Unstructured and Inconsistent Data contains differences in codes or names etc., that remains in the systems or files constant. All you need a proper Data Strategy or process to make the Data well structured

Why Data Quality is Important?

Quality Data is all about valuable data that is readily available to businesses. That data helps evaluate your market position, understand your audience, identify risks and growth opportunities, understand trends and dynamics, build effective strategies, and eventually drive organization growth.

Bad data leads to wrong decisions, disgruntled customers, high costs, wrong targeting, etc. Even technologies such as artificial intelligence (AI) and machine-learning (ML) require accurate data to function properly.

Data-driven organizations need to trust their data, but the scenario looks grim: some 55% of business leaders don't trust their data assets.

Attributes of Data Used to Derive Business outcomes

Accurate: Data with no errors or outdated information, redundancies, or typos

Complete: Data with no missing fields, values, or incomplete information.

Relevant: Data that's helpful for your set goals

Valid: Data that's verified and validated and therefore trustworthy

Consistent: Data that remains consistent and aligns with your format

Real-time: Data that's updated consistently and regularly

Let's begin with what PiLog Data Quality Hub constitutes high-quality data

iData Acquisition

• Pre-built data connectors
• Real-time data acquisition and Ingestion
• Data discovery, modelling, mining, profiling, assessment, analysis, and visualization
• Core ETL features + Data streaming

iData Analytics

• Business performance insights
• CxO Dashboards (Spend, Performance, ROI, KPIs, SLAs)
• On-demand real-time Infographics and Cockpit Views

iData Integration

• Seamless Data Integration, Synchronization, Distribution, Syndication, Orchestration, Micro-Service (APIs)

iData Harmonization

• Data loading and profiling
• Data cleansing, standardization, normalization, enrichment, auto-corrections / updates from repositories and libraries, AI + ML
• Data transformation and Data quality assessment
• Data Quality Establishment & Compliance

iData Governance

• Batch or record-based data management functions such as Create, Change, Extend, Delete, Undelete, Merge, Split, Match, Validate, Archive, Unarchive, Link, Delink as per the Business Rules of the data objects compliant with Target systems
• Data Quality establishment: SLAs, KPIs, Infographics

PiLog Data Quality Hub Architecture