Jet Global

AUGUST 28, 2023

We are eager to offer this additional support to insightsoftware customers, advancing our own capabilities that improve data quality and visibility, and enhance performance.” “This acquisition seamlessly connects insightsoftware’s expertise and product offerings with our extensive visualization library.

Dashboards 75

Dashboards Visualization Finance Data Quality 75

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. Informatica Axon Informatica Axon is a collection hub and data marketplace for supporting programs.

Data Governance 142

Data Governance Management Metadata Data Quality 142

IBM Big Data Hub

NOVEMBER 28, 2022

Provide early indicators of data quality. Poor data quality is one of the top barriers faced by organizations aspiring to be data-driven. Most data quality management approaches are reactive, triggered only when consumers complain to data teams about the integrity of datasets.

Metadata 56

Metadata Data Quality Data-driven Data Governance 56

IBM Big Data Hub

APRIL 20, 2023

This level of visibility also helps ensure that changes made over time don’t introduce new risks into the organization, can make it easier for banks to stay within regulatory guidelines, and helps ensure banks can respond quickly to changing business needs.

Risk 70

Risk Risk Management Reporting Metadata 70

erwin

APRIL 29, 2020

Regulatory compliance places greater transparency demands on firms when it comes to tracing and auditing data. For example, capital markets trading firms must understand their data’s origins and history to support risk management, data governance and reporting for various regulations such as BCBS 239 and MiFID II.

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

CIO Business Intelligence

MARCH 18, 2024

Similarly, Nafde put together an AI governance team of some two dozen people led by Webster Bank’s chief enterprise architect and chief data officer that includes technologists, risk and compliance staff, and lawyers. A key focus of the bank’s AI team is likewise data quality.

Risk 130

Risk Cost-Benefit Data Processing Testing 130

erwin

MARCH 25, 2021

Risk Management and Regulatory Compliance. Risk management, specifically around regulatory compliance, is an important use case to demonstrate the true value of data governance. According to Pörschmann, risk management asks two main questions. How likely is a specific event to happen? “You

Data Governance 109

Data Governance IT Cost-Benefit Risk 109

erwin

JULY 12, 2019

It also helps enterprises put these strategic capabilities into action by: Understanding their business, technology and data architectures and their inter-relationships, aligning them with their goals and defining the people, processes and technologies required to achieve compliance. How erwin Can Help.

Data Governance 110

Data Governance Management Metadata Risk Management 110

FineReport

APRIL 9, 2024

Financial Services Optimization : In the financial services sector, a major institution leveraged a sophisticated BI platform to analyze market trends, customer behavior, and risk management strategies. This framework ensures that data remains accurate, consistent, and secure across all levels of the organization.

Business Intelligence 52

Business Intelligence Software Data-driven Predictive Modeling 52

erwin

APRIL 4, 2019

Some other common data governance obstacles include: Questions about where to begin and how to prioritize which data streams to govern first. Issues regarding data quality and ownership. Concerns about data lineage. Competing project and resources (time, people and funding).

Data Governance 75

Data Governance Data Quality Risk Management Data-driven 75

CIO Business Intelligence

MAY 15, 2024

This should fall under the responsibility of a company’s risk management team, she says, and the person who makes sure that developers, and the business as a whole, understands there’s a process is the CIO. They’re the ones best positioned to set the culture,” she says.

Modeling 135

Modeling Risk Software Enterprise 135

Cloudera

SEPTEMBER 7, 2023

The way to manage this is by embedding data integration, data quality-monitoring, and other capabilities into the data platform itself , allowing financial firms to streamline these processes, and freeing them to focus on operationalizing AI solutions while promoting access to data, maintaining data quality, and ensuring compliance.

Insurance 81

Insurance Risk Data-driven Finance 81

CIO Business Intelligence

MARCH 24, 2023

Overcoming data challenges Despite their growing commitment to ESG, financial firms have learned the path to sustainability and prosperity can be rocky. “ESG ESG data quality is the biggest challenge. revenue growth from businesses showing a lower commitment to ESG.

Risk Management 52

Risk Management Consulting Metrics Data Quality 52

Cloudera

JULY 15, 2021

For financial institutions and insurers, risk and exposure management has always been a fundamental tenet of the business. Now, risk management has become exponentially complicated in multiple dimensions. . Attendees included senior risk managers and analytics experts from financial institutions and insurance companies.

Risk 99

Risk Optimization Management Insurance 99

datapine

FEBRUARY 22, 2022

Improved risk management: Another great benefit from implementing a strategy for BI is risk management. Clean data in, clean analytics out. Cleaning your data may not be quite as simple, but it will ensure the success of your BI. Indeed, every year low-quality data is estimated to cost over $9.7

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

erwin

OCTOBER 24, 2019

Addressing the Complexities of Metadata Management. The complexities of metadata management can be addressed with a strong data management strategy coupled with metadata management software to enable the data quality the business requires. With erwin, organizations can: 1.

Metadata 104

Metadata Management Data-driven Data Architecture 104

DataRobot Blog

JULY 21, 2022

Inquire whether there is sufficient data to support machine learning. Document assumptions and risks to develop a risk management strategy. Data aggregation such as from hourly to daily or from daily to weekly time steps may also be required. Perform data quality checks and develop procedures for handling issues.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. There are at least four major ways for data scientists to find bugs in ML models: sensitivity analysis, residual analysis, benchmark models, and ML security audits. Sensitivity analysis.

Machine Learning 187

Machine Learning Modeling Testing Risk Management 187

Octopai

JUNE 19, 2023

Everyone has access to the same data and the same understanding of what the data represents, reducing miscommunications and discrepancies. Catalogs also allow for better Risk Management; data catalogs help businesses maintain regulatory compliance by providing a clear record of what data is stored and how it’s used.

