O'Reilly on Data

OCTOBER 11, 2022

So from the start, we have a data integration problem compounded with a compliance problem. An AI project that doesn’t address data integration and governance (including compliance) is bound to fail, regardless of how good your AI technology might be. Decide where data fits in. What data do you have?

Data Integration 353

Data Integration Insurance Dashboards Data-driven 353

CIO Business Intelligence

DECEMBER 18, 2024

While value-based pricing is appealing in theory, it can be extremely difficult to measure and implement in practice. This can not only reduce costs but also simplify your IT landscape and improve data integration. This shift is partly driven by economic uncertainty and the need for businesses to justify every expense.

ROI 116

ROI Software Finance Uncertainty 116

Smarten

DECEMBER 5, 2024

‘Using anomaly alerts and monitoring tools, business team members can quickly establish key performance indicators (KPIs) and personalized alerts and reports to monitor and measure results with powerful, clear, concise results that help users to understand and manage the variables that impact their targets and their results.’

Key Performance Indicator 64

Key Performance Indicator KPI Measurement Data Quality 64

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners. Data integration and cleaning.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

IBM Big Data Hub

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. In short, yes.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

DataKitchen

MAY 14, 2024

Chris will overview data at rest and in use, with Eric returning to demonstrate the practical steps in data testing for both states. Session 3: Mastering Data Testing in Development and Migration During our third session, the focus will shift towards regression and impact assessment in development cycles. Reserve Your Spot!

Data Quality 195

Data Quality Testing Metrics Measurement 195

Alation

AUGUST 9, 2022

But in the four years since it came into force, have companies reached their full potential for data integrity? But firstly, we need to look at how we define data integrity. What is data integrity? Many confuse data integrity with data quality. Is integrity a universal truth?

Data Integration 52

Data Integration Data Quality Measurement Strategy 52

Business Over Broadway

SEPTEMBER 27, 2018

In this paper, I show you how marketers can improve their customer retention efforts by 1) integrating disparate data silos and 2) employing machine learning predictive analytics. Your marketing strategy is only as good as your ability to deliver measurable results. Data Integration as your Customer Genome Project.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

O'Reilly on Data

NOVEMBER 13, 2018

At the recent Strata Data conference we had a series of talks on relevant cultural, organizational, and engineering topics. Here's a list of a few clusters of relevant sessions from the recent conference: Data Integration and Data Pipelines. Data Platforms. Model lifecycle management.

Machine Learning 277

Machine Learning Risk Management Statistics 277

CIO Business Intelligence

MARCH 19, 2025

However, embedding ESG into an enterprise data strategy doesnt have to start as a C-suite directive. Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams.

IT 59

IT Data Governance Data-driven Metrics 59

DataKitchen

NOVEMBER 18, 2021

We talk about systemic change, and it certainly helps to have the support of management, but data engineers should not underestimate the power of the keyboard. Instead, you’ll focus on managing change in governance policies and implementing the automated systems that enforce, measure, and report governance.

Testing 246

Testing Data Governance Measurement Software 246

CIO Business Intelligence

APRIL 8, 2025

From reactive fixes to embedded data quality Vipin Jain Breaking free from recurring data issues requires more than cleanup sprints it demands an enterprise-wide shift toward proactive, intentional design. Data quality must be embedded into how data is structured, governed, measured and operationalized.

Data Quality 59

Data Quality Data-driven Key Performance Indicator Metadata 59

O'Reilly on Data

FEBRUARY 11, 2019

Data lineage, data catalog, and data governance solutions can increase usage of data systems by enhancing trustworthiness of data. Moving forward, tracking data provenance is going to be important for security, compliance, and for auditing and debugging ML systems. Data Platforms.

Big Data 272

Big Data Technology Machine Learning Deep Learning 272

DataKitchen

NOVEMBER 18, 2022

Question: What is the difference between Data Quality and Observability in DataOps? Data Quality is static. It is the measure of data sets at any point in time. A financial analogy: Data Quality is your Balance Sheet, Data Observability is your Cash Flow Statement.

