Precisely

MARCH 4, 2024

As you navigate the complexities of integrating AI into your operations, it’s essential to understand data integrity – data with maximum accuracy, consistency, and context – and its pivotal role in ensuring AI reliability. Next, you’ll see valuable AI use cases and how data integrity powers success.

Amazon Web Services 72

Amazon Web Services Data Integration High Quality Data Government 72

Precisely

SEPTEMBER 9, 2024

How confident are you in the quality of your data? Across industries and business objectives, high-quality data is a must for innovation and data-driven decision-making that keeps you ahead of the competition. Can you trust it for fast, confident decision-making when you need it most?

Management 64

Management High Quality Data Structured Data Data Lake 64

Precisely

AUGUST 29, 2024

Key Takeaways Data quality ensures your data is accurate, complete, reliable, and up to date – powering AI conclusions that reduce costs and increase revenue and compliance. Data observability continuously monitors data pipelines and alerts you to errors and anomalies.

Data Governance 52

Data Governance Government High Quality Data Datasets 52

Ascend.io

JULY 11, 2024

This helps in identifying and rectifying errors, leading to high-quality data. For example, timestamps verify data freshness and reliability. It also enables data profiling, analyzing data to understand its structure and quality. Implement processes for updating this information as data changes.

Metadata 52

Metadata IT Government High Quality Data 52

Monte Carlo

JUNE 20, 2024

It’s the mantra for data teams, and it underlines the importance of data quality anomaly detection for any organization. The quality of the input affects the quality of the output – and in order for data teams to produce high-quality data products, they need high-quality data from the very start.

High Quality Data 52

High Quality Data Machine Learning Algorithm Datasets 52

Databand.ai

AUGUST 30, 2023

Data quality monitoring refers to the assessment, measurement, and management of an organization’s data in terms of accuracy, consistency, and reliability. It utilizes various techniques to identify and resolve data quality issues, ensuring that high-quality data is used for business processes and decision-making.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Databand.ai

JULY 11, 2023

By automating many of the processes involved in data quality management, data quality platforms can help organizations reduce errors, streamline workflows, and make better use of their data assets. This functionality is critical for not only fixing current issues but also preventing future ones.

Data Cleanse 52

Data Cleanse Telecommunication High Quality Data BI 52

Monte Carlo

OCTOBER 10, 2024

As the use of AI becomes more ubiquitous across data organizations and beyond, data quality rises in importance right alongside it. After all, you can’t have high-quality AI models without high-quality data feeding them. That’s like trying to cook something using only spoiled ingredients.

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 52

Ascend.io

JUNE 8, 2023

The Essential Six Capabilities To set the stage for impactful and trustworthy data products in your organization, you need to invest in six foundational capabilities. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

dbt Developer Hub

JANUARY 23, 2023

Testing the quality of data in your warehouse is an important aspect in any mature data pipeline. One of the biggest blockers for developing a successful data quality pipeline is aggregating test failures and successes in an informational and actionable way. on BigQuery.

Metadata 52

Metadata High Quality Data SQL Data Integration 52

Monte Carlo

FEBRUARY 27, 2024

Organizations need to connect LLMs with their proprietary data and business context to actually create value for their customers and employees. They need robust data pipelines, high-quality data, well-guarded privacy, and cost-effective scalability. Data engineers. Who can deliver?

Data Engineering 59

Data Engineering Data Engineer Engineering High Quality Data 59

Databand.ai

JULY 5, 2023

What Are Data Observability Tools? Data observability tools are software solutions that oversee, analyze, and improve the performance of data pipelines. Data observability tools allow teams to detect issues such as missing values, duplicate records, or inconsistent formats early on before they affect downstream processes.

Data Pipeline 52

Data Pipeline Data Lake Data Warehouse Datasets 52

Ascend.io

FEBRUARY 28, 2024

While data engineering and Artificial Intelligence (AI) may seem like distinct fields at first glance, their symbiosis is undeniable. The foundation of any AI system is high-quality data. Here lies the critical role of data engineering: preparing and managing data to feed AI models.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Monte Carlo

MAY 25, 2023

Picture this: your data is scattered. Data pipelines originate in multiple places and terminate in various silos across your organization. Your data is inconsistent, ungoverned, inaccessible, and difficult to use. Some of the value companies can generate from data orchestration tools include: Faster time-to-insights.

