IBM Big Data Hub

JUNE 12, 2023

Companies rely heavily on data and analytics to find and retain talent, drive engagement, improve productivity and more across enterprise talent management. However, analytics are only as good as the quality of the data, which must be error-free, trustworthy and transparent. What is data quality? million each year.

Data Quality 67

Data Quality Data Governance People Analytics Data-driven 67

DataKitchen

MAY 10, 2024

The Second of Five Use Cases in Data Observability Data Evaluation: This involves evaluating and cleansing new datasets before being added to production. This process is critical as it ensures data quality from the onset. Examples include regular loading of CRM data and anomaly detection.

Data Quality 100

Data Quality Testing Software Dashboards 100

Alation

JANUARY 20, 2022

Several weeks ago (prior to the Omicron wave), I got to attend my first conference in roughly two years: Dataversity’s Data Quality and Information Quality Conference. Ryan Doupe, Chief Data Officer of American Fidelity, held a thought-provoking session that resonated with me. Step 2: Data Definitions.

Data Quality 94

Data Quality Data Governance Metrics Statistics 94

Octopai

AUGUST 3, 2022

It’s also a critical trait for the data assets of your dreams. What is data with integrity? Data integrity is the extent to which you can rely on a given set of data for use in decision-making. Where can data integrity fall short? Too much or too little access to data systems.

Data Integration 93

Data Integration Manufacturing Data Quality Data Governance 93

DataKitchen

SEPTEMBER 21, 2023

These layers help teams delineate different stages of data processing, storage, and access, offering a structured approach to data management. In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets.

Testing 176

Testing Data Quality Predictive Modeling Metrics 176

DataKitchen

FEBRUARY 27, 2024

Your LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers The rise of Large Language Models (LLMs) such as GPT-4 marks a transformative era in artificial intelligence, heralding new possibilities and challenges in equal measure.

Data Quality 120

Data Quality Unstructured Data Testing Modeling 120

Jet Global

JANUARY 13, 2023

Measure customer satisfaction? While these areas can all be measured, giving your company a number to aim for, there is often a gap between your numbers and true customer happiness. For example, there are at least five different options to measure “customer arrival date.” Discover how SAP data quality can hurt your OTIF.

Measurement 52

Measurement Reporting Operational Reporting Forecasting 52

Jim Harris

DECEMBER 24, 2015

Have you ever experienced that sinking feeling, where you sense if you don’t find data quality, then data quality will find you? These discussions are a critical prerequisite for determining data usage, standards, and the business relevant metrics for measuring and improving data quality.

Data Quality 56

Data Quality Enterprise Business Intelligence Data Governance 56

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. Data unification and integration.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

Ontotext

MARCH 20, 2024

In today’s data-driven world, businesses are drowning in a sea of information. Traditional data integration methods struggle to bridge these gaps, hampered by high costs, data quality concerns, and inconsistencies. It’s a huge productivity loss.”

Data-driven 52

Data-driven Strategy Sales Data Integration 52

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 66

Data Quality Data Integration Metadata Cost-Benefit 66

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. Data breaks.

Testing 307

Testing Machine Learning Consulting Data Quality 307

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

CIO Business Intelligence

MARCH 24, 2023

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. Informatica Axon Informatica Axon is a collection hub and data marketplace for supporting programs.

Data Governance 142

Data Governance Management Metadata Data Quality 142

Ontotext

FEBRUARY 14, 2024

Data integration If your organization’s idea of data integration is printing out multiple reports and manually cross-referencing them, you might not be ready for a knowledge graph. How do you measure its utility? RDF pipelines You also need to ensure you’ve got quality in your RDF pipeline.

Recreation/Entertainment 105

Recreation/Entertainment Data Integration Modeling Data Quality 105

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

FEBRUARY 29, 2024

Working with large language models (LLMs) for enterprise use cases requires the implementation of quality and privacy considerations to drive responsible AI. However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications.

Data Governance 91

Data Governance Unstructured Data Metadata Data Lake 91

DataKitchen

JUNE 19, 2023

Another way to look at the five pillars is to see them in the context of a typical complex data estate. Monitoring is another pillar of Data Journeys, extending down the stack. Moreover, cost monitoring ensures that your data operations stay within budget and that resources are used efficiently.

Testing 130

Testing Cost-Benefit Metrics Data Quality 130

FineReport

MAY 31, 2023

Return on assets measures the net profit generated per unit of asset, while return on equity (ROE) signifies the return on shareholders’ equity, indicating the efficiency of the company’s own capital. Ensuring seamless data integration and accuracy across these sources can be complex and time-consuming.

Dashboards 52

Dashboards Key Performance Indicator Metrics Visualization 52

Ontotext

JANUARY 10, 2024

And each of these gains requires data integration across business lines and divisions. Limiting growth by (data integration) complexity Most operational IT systems in an enterprise have been developed to serve a single business function and they use the simplest possible model for this. We call this the Bad Data Tax.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

BI-Survey

FEBRUARY 17, 2020

While compliance is the major driver for data governance, it bears the risk of reducing it to a very restrictive procedure. Data quality is the top challenge when it comes to using data, closely followed by organizational issues. Inadequate data quality remains the foremost challenge users face when using data.

