Data Health Assessment
One Assessment.One Insight. Unlimited Confidence.
Through PiLog’s Data Health Assessment, organizations gain a crystal‑clear view of the current state of their master data across material, vendor, service, asset, customer and other critical domains. The assessment quantifies data quality, pinpoints root‑cause issues and delivers actionable recommendations that are aligned with ISO‑based standards, enabling a swift path toward trusted, governed data that fuels every downstream system.
SAP ECC / ERP
Supply Chain
CRM
IoT/ Sensors
Legacy Systems
PiLog DQG Suite
Data Health Assessment Engine
Legacy Systems -> Extraction -> Analysis -> Cleansing -> Delivery
ISO 8000 Compliant
Single Source of Truth
AI Ready
What is Data Health Assessment
Turning Data Uncertainty into Actionable Insight.
PiLog’s Data Health Assessment provides a fact based, standards‑aligned snapshot of the current state of an organization’s master data. By profiling material, vendor, service, asset, customer and other critical domains, the assessment quantifies data quality gaps, pinpoints root‑cause issues and delivers a prioritized roadmap that enables governance at the point of entry and reliable data flow to downstream systems such as SAP, asset management tools, and analytics platforms.
Comprehensive Quality Scorecard
Measures completeness, accuracy, consistency, uniqueness and timeliness against ISO 8000 and industry‑specific benchmarks.
1
Root‑Cause Gap Analysis
Identifies duplicates, missing UoM, abnormal values, and classification mismatches, linking each issue to its source system.
2
ISO‑Aligned Governance Blueprint
Provides policies, stewardship roles and approval workflows that enforce data quality at entry.
3
Migration & Integration Roadmap
Prioritizes cleansing, enrichment and harmonization steps to reduce risk during ERP, SAP or IoT migrations.
4
Cost & Risk Impact Forecast
Translates data quality improvements into tangible savings, reduced audit exposure and faster time‑to‑insight.
5
The Cost of Inaction
Fragmented data after the Data Health Assessment fuels costly chaos.
Without a unified data foundation after the assessment, the organization inherits the same silos that plagued its legacy systems.
Scope & Complexity
Analysis
Quantifies data volume, domain breadth (Material, Vendor, Service, Asset, Customer, etc.) and structural complexity to size projects accurately.
Source‑Data Issue Detection
Identifies duplicates, missing units of measure, improper or unusual values, and other anomalies that can cause interface loads or migration failures.
Actionable
Recommendations & Roadmap
Delivers prioritized remediation steps (cleansing scripts, enrichment routines, taxonomy alignment) and a phased implementation plan.
Migration
Readiness Support
Prepares the environment for successful data migrations by surfacing critical issues early and offering mitigation guidance.
Operational Cost
Reduction
Highlights opportunities to simplify infrastructure, reduce redundancy and improve workflow efficiency.
End-to-End Framework
Five Steps From a Strategic Intent to Fully Integrated.
The PiLog Data Health Assessment follows a disciplined, five‑step workflow that turns raw master‑data into a clear, actionable view of its quality and readiness.
Identify all MDM domains (Material, Vendor, Service, Asset, Customer, etc.) and capture every source system – ERP, SAP, IoT, legacy databases.
Scope Definition &
Data Inventory
1
Run PiLog’s statistical engine to measure completeness, uniqueness, consistency, timeliness, and ISO‑8000 compliance.
Quantitative Profiling
& Statistical Checks
2
Map each data‑quality gap to its operational and financial impact (e.g., increased infrastructure cost, delayed migrations, audit risk).
Gap Analysis &
Business Impact
4
Analyze the profiling results to surface concrete problems such as duplicate records, missing units of measure, improper information, or unusual values that could cause interface loads or migration errors.
Source‑Data Issue
Identification
3
Provide a prescriptive set of recommendations cleansing scripts, enrichment routines, taxonomy alignment, and best‑practice processes.
Recommendations,
Blueprint & Roadmap
5
Core Capabilities That Make Data Health Assessment Stick.
Customers gain immediate, tangible value from PiLog’s Data Health Assessment, turning fragmented data into a strategic advantage.
Dynamic Scoring Engine
Applies weighted, business‑driven rules (regulatory, financial, operational) to produce a single, continuously refreshed health index for each master‑data domain.
Predictive Risk Modeling
Uses machine‑learning to forecast the downstream impact of identified anomalies (e.g., duplicate vendors causing procurement overruns) and prioritises remediation based on projected cost‑of‑risk.
Interactive Lineage Explorer
Visually maps every attribute from source system through transformation to the final golden record, allowing users to trace the origin of any quality issue in seconds.
Continuous Monitoring Hub
Establishes a real‑time health‑score feed that alerts data stewards when new records breach quality thresholds, ensuring the as is snapshot stays current.
Regulatory & Standard Mapping
Automatically aligns identified gaps with relevant standards (ISO 8000, GDPR, SOX, industry‑specific regulations) and suggests the exact control adjustments needed for compliance.
ROI & Business‑Value Projection
Translates improved data‑quality scores into quantifiable benefits reduced processing time, lower error‑handling cost, faster month‑end close and presents a clear business case for further investment.
Key Benefits for Customers
Reasons Why Organizations Choose PiLog Data Health Assessment.
Customers gain immediate, tangible value from PiLog’s Data Health Assessment, turning fragmented data into a strategic advantage.
Clear Visibility of Current Data Quality
Provides an objective, fact-based report on the as is condition of all master‑data domains (Material, Vendor, Service, Asset, Customer, etc.), enabling stakeholders to see exactly where data gaps exist.
Quantified Business Impact
Links each data quality issue to concrete operational costs, compliance risk, and migration challenges, giving a financial justification for remediation investments.
Prioritized Remediation Roadmap
Delivers actionable recommendations and a phased implementation plan (cleansing scripts, enrichment routines, taxonomy alignment) that focus on the highest‑impact fixes first.
Foundation for Passive Data Governance
Provides a blueprint for establishing governance processes at the point of entry, reducing future data entry errors and preventing the recreation of silos.
Cost & Complexity Reduction
Identifies unnecessary infrastructure and data redundancies, enabling streamlined system landscapes and lower total ownership costs.
Faster, Safer Data Migrations
Equips project teams with a clean, validated data set and detailed gap analysis, minimizing migration errors and accelerating go live timelines.
Let PiLog Group Help Solve Your Data Health Assessment Problem.
Let PiLog assess your current data landscape and quantify the efficiency, compliance, and analytics value that assessment can unlock in your organization.
