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: .

Risk 64

Risk Modeling Machine Learning Data Quality 64

Octopai

MARCH 23, 2022

If today, federal inspections and authorizations of meatpacking plants are made with some level of reliance on data systems (and they almost certainly are), then dirty data could even lead directly to dirty meat. Data lineage tools give you exactly that kind of transparent, x-ray vision into your data quality.

Data Quality 91

Data Quality Reporting Modeling Interactive 91

Cloudera

JULY 15, 2021

We recently hosted a roundtable focused on o ptimizing risk and exposure management with data insights. 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. .

Risk 97

Risk Optimization Management Insurance 97

Jim Harris

JANUARY 1, 2018

The Chicken Littles of Data Quality use sound bites like “data quality problems cost businesses more than $600 billion a year!” or “poor data quality costs organizations 35% of their revenue!” Furthermore, the reason that citing specific examples of poor data quality (e.g.,

Data Quality 77

Data Quality Cost-Benefit Consulting Dashboards 77

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.

Risk 52

Risk Modeling Metrics Business Objectives 52

Jim Harris

JANUARY 1, 2016

1 — Investigate Data quality is not exactly a riddle wrapped in a mystery inside an enigma. However, understanding your data is essential to using it effectively and improving its quality. In order for you to make sense of those data elements, you require business context.

Data Quality 61

Data Quality Data Governance ROI Cost-Benefit 61

DataKitchen

MARCH 12, 2024

Our vision was to create a flexible, state-of-the-art data infrastructure that would allow our analysts to transform the data rapidly with a very low risk of error. After working with DataKitchen for a while, we noticed almost an absolute absence of data errors we didn’t catch earlier. That was amazing for the team.”

Metrics 120

Metrics Software Cost-Benefit Testing 120

CIO Business Intelligence

APRIL 16, 2024

With the emergence of GenAI capabilities, fast-tracking digital transformation deployments are likely to change manufacturing as we know it, creating an expanding chasm of leaders versus followers, the latter of which will risk obsolescence. However, despite the benefits of GenAI, there are some areas of risk. Data quality dependence.

Manufacturing 98

Manufacturing Digital Transformation Risk Dashboards 98

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.

Consulting 142

Consulting Uncertainty Data Lake Risk 142

Alation

SEPTEMBER 7, 2021

In this blog we will discuss how Alation helps minimize risk with active data governance. Now that you have empowered data scientists and analysts to access the Snowflake Data Cloud and speed their modeling and analysis, you need to bolster the effectiveness of your governance models. Find Trusted Data.

Data Governance 59

Data Governance Risk Data Quality Dashboards 59

Smart Data Collective

JUNE 19, 2019

The Relationship between Big Data and Risk Management. Big data calls for complex processing, handling, and storage system, which may include elements such as human beings, computers, and the internet. Tips for Improving Risk Management When Handling Big Data. Risk Management Applications for Analyzing Big Data.

Risk Management 69

Risk Management Risk Management Big Data 69

DataKitchen

FEBRUARY 27, 2024

However, the foundation of their success rests not just on sophisticated algorithms or computational power but on the quality and integrity of the data they are trained on and interact with. The Imperative of Data Quality Validation Testing Data quality validation testing is not just a best practice; it’s imperative.

Data Quality 120

Data Quality Unstructured Data Testing Interactive 120

DataKitchen

MARCH 5, 2024

The Stakeholder Confidence Crisis Relying on hope as a data accuracy and integrity strategy is fraught with risks. Stakeholders, from internal teams to external clients, seek confidence in the data they use and depend on. You will be in trouble if you are not measuring data quality or delivering error rates and SLAs.

Data Quality 150

Data Quality Measurement Testing Data-driven 150

CIO Business Intelligence

APRIL 8, 2024

Working with partner Amazon Web Services (AWS), the NFL has developed Digital Athlete, a platform that uses computer vision and ML to predict which players are at the highest risk of injury based on plays and their body positions. Souza’s advice: Cultivate curiosity. “One

Analytics 117

Analytics Broadcasting Predictive Analytics Metrics 117

CIO Business Intelligence

DECEMBER 8, 2023

Minimizing risk Many companies are starting to invest in mitigating risk. Continually upgrade data quality. When training LLMs, IT teams should validate the data to ensure it is high quality, relevant, and comprehensive. For more insights about innovating with AI, while minimizing the risks, visit The Works.

Risk 122

Risk Finance Data Quality Modeling 122

CIO Business Intelligence

JANUARY 9, 2024

[Our firm’s leaders] wanted to make sure there were guidelines in place to protect the company, its data, and its people.” To get past those points, Colisto worked to educate leaders about the capabilities and risks of AI, seeking to move the company from “no to know.” What is our appetite for risk and how do we address it?

IT 140

IT Risk Cost-Benefit Enterprise 140

Octopai

MAY 15, 2023

Data lineage helps identify redundant or inefficient data integration steps, allowing organizations to streamline their processes and reduce development, maintenance, and operational costs associated with ETL. Data quality costs: Poor data quality can result in significant costs for organizations.

