it in: it’s you), we have to ask: Who’s paying your data integration tax? Data integration tax is a term used to describe the hidden costs associated with integrating data solutions to process your data from disparate sources and for different needs.

Data Integration 52

Data Integration ROI Risk Machine Learning 52

CIO Business Intelligence

NOVEMBER 19, 2024

However, as model training becomes more advanced and the need increases for ever more data to train, these problems will be magnified. As the next generation of AI training and fine-tuning workloads takes shape, limits to existing infrastructure will risk slowing innovation. Seamless data integration.

Management 116

Management Unstructured Data Deep Learning Metadata 116

DataKitchen

NOVEMBER 5, 2024

As data ingestion transitions to a continuous flow, the second part of DQ training equips engineers to monitor schema consistency, row counts, and data freshness, ensuring data integrity over time.

Data Quality 195

Data Quality Testing Metrics Reporting 195

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 278

Modeling Machine Learning Predictive Modeling Consulting 278

CIO Business Intelligence

OCTOBER 9, 2024

However, this enthusiasm may be tempered by a host of challenges and risks stemming from scaling GenAI. As the technology subsists on data, customer trust and their confidential information are at stake—and enterprises cannot afford to overlook its pitfalls. That’s why many enterprises are adopting a two-pronged approach to GenAI.

Strategy 143

Strategy Modeling Management Data Lake 143

O'Reilly on Data

FEBRUARY 11, 2019

Data lineage, data catalog, and data governance solutions can increase usage of data systems by enhancing trustworthiness of data. Moving forward, tracking data provenance is going to be important for security, compliance, and for auditing and debugging ML systems. Data Platforms.

Big Data 271

Big Data Technology Machine Learning Deep Learning 271

CIO Business Intelligence

NOVEMBER 13, 2024

By automating data profiling and validation, it minimizes errors and maintains data integrity throughout the migration. Advanced algorithms and generative AI systematically check data for accuracy and completeness, catching inconsistencies that might otherwise slip through the cracks.

Cost-Benefit 64

Cost-Benefit Data-driven Data Integration Data Quality 64

Smart Data Collective

MAY 23, 2021

Client Risk Profile Categorization. The financial status of an individual is revealed in their risk profile. Artificial intelligence systems help in the automation of profiling of customers and can seek easily which are risk profile. As a result, years of human searching work in hundreds of record rooms have been replaced.

Insurance 137

Insurance Machine Learning Cost-Benefit Data-driven 137

CIO Business Intelligence

APRIL 1, 2024

Deloitte 2 meanwhile found that 41% of business and technology leaders said a lack of talent, governance, and risks are barriers to broader GenAI adoption. Data preparation, including anonymizing, labeling, and normalizing data across sources, is key. Low-cost proof-of-concepts can help you reduce the risk of overprovisioning.

Uncertainty 142

Uncertainty Data Lake Consulting Risk 142

Smart Data Collective

JANUARY 21, 2021

Therefore, to mitigate the risk of losing essential data forever in a data breach or other crisis, every competitive firm should work on its backup strategy to keep such information safe from violation. This is one of the biggest causes of data breaches. What Are the Potential Risks for Your Business?

Strategy 141

Strategy Risk Software Enterprise 141

CIO Business Intelligence

SEPTEMBER 23, 2024

For sectors such as industrial manufacturing and energy distribution, metering, and storage, embracing artificial intelligence (AI) and generative AI (GenAI) along with real-time data analytics, instrumentation, automation, and other advanced technologies is the key to meeting the demands of an evolving marketplace, but it’s not without risks.

Manufacturing 122

Manufacturing Internet of Things Metadata Risk 122

CIO Business Intelligence

MARCH 19, 2025

However, embedding ESG into an enterprise data strategy doesnt have to start as a C-suite directive. Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams.

IT 59

IT Data Governance Data-driven Metrics 59

Smart Data Collective

JUNE 1, 2022

The International Association of Privacy Professionals reports that there were 1,862 data breaches in 2021 alone. Organizations must make data security a top priority. Those that do not risk bankruptcy, as the costs of data breaches are horrifying. Rising Data Breaches Have Made Greater Data Security a Necessity.