Data Quality 130

Data Quality Testing Measurement Data Integration 130

Jedox

FEBRUARY 20, 2020

In our previous blog post “ Proven AI solutions for modern planning “, we shared detailed insights from Dr. Rolf Gegenmantel, our Chief Marketing & Product Officer, into data management and data integration as a basis for advanced analytics and automated sales forecasts at Mitsui Chemicals Europe.

Data Integration 52

Data Integration Finance Business Driver Forecasting 52

AWS Big Data

OCTOBER 21, 2024

Let’s briefly describe the capabilities of the AWS services we referred above: AWS Glue is a fully managed, serverless, and scalable extract, transform, and load (ETL) service that simplifies the process of discovering, preparing, and loading data for analytics. To incorporate this third-party data, AWS Data Exchange is the logical choice.

Sales 106

Sales Data-driven Data Processing Key Performance Indicator 106

DataKitchen

APRIL 13, 2021

RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Azure DevOps. AWS Code Deploy.

Testing 300

Testing Machine Learning Consulting Data Science 300

CIO Business Intelligence

OCTOBER 9, 2024

How will organizations wield AI to seize greater opportunities, engage employees, and drive secure access without compromising data integrity and compliance? While it may sound simplistic, the first step towards managing high-quality data and right-sizing AI is defining the GenAI use cases for your business.

Strategy 143

Strategy Modeling Management Data Lake 143

AWS Big Data

JANUARY 15, 2025

While real-time data is processed by other applications, this setup maintains high-performance analytics without the expense of continuous processing. This agility accelerates EUROGATEs insight generation, keeping decision-making aligned with current data.

IoT 102

IoT Machine Learning Metadata Data-driven 102

Smart Data Collective

JUNE 1, 2022

Therefore, loss of data due to a cyber-attack would mean that your company does not value its consumers’ privacy and does not implement the necessary steps to ensure data protection. Usually, we find that the majority of cyber-attack victims are small businesses that are complacent with their data security measures.

Revenue Optimization 145

Revenue Optimization Measurement Marketing Interactive 145

O'Reilly on Data

DECEMBER 12, 2019

Residuals are a numeric measurement of model errors, essentially the difference between the model’s prediction and the known true outcome. There are several known attacks against machine learning models that can lead to altered, harmful model outcomes or to exposure of sensitive training data. [8] Residual analysis.

Machine Learning 282

Machine Learning Modeling Testing Risk Management 282

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 Dashboards 218

CIO Business Intelligence

SEPTEMBER 23, 2024

Data doubt compounds tough edge challenges The variety of operational challenges at the edge are compounded by the difficulties of sourcing trustworthy data sets from heterogeneous IT/OT estates. Consequently, implementing continuous monitoring systems in these conditions is often not practical or effective.

Manufacturing 122

Manufacturing Internet of Things Metadata Risk 122

DataKitchen

MAY 10, 2021

An automated process that catches errors early in the process gives the data team the maximum available time to resolve the problem – patch the data, contact data suppliers, and rerun processing steps. The measurement and monitoring of your end-to-end process can serve as an important tool in the battle to eliminate errors.

Testing 214

Testing Manufacturing Data Quality Statistics 214

DataKitchen

SEPTEMBER 21, 2023

After navigating the complexity of multiple systems and stages to bring data to its end-use case, the final product’s value becomes the ultimate yardstick for measuring success. By diligently testing and monitoring data in Use, you uphold data integrity and provide tangible value to end-users.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

CIO Business Intelligence

JUNE 24, 2024

These 10 strategies cover every critical aspect, from data integrity and development speed, to team expertise and executive buy-in. Data done right Neglect data quality and you’re doomed. It’s simple: your AI is only as good as the data it learns from. Implement stringent security measures from the start.

