IBM Big Data Hub

FEBRUARY 28, 2024

Successful strategic sourcing often results in process optimization, cost management, customer satisfaction, risk management , increased sustainability and other benefits. Sourcing teams are automating processes like data analysis as well as supplier relationship management and transaction management.

Strategy 102

Strategy Technology Cost-Benefit Risk Management 102

IBM Big Data Hub

APRIL 20, 2023

When consumers lose trust in a bank’s ability to manage risk, the system stops working. Banks and their employees place trust in their risk models to help ensure the bank maintains liquidity even in the worst of times. This can ensure that the decisions made are reliable and of high quality.

Risk 71

Risk Risk Management Reporting Metadata 71

FineReport

APRIL 9, 2024

Moreover, BI platforms provide the means for organizations to harness their data assets effectively, leading to improved customer satisfaction through personalized services and targeted marketing initiatives. 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

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

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. Strengthen data security. How erwin Can Help.

Data Governance 110

Data Governance Management Metadata Risk Management 110

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 57

Metadata Data Quality Data-driven Data Governance 57

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

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

Alation

AUGUST 17, 2022

We’ve seen the emergence of new, less invasive approaches, yet the question remains: What do CIOs think is most required to deliver a successful data governance program? The Risks of Early Data Governance Programs. The biggest risk was a lack of ownership. If your definitions are bad, so is your governance/risk/security.

Data Governance 92

Data Governance Risk Data-driven Measurement 92

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 190

Machine Learning Modeling Testing Risk Management 190

Ontotext

SEPTEMBER 1, 2023

They run the risk of using trademarked, copyrighted, or protected data as they scour public data and can be easily exploited and manipulated to ignore previous instructions. Additionally, data is the fulcrum of AI, and the data used to train LLMs must be properly governed and controlled.

Risk 52

Risk Modeling Data Quality Data Governance 52

IBM Big Data Hub

SEPTEMBER 15, 2023

These requirements will likely mandate publicly traded companies to disclose their greenhouse gas (GHG) emissions footprint, climate-related goals, and progress, as well as climate-risk related financial impact and expenditures. Furthermore, companies would need to disclose the price and rationale for internal carbon prices.

Risk 77

Risk Reporting Data-driven Publishing 77

Octopai

JANUARY 19, 2020

BCBS 239 is a document published by that committee entitled, Principles for Effective Risk Data Aggregation and Risk Reporting. The document, first published in 2013, outlines best practices for global and domestic banks to identify, manage, and report risks, including credit, market, liquidity, and operational risks.

Metadata 61

Metadata Risk Management Business Intelligence Data Governance 61

IBM Big Data Hub

AUGUST 15, 2022

To help data scientists reflect and identify possible ethical concerns the standard process for data mining should include 3 additional steps: data risk assessment, model risk assessment and production monitoring. Data risk assessment. Model risk management.

Data mining 62

Data mining Risk Modeling Risk Management 62

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

Ontotext

JULY 6, 2023

Let’s consider an example about risk and opportunity event detection. Case studies The risk and opportunity event detection use case discussed above combines all of Ontotext’s capabilities: storing and managing large amounts of data adding meaning to it (e.g.,, The solution brings many business benefits.

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

CIO Business Intelligence

APRIL 28, 2022

Right from the start, auxmoney leveraged cloud-enabled analytics for its unique risk models and digital processes to further its mission. Particularly in Asia Pacific , revenues for big data and analytics solutions providers hit US$22.6bn in 2020 , with financial services companies ranking among their biggest clients.

Analytics 85

Analytics Insurance Big Data Uncertainty 85

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

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. Manage Regulation, Manage Risk. The companies in the financial sector have to operate in one of the most regulated business domains.

Metadata 59

Metadata Finance Cost-Benefit Risk 59

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

Cloudera

JUNE 29, 2023

To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure. To improve the way they model and manage risk, institutions must modernize their data management and data governance practices.

Data Architecture 83

Data Architecture Risk Management Risk Management 83

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 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 114

Digital Transformation Strategy Data-driven Data Strategy 114

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

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

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

FineReport

MARCH 24, 2024

Their role extends to managing information for corporate decision-making, improving reporting systems , and performing complex analyses. Additionally, they facilitate organizational risk assessments, provide consulting services to leadership, and mentor junior analysts. JPMorgan Chase & Co.:

Statistics 52

Statistics Data mining Visualization Reporting 52

CIO Business Intelligence

AUGUST 7, 2023

Organizations big and small, across every industry, need to manage IT risk. based IT directors and vice presidents in companies with more than 1,000 employees to determine what keeps them up at night—and it comes as no surprise that one of their biggest nightmares is managing IT risk. trillion annually by 2025.

Risk 89

Risk IT Management Risk Management 89

Alation

JUNE 14, 2021

Do we know the business outcomes tied to data risk management? Once you have data classification then you can talk about whether you need to tokenize and why, or anonymize and why, or encrypt and why, etc.” These are essential to enabling a more rapid process of sensitive data discovery. What am I required to do?

Data Governance 97

Data Governance Recreation/Entertainment Data Lake Digital Transformation 97

Cloudera

FEBRUARY 14, 2018

operations, and our CISO’s team while we invest in and form a stronger data and analytics team. their customer insights, connected products, risk mitigation). Mitigate risk to Cloudera’s business – for example in the area of Cybersecurity, and to ensure compliance with regulatory and consumer privacy policies and concerns.

Data-driven 44

Data-driven Data Quality Risk Interactive 44

Sisense

SEPTEMBER 3, 2020

Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization. Data analysts in one organization might be called data scientists or statisticians in another. See an example: Explore Dashboard.

Statistics 97

Statistics Metrics Visualization Finance 97

Smart Data Collective

SEPTEMBER 25, 2019

However, according to a 2018 North American report published by Shred-It, the majority of business leaders believe data breach risks are higher when people work remotely. Whether you work remotely all the time or just occasionally, data encryption helps you stop information from falling into the wrong hands.

Technology 84

Technology Statistics Strategy Insurance 84

Cloudera

SEPTEMBER 7, 2023

But it’s also fraught with risk. This June, for example, the European Union (EU) passed the world’s first regulatory framework for AI, the AI Act , which categorizes AI applications into “banned practices,” “high-risk systems,” and “other AI systems,” with stringent assessment requirements for “high-risk” AI systems.

Insurance 79

Insurance Risk Data-driven Modeling 79

TDAN

JUNE 2, 2020

As I am writing this, countries around the world are fighting a global pandemic. This is the first global pandemic of the modern information age. There have been a number of epidemics in recent years, such as Ebola, but they (thankfully) have been regional in nature. This time around, we have the power of AI, […].

Risk Management 85

Risk Management Data Quality Risk Data Strategy 85

erwin

FEBRUARY 4, 2021

However, organizations still encounter a number of bottlenecks that may hold them back from fully realizing the value of their data in producing timely and relevant business insights. Overcoming Data Governance Bottlenecks. Put data quality first : Users must have confidence in the data they use for analytics.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

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. For a credit risk model, the target could be defined as “fully repays loan” or “payments in first 2 years are current” or or “collateral is repossessed.”. Define project scope.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98