Precisely

MARCH 23, 2023

You need a flexible framework to efficiently identify, understand, and link the underlying data elements required for accurate, consistent, and contextualized ESG reporting. In summary: your ESG data needs data integrity. The stakes are high and there isn’t a tolerance for error. Let’s examine that more.

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Monte Carlo

JUNE 16, 2023

Data integrity and quality may seem similar at first glance, and they are sometimes used interchangeably in everyday life, but they play unique roles in successful data management. You can have data quality, without data integrity.

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Monte Carlo

MARCH 24, 2023

The key differences are that data integrity refers to having complete and consistent data, while data validity refers to correctness and real-world meaning – validity requires integrity but integrity alone does not guarantee validity. What is Data Integrity? What Is Data Validity?

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Precisely

SEPTEMBER 19, 2024

Key Takeaways: Data integrity is essential for AI success and reliability – helping you prevent harmful biases and inaccuracies in AI models. Robust data governance for AI ensures data privacy, compliance, and ethical AI use. Proactive data quality measures are critical, especially in AI applications.

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Data Engineering Podcast

MAY 26, 2024

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. Starburst : ![Starburst

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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.

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Precisely

OCTOBER 24, 2024

Maintaining data integrity during cloud migration is essential to ensure reliable and high-quality data for better decision-making and future use in advanced applications. You rely on accurate and trustworthy data to drive better decision-making – and anomalies in your data are all too common.

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Precisely

SEPTEMBER 26, 2024

Key Takeaways: Trusted AI requires data integrity. For AI-ready data, focus on comprehensive data integration, data quality and governance, and data enrichment. Building data literacy across your organization empowers teams to make better use of AI tools.

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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?

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Precisely

AUGUST 22, 2024

While data fabric and data mesh can function independently, they are increasingly viewed as complementary strategies. By integrating data from multiple sources and abstracting complexity, data fabric allows users to interact with consistent, high-quality data as if it were centralized.

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Precisely

JULY 27, 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 the challenges and impacts of poor data quality.

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Data Science Blog: Data Engineering

AUGUST 22, 2024

Real-time data preparation tools allow companies to react quickly to new information, maintaining a competitive edge in fast-paced industries. Improved Data Integration Data often comes from various sources, and integrating this data smoothly is essential.

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Precisely

OCTOBER 8, 2024

But effective AI and ML models must be built on a foundation of data integrity. Without the right data, your models are prone to errors that can lead to costly mistakes and missed opportunities. Data enrichment has a key role to play here. This is what makes the breadth and depth of your AI data so essential.

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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.

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Databand.ai

MAY 30, 2023

Data quality refers to the degree of accuracy, consistency, completeness, reliability, and relevance of the data collected, stored, and used within an organization or a specific context. High-quality data is essential for making well-informed decisions, performing accurate analyses, and developing effective strategies.

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Striim

JULY 10, 2024

Data Collection and Integration: Data is gathered from various sources, including sensor and IoT data, transportation management systems, transactional systems, and external data sources such as economic indicators or traffic data.

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Precisely

AUGUST 29, 2024

Solving the Challenge of Untrustworthy AI Results AI has the potential to revolutionize industries by analyzing vast datasets and streamlining complex processes – but only when the tools are trained on high-quality data. So, the risk of entering into these initiatives without taking care of your data first is simply too high.

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Precisely

DECEMBER 21, 2022

Read more > #4 Top 3 Ways to Improve Patient Care Through Healthcare Data Governance A data governance solution that incorporates analytics and delivers high-quality data can help revolutionize our approach to healthcare. Read #3 How are Insurance Carriers Maximizing Their Return on Investment?

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Striim

JULY 11, 2024

To better understand how integrating Striim and Snowflake can help, it’s helpful to first learn about each platform’s disparate capabilities. Striim is a next-generation Cloud Data Integration platform that specializes in real-time data replication and Change Data Capture (CDC).

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Precisely

DECEMBER 27, 2022

As 2022 wraps up, we would like to recap our top posts of the year in Data Integrity, Data Integration, Data Quality, Data Governance, Location Intelligence, SAP Automation, and how data affects specific industries. Let’s take a look!

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Precisely

AUGUST 19, 2024

To build economies of scale, start small with high-value projects, focus on the larger data integrity journey, and utilize domain expertise to build a robust MDM framework for long-term success. The demand for multi-domain master data management (MDM) is at an all-time high.

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dbt Developer Hub

JANUARY 23, 2023

At Tempus , a precision medicine company specializing in oncology, high quality data is a necessary component for high quality clinical models. Aggregating test failure results using Jinja macros and pre-configured metadata to pull together high level summary tables. on BigQuery.

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Precisely

SEPTEMBER 7, 2023

Advanced data analytics enable insurance carriers to evaluate risk at a far more granular level than ever before, but big data can only deliver real business value when carriers ensure data integrity. Data quality is critical, but data integrity goes much further than accuracy, completeness, and consistency.

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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.

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Precisely

FEBRUARY 23, 2024

Their ability to generate business value is directly related to the quality of their data, however. Unless they have high-quality data, business users simply cannot deliver optimal results. The best data quality tools adapt easily as your company changes and grows.

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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.

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Precisely

SEPTEMBER 25, 2023

Read Turning Raw Data into Meaningful Insights Even though organizations value data-driven decision-making more than ever before, data quality remains a major barrier across industries. So how does the data validation process help on the journey to better data quality and ultimately, data integrity?

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Striim

APRIL 30, 2024

Striim’s Solution Kramp adopted Striim for its powerful, mature real-time data integration, seamlessly connecting diverse databases like Oracle, Microsoft, and Postgres, to ensure continuous, high-quality data replication essential for forecasting and order management.

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Precisely

JANUARY 27, 2023

Data plays a central role here. Powerful customer engagement hinges on high levels of data integrity, effective data governance programs, and a clear vision of how CX can be a differentiator. The challenge is that many business leaders still struggle to turn their data into tangible improvements in CX.

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Precisely

NOVEMBER 29, 2023

Organizations should be careful not to automate business processes before considering which data sets those processes impact. Automation increases the potential to create a large volume of bad data very quickly. Conversely, beginning with high-quality data and ensuring continuing data integrity helps automation efforts succeed.

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Precisely

JANUARY 22, 2024

Trusting your data is the cornerstone of successful AI and ML (machine learning) initiatives, and data integrity is the key that unlocks the fullest potential. Without data integrity, you risk compromising your AI and ML initiatives due to unreliable insights and biases that don’t fuel business value.

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Data Integration High Quality Data Machine Learning Data Governance 59

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.

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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.

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Data Cleanse Metadata High Quality Data Datasets 52

Data Engineering Weekly

MARCH 11, 2023

We also touch on the idea that data creation will be a decentralized process and the role of tools like data contracts in enabling successful decentralized data modeling. We emphasize the importance of creating high-quality data and the need for technological and organizational solutions to achieve this goal.

Data Engineering 52

Data Engineering Data Engineer Engineering High Quality Data 52

The Modern Data Company

JULY 18, 2023

Data Quality and Reliability Ensuring data quality is crucial for any data product. High-quality data, free from errors, inconsistencies, or biases, forms the foundation for accurate analysis and reliable insights.

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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.

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Precisely

MARCH 12, 2024

Enriched customer data and best-in-class customer engagement technology enable companies to deliver outstanding CX at scale, using automation. Whether it’s a high-touch person-to-person interaction or a web-based inquiry, your customer interactions will be better because you are fully informed with high-quality data.

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