Data Pipeline 52

Data Pipeline Data Workflow Data Data Governance 52

Monte Carlo

JULY 27, 2023

These specialists are also commonly referred to as data reliability engineers. To be successful in their role, data quality engineers will need to gather data quality requirements (mentioned in 65% of job postings) from relevant stakeholders.

Engineering 52

Engineering Healthcare Data Warehouse Scala 52

Monte Carlo

JULY 11, 2023

Regardless of the approach you choose, it’s important to keep a scrutinous eye on whether or not your data outputs are matching (or close to) your expectations; often, relying on a few of these measures will do the trick. System or technical errors: Errors within the data storage, retrieval, or analysis systems can introduce inaccuracies.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Databand.ai

AUGUST 30, 2023

Benefits of a Data Quality Strategy Implementing a robust data quality strategy offers numerous benefits that directly impact your business’s bottom line and overall success. Additionally, high-quality data reduces costly errors stemming from inaccurate information.

Building 52

Building Data Cleanse Data Governance Datasets 52

Edureka

FEBRUARY 7, 2023

Azure Data Engineers use a variety of Azure data services, such as Azure Synapse Analytics, Azure Data Factory, Azure Stream Analytics, and Azure Databricks, to design and implement data solutions that meet the needs of their organization. Gain hands-on experience using Azure data services.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

DataKitchen

SEPTEMBER 21, 2023

Running these automated tests as part of your DataOps and Data Observability strategy allows for early detection of discrepancies or errors. There are multiple locations where problems can happen in a data and analytic system. What is Data in Use?

Raw Data 69

Raw Data Data Business Intelligence Data Engineering 69

Monte Carlo

FEBRUARY 20, 2024

Integrations : They offer a wide array of connectors for databases, SaaS applications, cloud storage solutions, and more, covering both popular and niche data sources. Batch vs Streaming : They focus on automated batch data integration but also support near real-time data replication for certain sources.

Data Ingestion 52

Data Ingestion Google Cloud Kafka AWS 52

DataKitchen

OCTOBER 2, 2023

This lack of control is exacerbated by many people and/or automated data ingestion processes introducing changes to the data. This creates a chaotic data landscape where accountability is elusive and data integrity is compromised. Did each technology start and end successfully and on time?)

Data Ingestion 40

Data Ingestion Data Government Datasets 40

Monte Carlo

AUGUST 30, 2023

Run the test again to validate that the initial problem is solved and that your data meets your quality and accuracy standards. Schedule and automate Just like schema tests, custom data tests in dbt are typically not run just once but are incorporated into your regular data pipeline to ensure ongoing data quality.

SQL 52

SQL Datasets Database High Quality Data 52

DataKitchen

JUNE 19, 2023

The Data Journey, in this sense, provides transparency about the status and outcomes of individual tasks, offers insights into potential bottlenecks or inefficiencies in the sequence of operations, and helps ensure that scheduled tasks are executed as planned. Consider a data pipeline orchestrated by Airflow.

Data 52

Data Data Validation Utilities High Quality Data 52

Monte Carlo

JUNE 20, 2023

Second step: Creating data product SLOs A key part of data product thinking is keeping the consumers at the center and considering what provides the most value for them. You can think of SLOs as measures that remove uncertainty surrounding the data and serve as a primary way to define trustworthiness for its consumers.

Building 52

Building Data Datasets High Quality Data 52

Precisely

JULY 15, 2024

Accurate, consistent, and contextual data leads to more confident decisions, but in a world drowning in data, organizations are struggling to manage and leverage it to produce trusted and timely data products for decision-making. Data products must be properly designed and organized to be reused across the organization.

Data Governance 72

Data Governance Data Integration Government Data 72

Databand.ai

JANUARY 24, 2023

Next, it pulls the concepts of collaboration, breaking down silos, and continuous delivery from DevOps to bring together data scientists, data analysts, data engineers , AI and ML teams, and DevOps teams to deploy new insights quickly, make those insights easily accessible, and regularly iterate as needed.

Retail 52

Retail Banking Government Manufacturing 52

Monte Carlo

MAY 17, 2023

Data Warehouse (Or Lakehouse) Migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Transition To A Data Mesh (Or Other Data Team Structure) 40. Conduct Pre-Mortems 38.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Data warehouse (or Lakehouse) migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Transition To A Data Mesh (Or Other Data Team Structure) 40. Conduct Pre-Mortems 38.

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Data 52