erwin

MARCH 25, 2021

And with many competing projects and activities vying for people’s time, it must be clear to people why choosing data governance activities will have a direct benefit to them. Usually we talk about benefits which are rather qualitative measures, but what we need for decision-making processes are values,” Pörschmann says. “We

Data Governance 109

Data Governance IT Cost-Benefit Risk 109

O'Reilly on Data

JULY 28, 2020

These measures are commonly referred to as guardrail metrics , and they ensure that the product analytics aren’t giving decision-makers the wrong signal about what’s actually important to the business. When a measure becomes a target, it ceases to be a good measure ( Goodhart’s Law ). Any metric can and will be abused.

Marketing 362

Marketing Experimentation Metrics Testing 362

Alation

FEBRUARY 14, 2023

In this day and age, regardless of industry, keeping data protected and its uses understood is vital. No business wants to have the PR nightmare of a data leak or personal data being used without the correct permissions. This is where successful data governance programs can act as a savior to many organizations.

Data Governance 52

Data Governance ROI Metrics Cost-Benefit 52

erwin

JANUARY 11, 2019

A company can’t effectively implement data governance – documenting and applying business rules and processes, analyzing the impact of changes and conducting audits – if it fails at data management. The problem usually starts by relying on manual integration methods for data preparation and mapping.

Data Governance 40

Data Governance Risk Metadata Management 40

Alation

OCTOBER 5, 2021

What is data governance and how do you measure success? Data governance is a system for answering core questions about data. It begins with establishing key parameters: What is data, who can use it, how can they use it, and why? Why is your data governance strategy failing?

Data Governance 96

Data Governance Strategy Data Quality Metrics 96

CIO Business Intelligence

JANUARY 23, 2024

Regardless of where organizations are in their digital transformation, CIOs must provide their board of directors, executive committees, and employees definitions of successful outcomes and measurable key performance indicators (KPIs). He suggests, “Choose what you measure carefully to achieve the desired results.

Measurement 140

Measurement Digital Transformation KPI Metrics 140

datapine

JANUARY 24, 2021

This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy. 3) Where will your data come from?

IT 317

IT Statistics KPI Data-driven 317

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 Consulting Metrics Data Quality 52

CIO Business Intelligence

MARCH 7, 2024

Public LLMs in the sandbox: Safely test publicly available Large Language Models (LLMs) in a sandbox environment, separate from the production setting, to assess their impact without risking operational disruptions. There is also the concern about using data. This happens more frequently than we like to admit.

Digital Transformation 67

Digital Transformation Machine Learning Risk Testing 67

O'Reilly on Data

DECEMBER 12, 2019

1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. That’s where model debugging comes in. Sensitivity analysis.

Machine Learning 190

Machine Learning Modeling Testing Risk Management 190

DataRobot

JULY 16, 2021

They all serve to answer the question, “How well can my model make predictions based on data?” In performance, the trust dimensions are the following: Data quality — the performance of any machine learning model is intimately tied to the data it was trained on and validated against.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

DataRobot

JUNE 1, 2021

In an earlier post, I shared the four foundations of trusted performance in AI : data quality, accuracy, robustness and stability, and speed. You should first identify potential compliance risks, with each additional step again tested against risks. Protecting Sensitive Data. Deploying Good Governance.

Uncertainty 52

Uncertainty Machine Learning Advertising Risk 52

O'Reilly on Data

OCTOBER 22, 2020

It’s also a good indirect measure of training data quality: a team that does not know where their data originated is likely to not know other important details about the data as well. ML models directly involved in critical safety systems are generally easy to identify as areas of concern.

Consulting 296

Consulting Risk Cost-Benefit Modeling 296

Alation

MARCH 14, 2023

All critical data elements (CDEs) should be collated and inventoried with relevant metadata, then classified into relevant categories and curated as we further define below. Some business processes may need reviewing to include data analysis — even going as far as requiring specific data to make a business decision.

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

erwin

APRIL 15, 2020

In our businesses, it is vital that we work to develop a deeper understanding of the sources, methods and quality of incoming third-party data. This deeper understanding will help us make better decisions about the risks and rewards of using that external data. Data Governance Methods for Data Distancing.

Data Governance 134

Data Governance Metadata Testing Reporting 134

AWS Big Data

JUNE 6, 2023

Data consumers lose trust in data if it isn’t accurate and recent, making data quality essential for undertaking optimal and correct decisions. Evaluation of the accuracy and freshness of data is a common task for engineers. Currently, various tools are available to evaluate data quality.

Data Quality 75

Data Quality Data Lake Data-driven Metrics 75

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. Machine learning developers are beginning to look at an even broader set of risk factors.

Machine Learning 37

Machine Learning Management Enterprise Risk Management 37

FineReport

APRIL 26, 2023

Here are some key factors to keep in mind: Understanding business objectives : It is important to identify and understand the business objectives before selecting a big data tool. These objectives should be broken down into measurable analytical goals, and the chosen tool should be able to meet those goals. Top 10 Big Data Tools 1.

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

Sisense

FEBRUARY 10, 2021

We do this throughout a variety of industries, and as the head of the electric grid ecosystem and product center at DNV, my focus is on helping optimize risk assessment, remediation, and overall reliability of critical assets at our electric utility customers. At DNV , our mission is to safeguard life, property, and the environment.