Revenue Optimization 145

Revenue Optimization Measurement Marketing Interactive 145

CIO Business Intelligence

APRIL 29, 2022

Preparing for an artificial intelligence (AI)-fueled future, one where we can enjoy the clear benefits the technology brings while also the mitigating risks, requires more than one article. This first article emphasizes data as the ‘foundation-stone’ of AI-based initiatives. Establishing a Data Foundation. era is upon us.

Data Governance 145

Data Governance IT Data Lake Risk 145

AWS Big Data

DECEMBER 4, 2024

Our customers tell us that the fragmented nature of permissions and access controls, managed separately within individual data sources and tools, leads to inconsistent implementation and potential security risks.

Data Analytics 101

Data Analytics Analytics Data Lake Data Quality 101

CIO Business Intelligence

OCTOBER 22, 2024

The hybrid cloud factor A modicum of interoperability between public clouds may be achieved through network interconnects, APIs, or data integration between them, but “you probably won’t find too much of that unless it’s the identical application running in both clouds,” IDC’s Tiffany says.

Strategy 128

Strategy Cost-Benefit Risk Enterprise 128

CIO Business Intelligence

NOVEMBER 21, 2023

Ask IT leaders about their challenges with shadow IT, and most will cite the kinds of security, operational, and integration risks that give shadow IT its bad rep. That’s not to downplay the inherent risks of shadow IT. There may be times when department-specific data needs and tools are required.

IT 137

IT Risk Cost-Benefit Data Science 137

CIO Business Intelligence

MAY 15, 2024

Covered cyber incidents must be “substantial” and reflect certain scenarios affecting data integrity, confidentiality, or availability – such as a data breach where lots of customer data is stolen or a ransomware attack where corporate systems are locked up until a payment is made.

Reporting 119

Reporting Risk Data-driven Measurement 119

DataKitchen

OCTOBER 20, 2023

Example 2: The Data Engineering Team Has Many Small, Valuable Files Where They Need Individual Source File Tracking In a typical data processing workflow, tracking individual files as they progress through various stages—from file delivery to data ingestion—is crucial.

Insurance 169

Insurance Metadata Data-driven Data Quality 169

AWS Big Data

OCTOBER 11, 2024

Snapshots play a critical role in providing the availability, integrity and ability to recover data in OpenSearch Service domains. By implementing a robust snapshot strategy, you can mitigate risks associated with data loss, streamline disaster recovery processes and maintain compliance with data management best practices.

Snapshot 110

Snapshot Dashboards Management Testing 110

CIO Business Intelligence

JANUARY 11, 2024

“Our internal data and adherence to process is where our focus is, and we don’t necessarily want to leap ahead until we feel like we have a stable footing there.” Ensuring data integrity is part of a broader governance approach organizations will require to deploy and manage AI responsibly.

Enterprise 128

Enterprise Uncertainty Reporting Risk Management 128

CIO Business Intelligence

MARCH 19, 2025

These include improvements to operational efficiency (56%), bolstering risk management (53%), and elevating decision-making (51%). Of those top motivators, 85% of respondents said they were focused on business optimization, driven by a desire to boost operational efficiency or improve their risk management.

Analytics 52

Analytics Enterprise Risk Management Strategy 52

CIO Business Intelligence

DECEMBER 4, 2022

Process – Developing, communicating and enforcing cybersecurity policy with alignments to enterprise risk management prioritisation and remediation. Technology – Leveraging telemetry data integration and machine learning to gain full cyber risk visibility for action.

Risk Management 104

Risk Management Risk Machine Learning Strategy 104

Cloudera

FEBRUARY 15, 2024

While there are clear reasons SVB collapsed, which can be reviewed here , my purpose in this post isn’t to rehash the past but to present some of the regulatory and compliance challenges financial (and to some degree insurance) institutions face and how data plays a role in mitigating and managing risk.

Insurance 88

Insurance Data Architecture Risk Management Risk 88

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.

Data Governance 142

Data Governance Management Metadata Data Quality 142

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

Cloudera

JUNE 1, 2021

In summary, the next chapter for Cloudera will allow us to concentrate our efforts on strategic business opportunities and take thoughtful risks that help accelerate growth. Datacoral powers fast and easy data transformations for any type of data via a robust multi-tenant SaaS architecture that runs in AWS.

Uncertainty 144

Uncertainty Cost-Benefit Risk Strategy 144