This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
In Ryan’s “9-Step Process for Better Data Quality” he discussed the processes for generating data that business leaders consider trustworthy. To be clear, data quality is one of several types of datagovernance as defined by Gartner and the DataGovernance Institute. Step 4: Data Sources.
Business users want to know where that data lives, understand if people are accessing the right data at the right time, and be assured that the data is of high quality. But they are not always out shopping for Data Quality […].
For any data user in an enterprise today, dataprofiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of dataprofiling, top use cases, and share important techniques and best practices for dataprofiling today.
This is the last of the 4-part blog series. In the previous blog , we discussed how Alation provides a platform for data scientists and analysts to complete projects and analysis at speed. In this blog we will discuss how Alation helps minimize risk with active datagovernance. Meet Governance Requirements.
This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches. Learn more about designing the right data architecture to elevate your data quality here.
By creating microsegments, businesses can be alerted to surprises, such as sudden deviations or emerging trends, empowering them to respond proactively and make data-driven decisions. These SQL assets can be used in downstream operations like dataprofiling, analysis, or even exporting to other systems for further processing.
In this blog, we are going to unfold the two key aspects of data management that is Data Observability and Data Quality. Data is the lifeblood of the digital age. Today, every organization tries to explore the significant aspects of data and its applications. It helps prevent data errors and inconsistencies.
Whether you are a business executive making critical choices, a scientist conducting groundbreaking research, or simply an individual seeking accurate information, data quality is a paramount concern. The Relevance of Data Quality Data quality refers to the accuracy, completeness, consistency, and reliability of data.
2) DataProfiling : To profiledata in Excel, users typically create filters and pivot tables – but problems arise when a column contains thousands of distinct values or when there are duplicates resulting from different spellings. 3) DataGovernance and Trust: With Excel, there is no actual audit trail or data lineage.
This blog post explores effective strategies for gathering requirements in your data project. Whether you are a data analyst , project manager, or data engineer, these approaches will help you clarify needs, engage stakeholders, and ensure requirements gathering techniques to create a roadmap for success.
But make no mistake: A data catalog addresses many of the underlying needs of this self-serve data platform, including the need to empower users with self-serve discovery and exploration of data products. In this blog series, we’ll offer deep definitions of data fabric and data mesh, and the motivations for each. (We
Data Observability and Data Quality are two key aspects of data management. The focus of this blog is going to be on Data Observability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data.
Efficiently adopt data platforms and new technologies for effective data management. Apply metadata to contextualize existing and new data to make it searchable and discoverable. Perform dataprofiling (the process of examining, analyzing and creating summaries of datasets).
It sits between the data lake and cloud object storage, allowing you to version and control changes to data lakes at scale. LakeFS facilitates data reproducibility, collaboration, and datagovernance within the data lake environment. Share features across the organization.
Introduction It is a critical process in the digital landscape, enabling organisations to transfer data between systems, formats, or storage solutions. As businesses evolve, the need for efficient data management becomes paramount. Explore More: Cloud Migration: Strategy and Tools What is Data Migration?
Customers enjoy a holistic view of data quality metrics, descriptions, and dashboards, which surface where they need it most: at the point of consumption and analysis. Trust flags signal the trustworthiness of data, and dataprofiling helps users determine usability. Subscribe to Alation's Blog.
Welcome to the latest installment of the phData Toolkit blog series! in this June episode of the blog. Data Source Tool Updates The data source tool has a number of use cases, as it has the ability to profile your data sources and take the resulting JSON to perform whatever action you want to take.
Early on, analysts used data catalogs to find and understand data more quickly. Increasingly, data catalogs now address a broad range of data intelligence solutions, including self-service analytics , datagovernance , privacy , and cloud transformation. Subscribe to Alation's Blog.
According to IDC, the size of the global datasphere is projected to reach 163 ZB by 2025, leading to the disparate data sources in legacy systems, new system deployments, and the creation of data lakes and data warehouses. Most organizations do not utilize the entirety of the data […].
In Part 1 and Part 2 of this series, we described how data warehousing (DW) and business intelligence (BI) projects are a high priority for many organizations. Project sponsors seek to empower more and better data-driven decisions and actions throughout their enterprise; they intend to expand their […].
In today’s digital world, data is undoubtedly a valuable resource that has the power to transform businesses and industries. As the saying goes, “data is the new oil.” However, in order for data to be truly useful, it needs to be managed effectively.
By providing a centralized platform for workflow management, these tools enable data engineers to design, schedule, and optimize the flow of data, ensuring the right data is available at the right time for analysis, reporting, and decision-making. This enhances the reliability and resilience of the data pipeline.
Common DataGovernance Challenges. Every enterprise runs into datagovernance challenges eventually. Issues like data visibility, quality, and security are common and complex. Datagovernance is often introduced as a potential solution. And one enterprise alone can generate a world of data.
It asks much larger questions, which flesh out an organization’s relationship with data: Why do we have data? Why keep data at all? Answering these questions can improve operational efficiencies and inform a number of data intelligence use cases, which include datagovernance, self-service analytics, and more.
From the sheer volume of information to the complexity of data sources and the need for real-time insights, HCLS companies constantly need to adapt and overcome these challenges to stay ahead of the competition. In this blog, we’ll explore 10 pressing data analytics challenges and discuss how Sigma and Snowflake can help.
We organize all of the trending information in your field so you don't have to. Join 17,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content