Metadata 88

Metadata Data Governance Measurement Risk Management 88

Octopai

JANUARY 19, 2020

It will not surprise you to learn all 11 of the bank-relevant principles are related to data in some form or fashion. Here’s a sampling: – Principle 1 covers data governance, including “a firm’s policies on data confidentiality, integrity, and availability, as well as risk-management policies.”.

Metadata 61

Metadata Risk Management Business Intelligence Data Governance 61

Alation

MARCH 14, 2023

Some business processes may need reviewing to include data analysis — even going as far as requiring specific data to make a business decision. Govern Data governance models should be flexible and dynamic while proactively addressing risk management and compliance with local and global regulations.

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

CIO Business Intelligence

APRIL 28, 2022

Its success is one of many instances illustrating how the financial services industry is quickly recognizing the benefits of data analytics and what it can offer, especially in terms of risk management automation, customized experiences, and personalization. .

Analytics 90

Analytics Insurance Big Data Uncertainty 90

CIO Business Intelligence

SEPTEMBER 2, 2022

As more financial companies embrace the cloud, there’s been an increase in demand for data engineers to help manage AWS and Azure services in the organization. Part of the role also includes continually improving the organization’s technology stack, while maintaining a priority for business continuity and risk management.

Finance 98

Finance IT Software Reporting 98

CIO Business Intelligence

AUGUST 31, 2022

As more financial companies embrace the cloud, there’s been an increase in demand for data engineers to help manage AWS and Azure services in the organization. Part of the role also includes continually improving the organization’s technology stack, while maintaining a priority for business continuity and risk management.

Finance 98

Finance IT Software Reporting 98

Ontotext

SEPTEMBER 1, 2023

Creating a g overnance team: This will add specific policy guidelines, train data engineers, data scientists, and data quality teams accordingly, and make sure data stewards enforce them. Leveraging the adoption framework, this team will help ensure proper data quality, security, and compliance.

Risk 52

Risk Modeling Data Quality Data Governance 52

Jet Global

MAY 9, 2024

ETL pipelines are commonly used in data warehousing and business intelligence environments, where data from multiple sources needs to be integrated, transformed, and stored for analysis and reporting. Data pipelines enable data integration from disparate healthcare systems, transforming and cleansing the data to improve data quality.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

IBM Big Data Hub

AUGUST 15, 2022

Model risk management. Again, data quality should be tracked by comparing the production data distribution to the data used to train the model. Often variables are correlated and bias can sneak in through one of the correlated fields, living as a proxy replacement in the model.

Data mining 60

Data mining Risk Modeling Risk Management 60

DataRobot

JULY 16, 2021

They all serve to answer the question, “How well can my model make predictions based on data?” In performance, the trust dimensions are the following: Data quality — the performance of any machine learning model is intimately tied to the data it was trained on and validated against.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

Alation

AUGUST 17, 2022

Data governance policy should be owned by the top of the organization so data governance is given appropriate attention — including defining what’s a potential risk and what is poor data quality.” It comes down to the question: What is the value of your data? Enterprise risk management.

Data Governance 92

Data Governance Risk Data-driven Measurement 92

Ontotext

APRIL 21, 2021

The problem was the left hand had no way of knowing the systemic issues around data governance, risk management and compliance framework. One of the biggest, and costly failures, was the inability to produce reports for management and regulatory agencies.

Metadata 59

Metadata Finance Cost-Benefit Data Quality 59

erwin

SEPTEMBER 17, 2020

Here are six benefits of automating end-to-end data lineage: Reduced Errors and Operational Costs. Data quality is crucial to every organization. Automated data capture can significantly reduce errors when compared to manual entry.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

IBM Big Data Hub

SEPTEMBER 15, 2023

Key challenges in meeting Sustainability disclosure requirements For most companies today, meeting this wide range of requirements will force accelerating the maturity level of ESG data quality, data management, and governance controls, as well as streamlining the ESG reporting processes to ensure auditability.

Risk 75

Risk Reporting Data-driven Publishing 75

Octopai

JULY 12, 2020

An enormous amount of time was being wasted performing manual searches, as the BI team needed to frequently comb through the enterprise data warehouse’s fields to determine how each was calculated or to find their sources. We now get the benefit of proliferated data quality.” – Andrew Stewardson, Data Risk Manager, FCSA.

Data Warehouse 58

Data Warehouse Data Quality Metadata Risk Management 58

CIO Business Intelligence

APRIL 29, 2022

From a policy perspective, the organization needs to mature beyond a basic awareness and definition of data compliance requirements (which typically holds that local operations make data “sovereign” by default) to a more refined, data-first model that incorporates corporate risk management, regulatory and reporting issues, and compliance frameworks.

Digital Transformation 121

Digital Transformation Strategy Data-driven Data Strategy 121

Ontotext

JULY 6, 2023

By promptly identifying and addressing risks, it enhances operational resiliency and enables proactive risk management. The solution also reduces incident response times, optimizes processes and streamlines asset management. First of all, it helps bridge the gap between business abstracts and technical realities.

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

O'Reilly on Data

JULY 15, 2019

After the 2008 financial crisis, the Federal Reserve issued a new set of guidelines governing models— SR 11-7 : Guidance on Model Risk Management. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk. Model risk management. AI projects in financial services and health care.

Machine Learning 37

Machine Learning Management Enterprise Risk Management 37

IBM Big Data Hub

DECEMBER 4, 2023

Clearly define the objective of the implementation project and determine its scope, timeline and budget as well as create a risk management plan. This is also the time to determine which data will be migrated, as some older data may be best stored in a secure archive.

Testing 75

Testing Consulting Digital Transformation Cost-Benefit 75