Precisely

MAY 13, 2024

Key Takeaways: Data integration is vital for real-time data delivery across diverse cloud models and applications, and for leveraging technologies like generative AI. As enterprise technology landscapes grow more complex, the role of data integration is more critical than ever before.

Data Integration 52

Data Integration Metadata Amazon Web Services Data Governance 52

Precisely

MAY 23, 2024

Key takeaways: Quickly adapt to market changes by easily adding new data sources and targets, ensuring your IT landscape evolves at the pace of your business. Gain a competitive edge with real-time data integration, crucial for time-sensitive decisions and actions in fraud detection and customer interactions.

Data Integration 64

Data Integration Metadata Telecommunication Data Governance 64

Precisely

MAY 23, 2024

Key takeaways: Quickly adapt to market changes by easily adding new data sources and targets, ensuring your IT landscape evolves at the pace of your business. Gain a competitive edge with real-time data integration, crucial for time-sensitive decisions and actions in fraud detection and customer interactions.

Data Integration 52

Data Integration Metadata Telecommunication Data Governance 52

Precisely

SEPTEMBER 15, 2023

Data governance is no trivial undertaking. When executed correctly, data governance transitions businesses from guesswork to data-informed strategies. For those who follow the right roadmap on their data governance journey, the payoff can be enormous.

Data Governance 52

Data Governance Government Programming Data 52

Precisely

MAY 31, 2023

To innovate, compete, and grow in the current macroeconomic environment, enterprises must approach data strategically. A sound data strategy doesn’t happen by accident; it’s built on a foundation of data integrity , including accuracy, consistency, and rich context. Many organizations still struggle with data integrity.

Data Integration 52

Data Integration Metadata Data Governance Data 52

Precisely

NOVEMBER 18, 2024

Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top data integrity challenges, and priorities. AI drives the demand for data integrity.

Data Analytics 72

Data Analytics Data Governance Data Integration Government 72

Knowledge Hut

APRIL 20, 2023

Data governance refers to the set of policies, procedures, mix of people and standards that organisations put in place to manage their data assets. It involves establishing a framework for data management that ensures data quality, privacy, security, and compliance with regulatory requirements.

Data Governance 59

Data Governance Government Data Cleanse Data Security 59

Precisely

JULY 19, 2023

Data governance is fast becoming a business imperative. Many top executives and line-of-business managers lack a clear understanding of the benefits of data governance. Data is a valuable organizational asset, yet if an organization isn’t capable of fully utilizing that asset, there can be a substantial opportunity cost.

Data Governance 52

Data Governance Government Building Data 52

Precisely

JULY 31, 2023

The 2023 Data Integrity Trends and Insights Report , published in partnership between Precisely and Drexel University’s LeBow College of Business, delivers groundbreaking insights into the importance of trusted data. Let’s explore more of the report’s findings around data integrity maturity, challenges, and priorities.

Data Integration 52

Data Integration Telecommunication Data Governance Government 52

Data Engineering Podcast

APRIL 22, 2018

Summary The information about how data is acquired and processed is often as important as the data itself. For this reason metadata management systems are built to track the journey of your business data to aid in analysis, presentation, and compliance. What is involved in deploying your metadata collection agents?

Business Intelligence 113

Business Intelligence Metadata Management Data Governance 113

Precisely

NOVEMBER 18, 2024

Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top data integrity challenges, and priorities. AI drives the demand for data integrity.

Data Analytics 59

Data Analytics Data Governance Data Integration Government 59

Precisely

JUNE 22, 2023

As the financial services landscape has become more complex and sophisticated, the concept of data quality has evolved to imply a holistic approach that encompasses the overall trustworthiness of data. They have found ways to curate and manage data to instill confidence among decision-makers.