Data Quality 124

Data Quality ROI Data Integration Metrics 124

datapine

NOVEMBER 27, 2019

The development of business intelligence to analyze and extract value from the countless sources of data that we gather at a high scale, brought alongside a bunch of errors and low-quality reports: the disparity of data sources and data types added some more complexity to the data integration process.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

O'Reilly on Data

MARCH 20, 2019

Data integrity constraints: Many databases don’t allow for strange or unrealistic combinations of input variables and this could potentially thwart watermarking attacks. Applying data integrity constraints on live, incoming data streams could have the same benefits. Disparate impact analysis: see section 1.

Modeling 278

Modeling Machine Learning Predictive Modeling Consulting 278

CIO Business Intelligence

NOVEMBER 29, 2022

Here, I’ll highlight the where and why of these important “data integration points” that are key determinants of success in an organization’s data and analytics strategy. It’s the foundational architecture and data integration capability for high-value data products. Data and cloud strategy must align.

Data Architecture 111

Data Architecture Data Integration IoT Data-driven 111

datapine

MAY 29, 2019

Each of that component has its own purpose that we will discuss in more detail while concentrating on data warehousing. A solid BI architecture framework consists of: Collection of data. Data integration. Storage of data. Data analysis. Distribution of data. Data integration.

Business Intelligence 196

Business Intelligence Data Warehouse Dashboards Visualization 196

Anmut

JANUARY 30, 2025

Couple this with the results of a study published in the Harvard Business Review which finds that only 3% of companies data meets basic quality standards ! Redman and David Sammon, propose an interesting (and simple) exercise to measure data quality. Authors, Tadhg Nagle, Thomas C.

Data Quality 52

Data Quality Cost-Benefit Statistics Data-driven 52

BizAcuity

MAY 4, 2021

In today’s data-powered world with data fueling everything from essential management systems to customer-focused AI solutions , one thing that often gets in the way is data fragmentation. . Why Data integration plays a vital role for gaming companies? Naturally, the player journey is segmented into six parts, .

Data Integration 40

Data Integration Cost-Benefit Optimization Behavioral Analytics 40

AWS Big Data

SEPTEMBER 20, 2023

She is a data enthusiast who enjoys problem solving and tackling complex architectural challenges with customers. She is passionate about designing and building end-to-end solutions to address customer data integration and analytic needs. Big Data Architect. Zach Mitchell is a Sr.

Interactive 104

Interactive Visualization Measurement Data Architecture 104

DataKitchen

MAY 10, 2024

Verifying data completeness and conformity to predefined standards. Critical Questions for Data Ingestion Monitoring Effective data ingestion anomaly monitoring should address several critical questions to ensure data integrity: Are there any unknown anomalies affecting the data?

Data Quality 130

Data Quality Testing Software Dashboards 130

CIO Business Intelligence

AUGUST 13, 2024

This furthers the opportunity for hackers to target AI systems, exploiting vulnerabilities that can compromise data integrity, operational functionality, and regulatory compliance. The need for robust security measures is underscored by several key factors.

Risk 119

Risk Cost-Benefit Enterprise Modeling 119

Smart Data Collective

OCTOBER 19, 2022

DynamoDB is generally considered to be the more secure of the two — with the full power of AWS’ security measures behind it. DynamoDB integrates with AWS security services so you can use your own encryption keys, which you can do as well on MongoDB using the key management system available on whatever cloud provider you utilize.

Big Data 132

Big Data Management Recreation/Entertainment Cost-Benefit 132

CIO Business Intelligence

MAY 10, 2022

By applying machine learning to the data, you can better predict customer behavior. Gartner has identified four main types of CDPs: marketing cloud CDPs, CDP engines and toolkits, marketing data-integration CDPs, and CDP smart hubs. Treasure Data CDP is a data science CDP built for predictive modeling and advanced analytics.

Advertising 122

Advertising Interactive Marketing Structured Data 122