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What are your biggest data migration challenges?   Aqurance specializes in seamlessly transferring vast datasets into Veeva Vault. ✅ Our deep industry knowledge and proven methodology ensure minimal disruption to your operations. 🚀 Complex data migrations don't have to be daunting.    Contact our expert Alexandros Paliatsos to learn more. 💡  #VeevaVault #DataMigration

I'm excited to share that I've written a review of #IBMInfoSphereInformationServer on #TrustRadius. With my experience, I was able to share how I use #IBMInfoSphereInformationServer day-to-day to help others choose the right software for them. Comment below if you have any questions or click the link below to read my full #TrustRadiusB2BReview. Well-Suited Scenarios: 1. Enterprise Data Integration: Ideal for large organizations needing to integrate data from multiple sources, ensuring consistency and accuracy across systems.2. Regulatory Compliance: Excellent for industries like finance and healthcare where data governance and compliance with regulations

🚀 Case Study Overview: Synthetic Test & Development Data with Leading EMR & Healthcare Solutions Provider 🌐 In today's fast-paced healthcare landscape, having secure and efficient test data is critical for driving innovation and improving patient care. A leading EMR and healthcare solutions provider serving over 150 million patients faced challenges in managing complex test data across multiple systems and platforms. With a need for faster test data provisioning, enhanced automation, and improved data consistency, the company turned to a cutting-edge synthetic data solution to streamline operations and accelerate development. Here's how they transformed their test data management and strengthened their operational efficiency! 𝐊𝐞𝐲 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: ► Managing complex data dependencies and diverse data formats across multiple platforms in a Service Oriented Architecture (SOA). ► Testing was time-consuming, and test data did not cover all test scenarios. ► The current test data solution had trouble supporting Referential integrity across systems 👇 #HealthcareInnovation #DataManagement #SyntheticData, Syntho https://lnkd.in/dBAXJdqk

The entire DPDP solution implementation can be categorised into three major milestones. Data Discovery, Data Registry and Data Residency. Lets cover one by one starting with Data Discovery. This refers to an inventory of systems deployed in an organization that collects, processes, shares, or stores personal data. While identifying the systems and the data they collect is relatively straightforward, pinpointing the various processes that utilize this data can be time-consuming. For large organizations (with several hundred million customers), we recommend using data discovery tools that can scan databases, files, and other repositories, providing a detailed analysis of the types of data present and their locations. While this, in itself, is not foolproof, it can be a good starting point to create an inventory of the system and the data captured by these systems. Once the inventory is ready, it can be mapped into the Atlas registry, either using APIs or file templates. For medium to smaller-sized organisations (up to a few million customers), we recommend using our semi-automated data discovery process. This involves working with your teams to identify the sources of data, profiling the data sources to classify and categorize personal and non-personal data, adding the data policies that define the collection, processing, sharing, and storing of data, and specifying the purpose for which the data will be used. Learn more about Atlas DPDP Discovery: https://lnkd.in/gFEzN-75 #DPDP

Playing Jenga with your data? 🤹 Be the ace, not the amateur. Learn to balance data quality, governance, and automation in Syniti's latest blog post. Keep your tower (and data) intact! #DataMigration #Syniti #DataThatIgnites

Playing Jenga with your data? 🤹 Be the ace, not the amateur. Learn to balance data quality, governance, and automation in Syniti's latest blog post. Keep your tower (and data) intact! #DataMigration #Syniti #DataThatIgnites

Playing Jenga with your data? 🤹 Be the ace, not the amateur. Learn to balance data quality, governance, and automation in Syniti's latest blog post. Keep your tower (and data) intact! #DataMigration #Syniti #DataThatIgnites

Playing Jenga with your data? 🤹 Be the ace, not the amateur. Learn to balance data quality, governance, and automation in Syniti's latest blog post. Keep your tower (and data) intact! #DataMigration #Syniti #DataThatIgnites

Playing Jenga with your data? 🤹 Be the ace, not the amateur. Learn to balance data quality, governance, and automation in Syniti's latest blog post. Keep your tower (and data) intact! #DataMigration #Syniti #DataThatIgnites

#Querymanagement Query management is a crucial aspect of Clinical Data Management (CDM) that ensures the accuracy and integrity of data collected during clinical trials. It involves the identification, resolution, and documentation of discrepancies or questions that arise regarding data entries. Effective query management is essential for maintaining the quality of clinical data and for ensuring that the data is reliable and compliant with regulatory standards. #Steps in Query Management Process 1. Data Entry and Validation Data entered into the Clinical Data Management System (CDMS) undergoes initial validation checks. These checks can be automated and are designed to identify data that is missing, inconsistent, or falls outside predefined acceptable ranges. 2. Query Generation When a data issue is detected, a query is generated. This query is a formal request for clarification or correction, sent to the site where the data originated. Queries can be generated automatically by the CDMS or manually by data managers who review the data. 3. Query Notification The site responsible for the data is notified of the query. This notification is typically managed through the CDMS, which tracks the status of queries and facilitates communication between data managers and site personnel. 4. Query Resolution Site personnel review the query and provide a response, which might involve correcting the data entry, providing justification for the data, or confirming that the original data was correct. This step may require consultation with clinical staff or reference to source documents. 5. Data Correction and Re-validation If a correction is made, the data is updated in the CDMS and subjected to re-validation to ensure that the new entry meets all required criteria. If the data is confirmed to be correct as originally entered, the justification is reviewed and approved by the data management team. 6. Query Closure Once the issue is resolved and all necessary adjustments are made, the query is closed in the system. Documentation of the query and its resolution is maintained for audit purposes and future reference. 7. Quality Review Periodic reviews are conducted to ensure that queries are being resolved efficiently and effectively. This includes analyzing the types and frequencies of queries to identify any trends or areas where data collection processes could be improved.