O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Smart Data Collective

SEPTEMBER 21, 2021

A growing number of businesses are starting to look for new data-driven approaches to streamline their business models. However, your data-driven business model won’t be very helpful if you don’t focus on the right metrics. Targeting the Right Variables for Your Data-Driven Retail Business Model.

Data-driven 108

Data-driven Metrics Sales Big Data 108

CIO Business Intelligence

MAY 13, 2024

IBM is betting big on its toolkit for monitoring generative AI and machine learning models, dubbed watsonx.governance , to take on rivals and position the offering as a top AI governance product, according to a senior executive at IBM. watsonx.governance is a toolkit for governing generative AI and machine learning models.

Risk 104

Risk Machine Learning Modeling Enterprise 104

IBM Big Data Hub

APRIL 19, 2024

Asset management and technological innovation Advancements in technology underpin the need for holistic grid asset management, making the assets in the grid smarter and equipping the workforce with smart tools. Cybersecurity reduces risk exposure for cyberattacks on digitally connected assets.

Management 94

Management Internet of Things Risk IoT 94

Domino Data Lab

AUGUST 10, 2021

Models are the central output of data science, and they have tremendous power to transform companies, industries, and society. At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. The process of creating models is called modeling.

Modeling 64

Modeling IT Risk Management Deep Learning 64

DataRobot Blog

DECEMBER 6, 2022

Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results. Taking a Multi-Tiered Approach to Model Risk Management. How Model Observability Provides a 360° View of Models in Production. Read the blog. Read the blog.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

CIO Business Intelligence

NOVEMBER 21, 2023

Ask IT leaders about their challenges with shadow IT, and most will cite the kinds of security, operational, and integration risks that give shadow IT its bad rep. That’s not to downplay the inherent risks of shadow IT.

IT 131

IT Risk Cost-Benefit Risk Management 131

Jet Global

APRIL 5, 2021

Enterprises actively use financial modeling to guide their financial planning and strategic decision-making. Financial models offer data-driven, quantitative analysis that tells you where your company stands and where it’s heading. That being said, one model can’t do it all. What Is Financial Modeling? Risk management.

Modeling 98

Modeling Forecasting Metrics Finance 98

IBM Big Data Hub

MARCH 4, 2024

While many organizations have implemented AI, the need to keep a competitive edge and foster business growth demands new approaches: simultaneously evolving AI strategies, showcasing their value, enhancing risk postures and adopting new engineering capabilities. Consider the following: Do you need a public foundation model?

Enterprise 107

Enterprise ROI Risk Strategy 107

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. Hollywood is a $10 billion-a-year industry, and movies range from huge hits to box office bombs.

Risk 67

Risk ROI Modeling Metrics 67

CIO Business Intelligence

JULY 13, 2022

Metrics that create a narrative and show how the business compares to competitors, the wider industry, and globally against all businesses give a clear picture that allows board members to set strategy. Burgess returns along with Atlassian Chief Trust Officer Adrian Ludwig to examine insider threats and third-party risk.

Strategy 119

Strategy Metrics Risk Risk Management 119

Smart Data Collective

SEPTEMBER 17, 2020

Here are a few of the advantages of Big Data in the banking and financial industry: Improvement in risk management operations. Big Data can efficiently enhance the ways firms utilize predictive models in the risk management discipline. Big Data provides financial and banking organizations with better risk coverage.

Big Data 142

Big Data Finance Risk Management Risk 142

IBM Big Data Hub

DECEMBER 13, 2023

By assessing and proactively managing risks inherent in the supply chain , organizations can shield themselves from disruptions and strengthen the resilience of their operations. This enables an effective and adaptive approach to sourcing that creates value and minimizes risk.

Strategy 73

Strategy Cost-Benefit Key Performance Indicator Consulting 73

CIO Business Intelligence

SEPTEMBER 20, 2023

Agile is an amazing risk management tool for managing uncertainty, but that’s not always obvious.” The key is recognizing that planning must be an agile discipline, not a standalone activity performed independently of agile teams. He recommends that leaders identify a metric that focuses on value to the customer.

Digital Transformation 126

Digital Transformation Uncertainty Metrics Business Objectives 126

CIO Business Intelligence

APRIL 13, 2023

Gartner projects that spending on information security and risk management products and services will grow 11.3% To better focus security spend, some chief information security officers (CISOs) are shifting their risk assessments from IT systems to the data, applications, and processes that keep the business going.

IT 111

IT Cost-Benefit Risk Consulting 111

Alation

AUGUST 17, 2022

The Risks of Early Data Governance Programs. The biggest risk was a lack of ownership. Too often, organizations don’t educate their users about data governance, security, and business risk,” claims analyst Jack Gold. If your definitions are bad, so is your governance/risk/security. It’s not just an IT problem.

Data Governance 92

Data Governance Risk Data-driven Measurement 92

FineReport

APRIL 9, 2024

From advanced analytics to predictive modeling, the evolving landscape of business intelligence is revolutionizing how data is processed and leveraged for actionable insights. The implementation empowered the organization with predictive modeling capabilities, enabling proactive risk mitigation and personalized customer engagement strategies.

Business Intelligence 52

Business Intelligence Software Data-driven Predictive Modeling 52

IBM Big Data Hub

JUNE 13, 2022

Beyond these common uses of AI, there are also uses that regulators are beginning to identify as areas where there may be a higher risk. Trustworthy outcomes are critical for all AI systems, particularly in high-risk contexts, and this is a key factor in why the market for responsible AI solutions is expected to double in size in 2022 [2].