Data Integration 52

Data Integration Banking Data Governance Government 52

AltexSoft

MARCH 2, 2023

As the amount of enterprise data continues to surge, businesses are increasingly recognizing the importance of data governance — the framework for managing an organization’s data assets for accuracy, consistency, security, and effective use. Projections show that the data governance market will expand from $1.81

Data Governance 52

Data Governance Government Programming Healthcare 52

Precisely

JULY 18, 2024

Data management recommendations and data products emerge dynamically from the fabric through automation, activation, and AI/ML analysis of metadata. As data grows exponentially, so do the complexities of managing and leveraging it to fuel AI and analytics.

Data Management 52

Data Management Management Metadata Database-centric 52

phData: Data Engineering

NOVEMBER 1, 2024

In an effort to better understand where data governance is heading, we spoke with top executives from IT, healthcare, and finance to hear their thoughts on the biggest trends, key challenges, and what insights they would recommend. Get the Trendbook What is the Impact of Data Governance on GenAI?

Government 52

Government Data Governance Finance Metadata 52

Precisely

AUGUST 22, 2024

Both architectures tackle significant data management challenges such as integrating disparate data sources, improving data accessibility, automating management processes, and ensuring data governance and security. Problems it solves Data fabric addresses key data management and use challenges.

Data Governance 69

Data Governance Government High Quality Data Metadata 69

Precisely

OCTOBER 31, 2024

Instead of relying on a central data management team, this architecture empowers your subject matter experts and domain owners to curate, maintain, and share data products that impact their domain. A data fabric weaves together different data management tools, metadata, and automation to create a seamless architecture.

Data Architecture 69

Data Architecture Architecture Metadata Government 69

Precisely

JUNE 24, 2024

The Precisely team recently had the privilege of hosting a luncheon at the Gartner Data & Analytics Summit in London. It was an engaging gathering of industry leaders from various sectors, who exchanged valuable insights into crucial aspects of data governance, strategy, and innovation.

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Food Data Analytics Pharmaceutical Consulting 94

Precisely

JANUARY 25, 2024

You must carefully consider various mainframe functions, including security, system logs, metadata, and COBOL copybooks when moving to the new cloud platform. Mind Your Metadata When you move data from one system to another, it’s important to maintain metadata regarding that data’s lineage. Best Practice 2.

Metadata 69

Metadata Relational Database Data Governance Government 69

ThoughtSpot

OCTOBER 9, 2023

How ThoughtSpot builds trust with data catalog connectors For many, the data catalog is still the primary home for metadata enrichment and governance. Think: data descriptions, data quality markers, business owners, etc. Set how often you want the metadata to sync from Alation to ThoughtSpot.

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Metadata Data Government Data Governance 105

Monte Carlo

SEPTEMBER 10, 2024

Let’s dive into what data warehouse governance entails, why it matters, and how companies can avoid becoming the next cautionary tale. Table of Contents What is Data Warehouse Governance? Data warehouse governance is the set of policies, procedures, and controls that allow for the effective management of data.

Data Warehouse 52

Data Warehouse Government Data Governance Metadata 52

AltexSoft

OCTOBER 8, 2021

What’s more, that data comes in different forms and its volumes keep growing rapidly every day — hence the name of Big Data. The good news is, businesses can choose the path of data integration to make the most out of the available information. Data integration in a nutshell. Data integration process.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

Monte Carlo

JULY 22, 2024

Data doesn’t just flow – it floods in at breakneck speed. How do we track this tsunami of changes, ensure data integrity, and extract meaningful insights? Data versioning is the answer. Maintaining metadata about each version. Why is Data Versioning Important? Tracking changes between versions.

Metadata 52

Metadata Datasets ETL Tools Data 52

Precisely

JUNE 6, 2023

Tendü Yogurtçu offered that organizations today are viewing data as a product, and they are working to streamline access to both data and metadata for faster insights that help business users deliver tangible value. To create more value from data, organizations must take a very proactive approach to data integrity.

