DataRobot Blog

JULY 21, 2022

Data scientists need to understand the business problem and the project scope to assess feasibility, set expectations, define metrics, and design project blueprints. If there is no forward-looking predictive component to the use case, it can probably be addressed with analytics and visualizations applied to historical data.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

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 94

Strategy Technology Cost-Benefit Risk Management 94

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. Database design is often an important part of the business analyst role.

Statistics 97

Statistics Metrics Visualization Finance 97

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

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

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 Machine Learning Data Quality Metrics 52

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

AWS Big Data

AUGUST 14, 2023

Goals of DPPM The goals of DPPM can be summarized as follows: Protect value – DPPM protects the value of the organizational data strategy by developing, implementing, and enforcing frameworks to measure the contribution of data products to organizational goals in objective terms. Monitoring and Event Management X X.

Management 98

Management Risk Measurement Optimization 98

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 71

Testing Consulting Digital Transformation Cost-Benefit 71

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

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.

Data Governance 52

Data Governance Data-driven Data Processing Metrics 52

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

Andrew White

JANUARY 11, 2021

What are you seeing as the differences between a Chief Analytics Officer and the Chief Data Officer? Value Management or monetization. Risk Management (most likely within context of governance). Product Management. Saul Judah is our main person focusing on D&A risk management. Governance.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

CIO Business Intelligence

MARCH 18, 2024

Our data team uses gen AI on Amazon cloud to explore sustainability metrics. In still another implementation, Covanta is using Salesforce’s CRM case management tool to create invoices and enable customers to talk directly to a Salesforce robot to answer any invoice questions.

Risk 133

Risk Cost-Benefit Data Processing Testing 133

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 Machine Learning Data-driven 66

datapine

AUGUST 29, 2022

Whether you are a complete novice or a seasoned BI professional, you will find here some books on data analytics that will help you cultivate your understanding of this essential field. Before we delve deeper into the best books for data analytics, here are three big data insights to put their relevance and importance into perspective.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Jet Global

MAY 9, 2024

Job schedulers help coordinate the pipeline’s different stages and manage dependencies between tasks. Monitoring can include tracking performance metrics such as execution time and resource usage, and logging errors or failures for troubleshooting and remediation. How is ELT different from ETL?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52