Data Quality 52

Data Quality Data Governance Data Integration Optimization 52

IBM Big Data Hub

NOVEMBER 29, 2023

To build that trust and drive broad adoption, vendors of synthetic data generation tools will need to address two critical questions that many business leaders ask: Will synthetic data expose my business to additional data privacy risks? How accurately does synthetic data reflect my existing data?

Metrics 86

Metrics Machine Learning Risk Statistics 86

Laminar Security

DECEMBER 13, 2023

Close to 70% of respondents in an ISC report indicated that they believe their organization lacks requisite cybersecurity staff to handle cloud data risk effectively. Learn in this article how Laminar harnesses AI for data discovery and classification and reduces public cloud data risks.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

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

DataKitchen

OCTOBER 20, 2023

Deploying a Data Journey Instance unique to each customer’s payload is vital to fill this gap. Such an instance answers the critical question of ‘Dude, Where is my data?’ ’ while maintaining operational efficiency and ensuring data quality—thus preserving customer satisfaction and the team’s credibility.

Insurance 176

Insurance Metadata Data-driven Data Quality 176

CIO Business Intelligence

MAY 2, 2023

The risk of derailments increases as I hear inconsistent answers or too many conflicting priorities. But there are common pitfalls , such as selecting the wrong KPIs , monitoring too many metrics, or not addressing poor data quality. The five derailments I focus on here fall within the CIO’s responsibilities to address.

Digital Transformation 141

Digital Transformation IT Data-driven Data Quality 141

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 Predictive Modeling Data-driven 52

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

CIO Business Intelligence

DECEMBER 19, 2023

Third, in the CDO Agenda: 2024: Navigating Data and Generative AI Frontiers , 57% of respondents haven’t changed their data environments to support generative AI. CIOs should look for other operational and risk management practices to complement transformation programs.

Digital Transformation 143

Digital Transformation Unstructured Data Machine Learning Risk Management 143

CIO Business Intelligence

JANUARY 24, 2024

At Vanguard, “data and analytics enable us to fulfill on our mission to provide investors with the best chance for investment success by enabling us to glean actionable insights to drive personalized client experiences, scale advice, optimize investment and business operations, and reduce risk,” Swann says.

Data Strategy 136

Data Strategy Strategy Unstructured Data Data Governance 136

CIO Business Intelligence

FEBRUARY 8, 2024

Developers need tools and resources to produce quality, safe, and secure products, while enterprises need to trust the tools and resources to not introduce risk to their business. AIs are more than the base knowledge or underlying “data” layer of the LLM or its “experience” layer.

Experimentation 119

Experimentation Cost-Benefit Interactive Risk 119

AWS Big Data

FEBRUARY 29, 2024

Implement data privacy policies. Implement data quality by data type and source. Let’s look at some of the key changes in the data pipelines namely, data cataloging, data quality, and vector embedding security in more detail. Link structured and unstructured datasets.

Data Governance 97

Data Governance Unstructured Data Metadata Data Lake 97

datapine

MAY 31, 2022

Like many other branches of technology, security is a pressing concern in the world of cloud-based computing, as you are unable to see the exact location where your data is stored or being processed. This increases the risks that can arise during the implementation or management process. Cost management and containment. Compliance.

Risk 237

Risk Cost-Benefit Business Intelligence Data-driven 237

Smart Data Collective

SEPTEMBER 28, 2022

One of the most important is that it helps to increase the reliability of your data. Data quality issues can arise from a variety of sources, including: Duplicate records Missing records Incorrect data. The proper use of data management solutions can help you identify these problems and correct them quickly and easily.

Big Data 101

Big Data Management Cost-Benefit Data Quality 101

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

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

erwin

JANUARY 11, 2019

It’s only when companies take their first stab at manually cataloging and documenting operational systems, processes and the associated data, both at rest and in motion, that they realize how time-consuming the entire data prepping and mapping effort is, and why that work is sure to be compounded by human error and data quality issues.

Data Governance 40

Data Governance Risk Metadata Management 40

Octopai

JUNE 22, 2023

However, self-service data management also comes with risks, including data inconsistencies, lack of governance, and potential security vulnerabilities. On the other hand, centralized data management emphasizes a more structured and governed approach.

Management 52

Management Data-driven Data Quality Data Governance 52

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

CIO Business Intelligence

JUNE 9, 2023

A few years ago, Gartner found that “organizations estimate the average cost of poor data quality at $12.8 million per year.’” Beyond lost revenue, data quality issues can also result in wasted resources and a damaged reputation. Without it, organizations incur a significant opportunity cost.

Strategy 116

Strategy Management Key Performance Indicator ROI 116

O'Reilly on Data

APRIL 19, 2021

The biggest problems in this year’s survey are lack of skilled people and difficulty in hiring (19%) and data quality (18%). The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%). Bad data yields bad results at scale.

Enterprise 356

Enterprise Modeling Risk Manufacturing 356