Data Architecture 52

Data Architecture Architecture Metadata Data Lake 52

Precisely

SEPTEMBER 11, 2023

At Precisely, we recognize the value and potential of AI to help our customers work faster and smarter, and make more powerful, confident decisions grounded in trusted data – supporting our overall mission of unlocking data integrity for organizations of all kinds.

Metadata 52

Metadata Data Integration Datasets Data Analysis Tools 52

Monte Carlo

MAY 14, 2024

At its core, a table format is a sophisticated metadata layer that defines, organizes, and interprets multiple underlying data files. Table formats incorporate aspects like columns, rows, data types, and relationships, but can also include information about the structure of the data itself.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Snowflake

NOVEMBER 29, 2023

While the former can be solved by tokenization strategies provided by external vendors, the latter mandates the need for patient-level data enrichment to be performed with sufficient guardrails to protect patient privacy, with an emphasis on auditability and lineage tracking.

Metadata 87

Metadata Healthcare Medical Data Storage 87

Databand.ai

AUGUST 30, 2023

Integrating these principles with data operation-specific requirements creates a more agile atmosphere that supports faster development cycles while maintaining high quality standards. This demands the implementation of advanced data integration techniques, such as real-time streaming ingestion, batch processing, and API-based access.

Data Governance 40

Data Governance Data Cleanse Government Data Science 40

Databand.ai

AUGUST 30, 2023

Finally, you should analyze the results of your data performance tests and implement any necessary improvements to your data processing systems and infrastructure. Metadata is data about data, such as data definitions, data lineage, and data quality rules, that helps organizations understand and manage their data more effectively.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

DataKitchen

AUGUST 16, 2024

At DataKitchen, we think of this is a ‘meta-orchestration’ of the code and tools acting upon the data. Data Pipeline Observability: Optimizes pipelines by monitoring data quality, detecting issues, tracing data lineage, and identifying anomalies using live and historical metadata.

Data Pipeline 50

Data Pipeline Metadata Data Governance Coding 50

Databand.ai

AUGUST 30, 2023

Data silos: Legacy architectures often result in data being stored and processed in siloed environments, which can limit collaboration and hinder the ability to generate comprehensive insights. This requires implementing robust data integration tools and practices, such as data validation, data cleansing, and metadata management.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Monte Carlo

FEBRUARY 21, 2023

In this post, we’ll discuss what, exactly, a data fabric is, how other companies have used it, and how you can build one at your company. Table of Contents What is a data fabric? A data fabric infuses data governance and security across all forms of data, no matter its origin or destination within the organization.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Monte Carlo

FEBRUARY 21, 2023

In this post, we’ll discuss what, exactly, a data fabric is, how other companies have used it, and how you can build one at your company. Table of Contents What is a data fabric? A data fabric infuses data governance and security across all forms of data, no matter its origin or destination within the organization.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

phData: Data Engineering

SEPTEMBER 19, 2023

Data modeling methodologies are systematic approaches used to design and define the structure and relationships of data within a system. They provide a framework for organizing and representing data elements, attributes, and relationships. Want to learn more about data governance?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Databand.ai

AUGUST 30, 2023

This includes defining roles and responsibilities related to managing datasets and setting guidelines for metadata management. Data profiling: Regularly analyze dataset content to identify inconsistencies or errors. This may include tasks such as data profiling, data cleansing, and metadata management.

Building 52

Building Data Cleanse Data Governance Datasets 52

Precisely

APRIL 11, 2023

When data owners from business and technical teams don’t share a common understanding of this siloed data enterprise-wide, confidence in decisions and results are impacted. That’s why a data catalog is critical to any organization – particularly if you run analysis and reports in cloud data platforms.

Accessibility 52

Accessibility Accessible Metadata Data Integration 52

Databand.ai

AUGUST 30, 2023

By using DataOps tools, organizations can break down silos, reduce time-to-insight, and improve the overall quality of their data analytics processes. DataOps tools can be categorized into several types, including data integration tools, data quality tools, data catalog tools, data orchestration tools, and data monitoring tools.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52