Risk 98

Risk Risk Management Data-driven Metrics 98

IBM Big Data Hub

JUNE 29, 2023

By integrating financial planning with strategic and operational planning, organizations can evaluate financial profitability, identify potential gaps or risks, and make necessary adjustments to achieve financial targets. These tools provide comprehensive insights into key metrics and help identify trends, patterns, and opportunities.

Key Performance Indicator 83

Key Performance Indicator Forecasting Data Integration Sales 83

AWS Big Data

AUGUST 14, 2023

Taken together, the posts in this series lay out some possible operating models for data mesh within an organization. The same organizational characteristics that make data mesh architectures valuable, however, also put them at risk of turning into one of the twin nightmares of Kudzu or data deserts.

Management 76

Management Risk Measurement Optimization 76

AWS Big Data

APRIL 12, 2023

With it, we found an intuitive product with rich visualizations that we could build and grow with rapidly, allowing us to innovate without monetary risks or being locked in to cumbersome contracts. We considered other options, but they couldn’t support the licensing model that fit our needs.

Dashboards 60

Dashboards Risk Data-driven Visualization 60

Smart Data Collective

JUNE 3, 2021

Analytics tools enable companies to assess the performance of employees across various metrics and find ways to improve performance. Making better risk assessments. Risk management is one of the most important elements of financial planning. Analytics tools help companies develop better risk scoring models.

Management 91

Management Finance Analytics Analytics Technologies 91

Cloudera

AUGUST 25, 2020

One of the key takeaways from recent times that should be considered into the future, is that banks need to rethink how they look at tail risk or extreme events that rarely happen. . Machine Learning and AI provide powerful predictive engines that rely on historical data to fit the models.

Prescriptive Analytics 70

Prescriptive Analytics Technology Machine Learning Risk 70

CIO Business Intelligence

MARCH 9, 2023

Pricing models and metrics can also be complex, making it difficult to understand when additional costs might kick in, Alexander says. Deals done by IT departments alone provide leading-edge technology but often at high cost and legal risk. There may be alternative deal models that make sense.

Cost-Benefit 106

Cost-Benefit Risk Uncertainty Technology 106

IBM Big Data Hub

DECEMBER 15, 2023

There are various examples of sustainable business practices that companies can adopt to improve their business model. Sustainable businesses keep track of these metrics to be more proactive with their sustainability efforts. The company promotes the “ buy less, demand more ” motto throughout their business model.

Consulting 73

Consulting Manufacturing Strategy Risk Management 73

Corinium

APRIL 25, 2019

For example, P&C insurance strives to understand its customers and households better through data, to provide better customer service and anticipate insurance needs, as well as accurately measure risks. Life insurance needs accurate data on consumer health, age and other metrics of risk. Now, there is a data risk here.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

Jet Global

MAY 11, 2021

May 11, 2021 – In the early days of the pandemic, cash flow management took center stage for many businesses and risk management continues to be a priority this year as business leaders depend more than ever on finance teams for decision-making support. RALEIGH, N.C. – Finance Team’s Role & Challenges.

Finance 98

Finance Technology Forecasting Uncertainty 98

Sisense

SEPTEMBER 3, 2020

Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. This includes database modeling, metrics definition, dashboard design , and creating and publishing executive reports. Data Scientist.

Statistics 97

Statistics Metrics Visualization Finance 97

IBM Big Data Hub

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data.

Data mining 58

Data mining Machine Learning Deep Learning Marketing 58

datapine

AUGUST 29, 2022

And with that understanding, you’ll be able to tap into the potential of data analysis to create strategic advantages, exploit your metrics to shape them into stunning business dashboards , and identify new opportunities or at least participate in the process. Product/market fit is THE most important factor to get right.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

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 82

Data Architecture Risk Management Risk Management 82

Grooper

OCTOBER 23, 2019

With the increasing value of data and more tools to process and analyze information than ever before, companies with information governance and master data model programs are outpacing their peers. Now is the time to get organized, and view information management as the single most important initiative for business growth and innovation.

Metrics 59

Metrics Measurement Strategy Optimization 59

CIO Business Intelligence

APRIL 11, 2022

This means embedding and integrating teams on key strategic priorities and supplying shared metrics to their business stakeholders to ensure a successful partnership. Proactive engagement and building an IT organization that’s integrated into front-office operations is critical to success, Scheefer says. Focus on outcomes, not technologies.

Cost-Benefit 117

Cost-Benefit Digital Transformation Strategy Consulting 117

CIO Business Intelligence

MARCH 13, 2024

AI models are able to present best practice code systems to help junior developers learn and hone their skills.” At Gallagher, a global insurance brokerage, risk management, and consulting services firm, executive search practice managing director Tom Wilson says his team uses gen AI for research and written communications.

IT 126

IT Consulting Testing Cost-Benefit 126

IBM Big Data Hub

JULY 12, 2023

Good contingency plans prioritize the risks an organization faces, delegate responsibility to members of the response teams and increase the likelihood that the company will make a full recovery after a negative event. Make a list of risks and prioritize them according to likelihood and severity.

Risk 40

Risk Cost-Benefit Internet of Things Testing 40

Jet Global

MARCH 2, 2022

For one, companies that place an emphasis on their environmental and social impacts and responsibilities, have been shown to be more resilient and that they’re able to manage their risks better during a crisis. The SFDR aims to give more transparency about sustainability and provide a common set of rules on sustainability risks.

Reporting 59

Reporting Recreation/Entertainment Risk Insurance 59

Jet Global

MAY 9, 2024

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. Job schedulers help coordinate the pipeline’s different stages and manage dependencies between tasks.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52