«Tiene clave primaria» no describe toda la garantía. En Snowflake importa el tipo de tabla: las estándar no hacen cumplir esa unicidad; las híbridas sí. ¿Qué detecta el duplicado antes de que llegue al informe?
@nicolauroca.bsky.social
@nicolauroca.bsky.social«Tiene clave primaria» no describe toda la garantía. En Snowflake importa el tipo de tabla: las estándar no hacen cumplir esa unicidad; las híbridas sí. ¿Qué detecta el duplicado antes de que llegue al informe?
@pilogian.bsky.socialContinuous Data Governance
Data governance cannot be a one-time exercise. As enterprise information changes, standards, ownership, monitoring, and improvement must evolve with it to keep data trusted and useful.
@pilogian.bsky.socialData Health Scores
Data health scores make quality easier to understand and prioritize. Knowing where information is strong and where weaknesses exist helps organizations focus improvement where it matters most.
@pilogian.bsky.socialGolden Records
A trusted golden record creates a consistent view of critical enterprise information. Reducing duplication and inconsistency strengthens the foundation for analytics, integration, and confident decisions.
@pilogian.bsky.socialReal-Time Data Quality
Data quality is strongest when organizations can identify issues as they happen. Continuous visibility helps teams act earlier, maintain trusted information, and make better decisions.
@belgianopendata.bsky.social
@numberchecker.bsky.socialIs your CSV structure silently killing your ingestion before the first record is even checked? Treating bulk uploads as a schema-validation gateway ensures your data is ready for processing. Learn more at https://numberchecker.ai/?utm_source=bsky #DataQuality #GTM
@walookup.bsky.socialIs your CRM data quality decaying faster than your marketing team can keep up? Monitoring 7-day registration trends can help you spot lead quality shifts before they impact your outreach. How do you handle list hygiene during intake? https://walookup.com/?utm_source=bsky #DataQuality #MarketingOps
@foot.io10,207 phantom 0-0 'finished' matches were polluting form guides; we published the bug and the fix.
@pilogian.bsky.socialData quality is not a one-time cleanup. Continuous validation, enrichment and monitoring help keep information reliable as the business evolves—turning trusted data into an ongoing driver of improvement.
@pilogian.bsky.socialConnected supply chains depend on connected data. Harmonized material, supplier and asset information can link procurement, inventory, maintenance and operations for greater consistency across the enterprise.
@pilogian.bsky.socialPoor data creates hidden waste through duplicates, missing information and rework. Better data quality transforms fragmented information into trusted data that can support more efficient operations and better decisions.
@pilogian.bsky.socialReliable maintenance depends on reliable asset information. Accurate equipment records, specifications and maintenance data give teams a stronger foundation for planning, reliability and operational performance.
@pilogian.bsky.socialStrong SAP transformation starts with strong data. AI-assisted profiling, cleansing, mapping and validation can help organizations prepare information for a more consistent and governed transformation journey.
@pilogian.bsky.socialData governance is moving beyond control toward action. AI can help identify data issues, support remediation and activate workflows—making trusted information more responsive to real business needs.
@walookup.bsky.socialAre you defaulting every WhatsApp check to a generic existence ping? You might be paying for data you don't need. By surgically selecting your service_type (ws, ws_avatar, or ws_business), you can optimize your API usage. Learn more at https://walookup.com/?utm_source=bsky #DataQuality #WhatsAppAPI
@checknumber.bsky.socialIs your bulk list 'active' or just 'present'? A registration signal confirms a number is reachable on WhatsApp right now, but it doesn't prove human activity. Use activity signals to better prioritize your outreach. https://checknumber.ai/?utm_source=bsky #DataQuality #MessagingOps
@openknowledgegraphs.com🔎 Missing names? Dates stored as text? Too many values where you expected one?
SHACL lets you define rules for RDF data and check your graph against them—catching problems before they reach your applications.
3 spreadsheet cleanup mistakes: deleting conflicting IDs, turning unknown amounts into zero, and changing raw data without a backup. Keep an audit trail and reconcile totals. Practise with synthetic data: https://payhip.com/b/R0ove #Excel #DataQuality
Excel duplicate check: enter =COUNTIF($A$2:$A$6,A2), then fill down. A result above 1 flags a repeated ID. Compare other fields before deleting—conflicting records need review. Free lesson: https://payhip.com/RemoteSkillsLab/blog/news #Excel #DataQuality