Data Governance 89

Data Governance Data Warehouse Data Quality Data Strategy 89

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data.

Data Strategy 66

Data Strategy Strategy Data Warehouse Prescriptive Analytics 66

DataKitchen

JANUARY 23, 2023

It allows organizations to see how data is being used, where it is coming from, its quality, and how it is being transformed. DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data lineage does not directly improve data quality.

Testing 130

Testing Data Governance Data Quality Data-driven 130

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. It’s that simple.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

Ontotext

FEBRUARY 7, 2024

However, what we usually don’t talk about when generating an asset, are the huge invisible or unplanned costs occurring at a later stage when the data needs to be made available for analysis or secondary usage. As a result, a big portion of the IT capacity in Pharma is bound by data integration.

Metadata 52

Metadata Data Integration Measurement Data-driven 52

Jet Global

OCTOBER 26, 2023

If your finance team is using JD Edwards (JDE) and Oracle E-Business Suite (EBS), it’s like they rely on well-maintained and accurate master data to drive meaningful insights through reporting. For these teams, data quality is critical. Ensuring that data is integrated seamlessly for reporting purposes can be a daunting task.

Data Quality 52

Data Quality Reporting Operational Reporting Finance 52

CIO Business Intelligence

MARCH 22, 2022

Then virtualize your data to allow business users to conduct aggregated searches and analyses using the business intelligence or data analytics tools of their choice. . Set up unified data governance rules and processes. With data integration comes a requirement for centralized, unified data governance and security.

Analytics 106

Analytics Key Performance Indicator Data Warehouse Data-driven 106

DataKitchen

DECEMBER 9, 2022

DataOps observability involves the use of various tools and techniques to monitor the performance of data pipelines, data lakes, and other data-related infrastructure. This can include the use of tools for data integration and transformation, as well as technologies for managing and monitoring data-related systems and processes.

Machine Learning 130

Machine Learning Data-driven Optimization Modeling 130

AWS Big Data

MARCH 3, 2023

Additionally, the scale is significant because the multi-tenant data sources provide a continuous stream of testing activity, and our users require quick data refreshes as well as historical context for up to a decade due to compliance and regulatory demands. Finally, data integrity is of paramount importance.

Software 52

Software Data Lake Testing Cost-Benefit 52

FineReport

APRIL 9, 2024

Comparing Leading BI Tools Key Features and Capabilities When comparing leading business intelligence software tools and data analysis platforms , it is essential to evaluate a range of key features and capabilities that contribute to their effectiveness in enabling informed decision-making and data analysis.

Business Intelligence 52

Business Intelligence Software Data-driven Predictive Modeling 52

Jet Global

SEPTEMBER 26, 2023

Data Cleansing Imperative: The same report revealed that organizations recognized the importance of data quality, with 71% expressing concerns about data quality issues. This underscores the need for robust data cleansing solutions.

Data Quality 52

Data Quality Reporting Management Software 52

Jet Global

MARCH 4, 2024

Data inconsistencies become commonplace, hindering visibility and inhibiting a holistic understanding of business operations. This lack of integration makes it difficult to track progress, measure performance, and identify potential issues. Data governance and compliance become a constant juggling act.

Management 52

Management Data Governance Reporting Software 52

Ontotext

JANUARY 6, 2022

That is why we have used GraphDB , Ontotext Platform and our significant expertise in semantic data integration to show how we can improve the quality of ENTSO-E Transparency data and develop flexible analytics by leveraging the knowledge graph approach. Spotting Data Consistency Issues. Let’s take a closer look.

Marketing 52

Marketing Data Quality Dashboards Visualization 52

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. Residual plots place input data and predictions into a two-dimensional visualization where influential outliers, data-quality problems, and other types of bugs often become plainly visible.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Jet Global

AUGUST 24, 2023

Data Security Concerns: Managing data security and compliance across hybrid environments can be a significant concern. Financial data is sensitive and requires robust security measures. Quickly and easily identify data quality or compatibility issues prior to migration for successful data cleanup and configuration.

Finance 52

Finance Reporting Data Integration Risk 52

erwin

JANUARY 11, 2019

It’s only when companies take their first stab at manually cataloging and documenting operational systems, processes and the associated data, both at rest and in motion, that they realize how time-consuming the entire data prepping and mapping effort is, and why that work is sure to be compounded by human error and data quality issues.

Data Governance 40

Data Governance Risk Metadata Management 40

CIO Business Intelligence

SEPTEMBER 2, 2022

Back-end software engineers are responsible for maintaining the structure of server-side information by optimizing servers, implementing security measures, and developing data storage solutions. Back-end software engineer.

Finance 98

Finance IT Software Reporting 98