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

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

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

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

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

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

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. To learn more about how data impacts your ability to create trustworthy AI visit our website.

Data mining 60

Data mining Risk Modeling Risk Management 60

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

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

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

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 74

Risk Reporting Data-driven Publishing 74

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

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

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

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 74

Testing Consulting Digital Transformation Cost-Benefit 74

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

Alation

NOVEMBER 23, 2021

Organizations don’t need to spend a lot of money to get data governance. They can improve data quality, security and risk management without the need for an expensive big-bang project. What organizations actually govern is data-consumer behavior, and not the data itself. Subscribe to Alation's Blog.

Data Governance 52

Data Governance Data-driven Data Processing Consulting 52

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

Sisense

SEPTEMBER 3, 2020

We live in a constantly-evolving world of data. That means that jobs in data big data and data analytics abound. The wide variety of data titles can be dizzying and confusing! Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization.

Statistics 97

Statistics Metrics Visualization Finance 97

Cloudera

FEBRUARY 14, 2018

operations, and our CISO’s team while we invest in and form a stronger data and analytics team. On the other end of the spectrum, our Engineering and Support teams heavily use this data for our more transformative use cases in delivering predictive support to our customers. These are our most business transformative use cases.

Data-driven 44

Data-driven Data Quality Risk Interactive 44

DataRobot Blog

MAY 26, 2022

To start with, SR 11-7 lays out the criticality of model validation in an effective model risk management practice: Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses. Conclusion.

Risk 52

Risk Modeling Metrics Business Objectives 52

datapine

AUGUST 29, 2022

Additionally, numerous case studies on risk management, fraud detection, customer relationship management, and web analytics are included and described in detail. Topics covered here range from backtesting and benchmarking approaches to data quality issues, software tools, and model documentation practices.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

DataRobot Blog

DECEMBER 20, 2022

You can understand the data and model’s behavior at any time. Once you use a training dataset, and after the Exploratory Data Analysis, DataRobot flags any data quality issues and, if significant issues are spotlighted, will automatically handle them in the modeling stage. Rapid Modeling with DataRobot AutoML.

Management 59

Management Machine Learning Optimization Modeling 59

Cloudera

JANUARY 22, 2019

Cloudera enables high-value analytical use cases from the edge to AI including proactive and predictive maintenance, usage-based analytics for targeted communications, recommendation engines, Enterprise Risk Management, AML (Anti-Money Laundering), Fraud Detection/Prevention, Cybersecurity, and Machine Models.

Big Data 44

Big Data Data Lake Internet of Things Enterprise 44

Domino Data Lab

FEBRUARY 4, 2019

Eric’s article describes an approach to process for data science teams in a stark contrast to the risk management practices of Agile process, such as timeboxing. As the article explains, data science is set apart from other business functions by two fundamental aspects: Relatively low costs for exploration.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66