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

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

Alation

OCTOBER 5, 2021

These divergences of focus can lead to consumers feeling bogged down by overly complicated processes or leadership teams being unable to see initiative investments reap the desired rewards of their predictive business success metrics. (1). Incomplete data. Lack of commitment.

Data Governance 96

Data Governance Strategy Data Quality Metrics 96

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

Ontotext

JUNE 22, 2023

This will minimize the risk of investing resources in fundamentally wrong assumptions and will give researchers more confidence to choose top targets for validation. The confidence score and provenance metrics enable researchers to trace back any decisions and have more confidence to make evidence-driven decisions.

Metrics 52

Metrics Statistics Visualization Metadata 52

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

erwin

MARCH 8, 2019

A number of best practices and technology solutions were used to establish the data required for managing the registration and classification of data feeds: The underlying metadata is harvested followed by an initial quality check. This, among other safeguards, ensures data quality.

Data Governance 65

Data Governance Metadata Data Warehouse Data Quality 65

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 73

Data Quality Data Lake Data-driven Metrics 73

Jet Global

MAY 1, 2020

In Gartner’s report, an analyst goes to great pains to say that there is “much more risk associated to non-technology issues than there is to deploying the infrastructure, tools, and apps.”. They are going to have different ways of combining numbers into metrics. To us that equals one thing, and that’s risk. Data quality issues.

Data Warehouse 64

Data Warehouse Data Quality Risk Reporting 64

CIO Business Intelligence

JANUARY 23, 2024

For example, McKinsey suggests five metrics for digital CEOs , including the financial return on digital investments, the percentage of leaders’ incentives linked to digital, and the percentage of the annual tech budget spent on bold digital initiatives. As a result, outcome-based metrics should be your guide.

Measurement 138

Measurement Digital Transformation KPI Metrics 138

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. The only way to stay ahead in this fiercely competitive industry is through the implementation of manufacturing KPIs and metrics. What Is A Manufacturing KPI?

Manufacturing 80

Manufacturing Metrics Reporting KPI 80

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

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

datapine

FEBRUARY 17, 2023

That said, data and analytics are only valuable if you know how to use them to your advantage. Poor-quality data or the mishandling of data can leave businesses at risk of monumental failure. In fact, poor data quality management currently costs businesses a combined total of $9.7 million per year.

Analytics 191

Analytics Cost-Benefit Dashboards Business Intelligence 191

IBM Big Data Hub

SEPTEMBER 11, 2023

Using data, user research and human-centered design to effectively address these considerations can help you develop a clear modernization strategy that objectively drives your priorities, backlogs and roadmaps. The pain point tracker clusters the foundational data in which value metrics are then applied.

Data-driven 91

Data-driven IT Measurement Cost-Benefit 91

IBM Big Data Hub

OCTOBER 12, 2023

Documentation can also be generated and maintained with information such as a model’s data origins, training methods and behaviors. Checkpoints can be created throughout the AI lifecycle to prevent or mitigate bias and drift.

Cost-Benefit 83

Cost-Benefit Metadata Data Governance Modeling 83

Alation

MARCH 21, 2023

Data security The travel industry collects, transmits, processes, and stores a wide range of personally identifiable information (PII) from customers, which are of interest to bad actors, as cybercriminals target this valuable data. Otherwise, they risk a data privacy violation.

Data Analytics 52

Data Analytics Analytics Data-driven Big Data 52

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

DataRobot Blog

DECEMBER 20, 2022

The use of artificial intelligence (AI) in the investment sector is proving to be a significant disruptor, catalyzing the connection between the different players and delivering a more vivid picture of the future risk and opportunities across all different market segments. You can understand the data and model’s behavior at any time.

Management 59

Management Machine Learning Optimization Modeling 59

Andrew White

JANUARY 9, 2022

Is there a good map that shows the connections between data, advanced analytics, digital, innovation, etc. I would have to admit that there are few documents that talk about all the connections across any set of topics. This is the same for scope, outcomes/metrics, practices, organization/roles, and technology.

Analytics 64

Analytics Measurement Modeling Data-driven 64

datapine

FEBRUARY 22, 2022

Improved risk management: Another great benefit from implementing a strategy for BI is risk management. They can govern the implementation with a documented business case and be responsible for changes in scope. On the flip side, document everything that isn’t working. Clean data in, clean analytics out.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

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

Alation

JULY 6, 2021

There are new ways to quickly and effectively overcome these data governance challenges. A person or team with influence must take responsibility for reducing data governance risks. They should have resources, tools for connectivity and integration, and insights into data usage and needs. Not all are data literate!

Data Governance 52

Data Governance Metadata Data Quality Risk 52

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 77

Management Risk Measurement Optimization 77

Alation

NOVEMBER 22, 2022

What Is Data Governance In The Public Sector? Effective data governance for the public sector enables entities to ensure data quality, enhance security, protect privacy, and meet compliance requirements. With so much focus on compliance, democratizing data for self-service analytics can present a challenge.

Data Governance 52

Data Governance Metadata Data-driven Unstructured Data 52

Alation

OCTOBER 12, 2021

We might have found some data but what does it mean? There was a lack of documentation and a very heavy reliance on IT and business SMEs. The third challenge was around trusting the data. There are inconsistent definitions and inconsistent metrics, and a lack of trust in the data used in the metrics.

Strategy 52

Strategy Enterprise Analytics Data Governance 52