DataRobot Blog

MAY 10, 2022

Addressing the Key Mandates of a Modern Model Risk Management Framework (MRM) When Leveraging Machine Learning . The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States. To reference SR 11-7: .

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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.

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Risk Cost-Benefit Data Processing Testing 122

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.

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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.

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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.

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Ontotext

JULY 6, 2023

In today’s fast changing environment, enterprises that have transitioned from being focused on applications to becoming data-driven gain a significant competitive edge. Transaction and pricing data (e.g., Let’s consider an example about risk and opportunity event detection. Signals from unstructured content (e.g.,

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Ontotext

SEPTEMBER 1, 2023

Enterprises worldwide are analyzing Generative AI capabilities and seeking ways to leverage them for a variety of use cases to improve their competitive edge and incorporate automation and efficiency. To avoid this, enterprises should consider: A comprehensive data strategy: to align data and AI initiatives with business objectives.

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Ontotext

APRIL 21, 2021

Ontotext clients have used enterprise knowledge graphs to ensure inventory control is done in an intelligent and automated way. Inventory here refers to the vast amounts of information and data that financial institutions need on a daily basis, which are core assets to the business. Knowledge Graphs Make the Impossible, Simple.

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CIO Business Intelligence

APRIL 29, 2022

The data-first transformation journey can appear to be a lengthy one, but it’s possible to break it down into steps that are easier to digest and can help speed you along the pathway to achieving a modern, data-first organization. Key features of data-first leaders. 5x more likely to be highly resilient in terms of data loss.

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Octopai

JUNE 19, 2023

Data Catalogs also allow for Improved Collaboration by serving as a central repository for enterprise data, a data catalog facilitates collaboration among different teams. Everyone has access to the same data and the same understanding of what the data represents, reducing miscommunications and discrepancies.

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Metadata Data Governance Measurement Risk Management 88

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.

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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.”.

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Metadata Risk Management Business Intelligence Data Governance 61

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.

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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.

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Finance IT Software Reporting 98

erwin

SEPTEMBER 17, 2020

It’s paramount that organizations understand the benefits of automating end-to-end data lineage. Critically, it makes it easier to get a clear view of how information is created and flows into, across and outside an enterprise. The importance of end-to-end data lineage is widely understood and ignoring it is risky business.

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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.” Intelligent systems powered by machine learning are necessary for overcoming the challenges of data management.

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Sisense

SEPTEMBER 3, 2020

As you can imagine, data scientists with all these aptitudes can be of considerable value to their employers. They are the link between the data resources available to an enterprise and executives looking for opportunities to make the business better, faster, and stronger. See an example: Explore Dashboard.

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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.

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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.

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Risk Modeling Metrics Business Objectives 52

FineReport

MARCH 24, 2024

They analyze, interpret, and manipulate complex data, track key performance indicators, and present insights to management through reports and visualizations. Data analysts interpret data using statistical techniques, develop databases and data collection systems, and identify process improvement opportunities.

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Statistics Data mining Visualization Reporting 52

Smart Data Collective

SEPTEMBER 25, 2019

If you trust the data, it’s easier to use confidently to make business decisions. Statistics show that poor data quality is a primary reason why 40% of all business initiatives fail to achieve their targeted benefits. Only 13% said they did not have a strategy at all. Time to Take Action.

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Technology Statistics Strategy Insurance 86

erwin

FEBRUARY 4, 2021

While acknowledging that data governance is about more than risk management and regulatory compliance may indicate that companies are more confident in their data, the data governance practice is nonetheless growing in complexity because of more: Data to handle, much of it unstructured. Sources, like IoT.

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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.

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erwin

OCTOBER 24, 2019

Untapped data, if mined, represents tremendous potential for your organization. While there has been a lot of talk about big data over the years, the real hero in unlocking the value of enterprise data is metadata , or the data about the data. Addressing the Complexities of Metadata Management.

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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. Large projects or those involving text, images, or streaming data may need specialized infrastructure. Perform data quality checks and develop procedures for handling issues.

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Machine Learning Metrics Modeling Testing 98

erwin

APRIL 4, 2019

A strong data governance framework is central to the success of any data-driven organization because it ensures this valuable asset is properly maintained, protected and maximized. But despite this fact, enterprises often face push back when implementing a new data governance initiative or trying to mature an existing one.

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IBM Big Data Hub

DECEMBER 4, 2023

By implementing Oracle , one of the world’s leading enterprise resource planning (ERP) tools, organizations can transform their business processes and significantly increase operational efficiency. Companies large and small are increasingly digitizing and managing vast troves of data.

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Testing Consulting Digital Transformation Cost-Benefit 72

Octopai

JULY 12, 2020

Like many BI teams, FCSA’s struggles began with the massive amount of data that existed in their enterprise data warehouse. Perhaps most frustrating for FCSA was the major data-quality issues that arose due to manual, error-prone data processing. Think of the wonders it could work for your enterprise.

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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.

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Cloudera

JANUARY 22, 2019

In conjunction with the evolving data ecosystem are demands by business for reliable, trustworthy, up-to-date data to enable real-time actionable insights. Big Data Fabric has emerged in response to modern data ecosystem challenges facing today’s enterprises. What is Big Data Fabric? Source: Cloudera.

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datapine

AUGUST 29, 2022

However, once you start working with larger enterprise-level data sets with millions of rows and hundreds of columns of information, Excel may not be capable of handling such volumes. Topics covered here range from backtesting and benchmarking approaches to data quality issues, software tools, and model documentation practices.

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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

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Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

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. Creates Shared Processes.

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Data Governance Data-driven Data Processing Consulting 52

AWS Big Data

AUGUST 14, 2023

DPPM does not exist in a vacuum—by its nature, DPPM is closely related to and interdependent with enterprise architecture practices like business capability management and project portfolio management. DPPM itself may therefore also be considered, in part, an enterprise architecture practice.

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Andrew White

JANUARY 11, 2021

What is your vision for D&A for small and medium enterprises? We have specific research for midsize and small enterprises. See 3 Questions That Midsize Enterprises Should Ask About Data and Analytics and have an inquiry with Alan Duncan. Which industry, sector moves fast and successful with data-driven?

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Jet Global

MAY 9, 2024

Data pipelines can serve various purposes beyond ETL, such as real-time analytics, machine learning, and stream processing applications. ETL pipelines are designed to prepare data for analysis, reporting, or other business intelligence applications. What is an ETL pipeline? How is ELT different from ETL?

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