Dashboards 52

Dashboards Enterprise Risk Software 52

DataRobot Blog

JULY 21, 2022

Inquire whether there is sufficient data to support machine learning. Outline clear metrics to measure success. Document assumptions and risks to develop a risk management strategy. Reshape and aggregate data as necessary. Perform data quality checks and develop procedures for handling issues.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

datapine

APRIL 15, 2020

Working software over comprehensive documentation. The agile BI implementation methodology starts with light documentation: you don’t have to heavily map this out. But before production, you need to develop documentation, test driven design (TDD), and implement these important steps: Actively involve key stakeholders once again.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

IBM Big Data Hub

JANUARY 9, 2024

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. Increased variance: Variance measures consistency.

Enterprise 80

Enterprise Data Integration Data Quality Contextual Data 80

Cloudera

SEPTEMBER 7, 2023

But it’s also fraught with risk. This June, for example, the European Union (EU) passed the world’s first regulatory framework for AI, the AI Act , which categorizes AI applications into “banned practices,” “high-risk systems,” and “other AI systems,” with stringent assessment requirements for “high-risk” AI systems.

Insurance 79

Insurance Risk Data-driven Modeling 79

Ontotext

MAY 22, 2023

At the heart of such tools is the extraction of fields from forms or specific attributes from documents. Luckily, the text analysis that Ontotext does is focused on tasks that require complex domain knowledge and linking of documents to reference data or master data. That’s something that LLMs cannot do.

Management 59

Management Unstructured Data Metadata Cost-Benefit 59

erwin

FEBRUARY 4, 2021

However, organizations still encounter a number of bottlenecks that may hold them back from fully realizing the value of their data in producing timely and relevant business insights. It uncovered a number of obstacles that organizations have to overcome to improve their data operations. Overcoming Data Governance Bottlenecks.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

IBM Big Data Hub

SEPTEMBER 11, 2023

How to quantify the impact : Quantify, articulate and measure the expected long-term benefit of a capability to justify the investment. This strategy is not based on a “gut feel” or anecdotal knowledge of a few individuals, but on a well-documented, expansive set of findings, business analysis and calculated projections.

Data-driven 91

Data-driven IT Measurement Cost-Benefit 91

CIO Business Intelligence

MARCH 14, 2024

AI has already found its way into many government departments, with the most common use of AI identified by the NAO being to support operational decision-making through automated document analysis, digital assistant functionality, and image recognition. Safety standards There is an element of caution to government departments’ approach.

Testing 107

Testing Risk Digital Transformation Statistics 107

IBM Big Data Hub

OCTOBER 12, 2023

They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources. Documentation can also be generated and maintained with information such as a model’s data origins, training methods and behaviors.

Cost-Benefit 83

Cost-Benefit Metadata Data Governance Modeling 83

Jet Global

MARCH 4, 2020

A manufacturing Key Performance Indicator (KPI) or metric is a well defined and quantifiable measure that the manufacturing industry uses to gauge its performance over time. While this is important, what if you could grow your bottom line without having to take on expansion risks? What Is A Manufacturing KPI? View Guide Now.

Manufacturing 80

Manufacturing Metrics Reporting KPI 80

Andrew White

JANUARY 9, 2022

First, how we measure emissions and carbon footprint is about data design and policy. In other words, D&A plays a key role in the foundational measuring angle. Is there a good map that shows the connections between data, advanced analytics, digital, innovation, etc. – Yes and no.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Anmut

NOVEMBER 3, 2020

This means they make considered data investment plans – prioritising data investments to get the best ROI from this unique asset. Data isn’t subject to the same forces as other assets. Unlike capital or oil, data is not limited to a single owner or user.

Management 52

Management ROI Metadata Data Quality 52

datapine

AUGUST 29, 2022

The book has three main ideas: The biggest risk your company faces is investing a lot of time and resources into building something that the market doesn’t want. and this book will give you an insight into their data collecting procedures and the reasons behind them. Product/market fit is THE most important factor to get right.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

datapine

FEBRUARY 22, 2022

This includes defining the main stakeholders, assessing the situation, defining the goals, and finding the KPIs that will measure your efforts to achieve these goals. A planned BI strategy will point your business in the right direction to meet its goals by making strategic decisions based on real-time data.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

Alation

DECEMBER 15, 2021

This uncovers actionable intelligence, maintains compliance with regulations, and mitigates risks. Let’s explore the key steps for building an effective data governance strategy. What is a Data Governance Strategy? Data governance focuses on the daily tasks that keep information usable, understandable, and protected.

Data Governance 52

Data Governance Strategy Metadata Data Strategy 52

CIO Business Intelligence

MAY 2, 2023

While citizen developers can improve micro-efficiency, business agility, and innovation, they also present risks, such as security, compliance, privacy, data quality, duplication of efforts and technologies, and mounting technical debt.

Cost-Benefit 103

Cost-Benefit Risk Enterprise Optimization 103

datapine

NOVEMBER 27, 2019

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

AWS Big Data

AUGUST 14, 2023

At the same time, unstructured approaches to data mesh management that don’t have a vision for what types of products should exist and how to ensure they are developed are at high risk of creating the same effect through simple neglect. When do new data products get created, and who is allowed to create them?

Management 79

Management Risk Measurement Optimization 79