AWS Glue Data Qualityの前処理クエリ機能が一般提供開始！データ検証前の事前変換やフィルタリングがパイプライン内で直接実行可能に。
・派生カラム作成や計算を統合
・事前処理ジョブの実装が不要に
・データ品質検証を大幅効率化
@sarubot.bsky.social
@sarubot.bsky.socialAWS Glue Data Qualityの前処理クエリ機能が一般提供開始！データ検証前の事前変換やフィルタリングがパイプライン内で直接実行可能に。
・派生カラム作成や計算を統合
・事前処理ジョブの実装が不要に
・データ品質検証を大幅効率化
@worldeconomics.bsky.socialWorld population estimates are guesswork. 58% of Nigerian births go unrecorded. Governments spend billions on policies built on fictional numbers. #Population #BigGovernment #DataQuality https://www.worldeconomics.com/Thoughts/The-Worlds-Population-Today.aspx
@programintegrity.bsky.socialPIA's President Linda Miller was quoted in the @washingtonsun.bsky.social on the WH fraud ledger. 'The fraud ledger appears to be primarily a piece of propaganda that provides very little, if any, meaningful information'.
www.washingtonsun.com/trump-white-...
#dataquality #transparency #fraud
@numberchecker.bsky.socialStop treating Snapchat registration status as an indicator of 'who' is behind an account. Instead, use it as a technical diagnostic for your CRM's regional consistency. It provides data context, not identity. Learn more at https://numberchecker.ai/?utm_source=bsky #DataQuality #CRM
@checknumber.bsky.socialStop asking 'is this number active?' and start asking 'what does this registration signal tell me about my list's lifecycle?' WhatsApp registration presence is a signal of account maturity, not a guarantee of reachability. https://checknumber.ai/?utm_source=bsky #DataQuality #ListHygiene
@velmere.bsky.socialAn unexplained dash leaves the reader guessing.
For Velmère, I’m working toward a clearer missing-data experience: what’s missing, which conclusion it limits, and what evidence to check next.
What’s the most useful explanation you’ve seen in an incomplete report?
@pilogian.bsky.socialOperational data becomes more valuable when it can be connected to the assets and maintenance activities behind the numbers.
Better information creates better context for understanding reliability, downtime and OEE.
@pilogian.bsky.socialA new ERP does not automatically create better data.
The transformation opportunity is to clean, standardize, validate and govern information before it enters the new environment—so modernization begins with a stronger foundation.
@pilogian.bsky.socialProcurement teams should not have to decode inconsistent descriptions to find what the business needs.
AI-assisted search, duplicate detection and governed purchasing workflows can connect requirements with better information and better decisions.
@pilogian.bsky.socialPredictive maintenance depends on the information surrounding the asset.
Clean equipment records, structured hierarchies, maintenance metadata and reliable relationships give AI the context needed to move from data to useful operational insight.
@marcus-schuler.comBuilt by a Google spatial computing PM. The visual grammar works. But public feeds alone have hard limits on coverage and timeliness. This is the constraint that separates a demo from a system. #Geospatial #DataQuality https://www.implicator.ai/gods-eye-view-planes-ships-palantir/
@tgvalidator.bsky.socialStop confusing carrier line-type intelligence with Telegram registration status. A carrier check identifies the line type, while a registration check confirms Telegram presence; using both as distinct layers creates better data context. https://tgvalidator.com/?utm_source=bsky #DataQuality #Telegram
@checknumber.bsky.socialStop dumping raw validation results into a single 'status' field. Map distinct registration states to specific CRM fields to keep segmentation clean. For example, store 'registered' vs 'unregistered' in separate columns for easier routing. https://checknumber.ai/?utm_source=bsky #DataQuality #CRM
@citizenptnewsct.bsky.socialDataHaven is taking a phased approach to create a maternal health report card, but concerns are rising that it may overlook crucial community experiences.
Read the full story
#CT #CitizenPortal #DataQuality #HealthEquity #CommunityRepresentation
@dinabridge.bsky.socialReliable systems depend on reliable data.
More than 56% of data professionals cite poor data quality as a significant challenge.
Start with trusted data.
#DataQuality #DataEngineering #DataReliability #Observability #DinaBridge
@pilogian.bsky.socialThe best inventory strategy depends on trustworthy material information.
Data harmonization and multi-dimensional analysis can help organizations balance availability, criticality, demand and cost.
@pilogian.bsky.socialA new ERP does not automatically create better data.
Migration is the opportunity to remove duplicates, fill missing attributes, standardize classifications and validate information before it enters the new environment.
@pilogian.bsky.socialAsset management becomes stronger when information follows the asset throughout its life.
From acquisition and onboarding to operation, maintenance, optimization and retirement, each stage creates information that can influence the next decision.
@pilogian.bsky.socialStrong data governance answers practical questions:
Who owns the data?
Who can change it?
What rules validate it?
Who approves it?
Embedding these controls into workflows turns governance into an operating capability.
