Data Health Assessment Services for Enterprise Master Data Quality
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
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
What is Data Health Assessment
Turning Data Uncertainty into Actionable Insight.
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.
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.
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
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.
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.
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.
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.
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.
Frequently Asked Questions
The primary goal is to evaluate the As-Is quality of your enterprise data to identify specific challenges hindering operational efficiency. For asset-heavy industries, this means ensuring that complex asset hierarchies, maintenance records, and spare parts data are accurate, governed, and ready to support critical operations like SAP S/4HANA migrations or ERP upgrades.
Asset-intensive businesses face unique data risks, including complex asset taxonomies, disconnected data across legacy ERP/CMMS/EAM systems, and time-sensitive maintenance histories. A specialized assessment identifies these specific pain points such as duplicate equipment records or missing calibration data that can lead to downtime, safety issues, and compliance failures if left unaddressed.
The assessment covers all Master Data Management (MDM) domains critical to your operations, including Material, Vendor, Service, Asset, and Customer data. For asset-intensive clients, we place a heavy emphasis on Asset and Material data to ensure proper classification, interchangeability tracking, and lifecycle management.Â
Migrating legacy data without a health check often results in garbage in, garbage out. Our assessment analyzes data quantity and complexity to identify issues like duplicates, improper units of measure (UoM), and missing values. This allows you to clean and filter data before migration, ensuring a smoother transition to SAP S/4HANA and reducing post-go-live support costs.Â
You will receive a comprehensive report detailing the existing data quality, the business impact of data errors, and actionable recommendations. This includes a detailed gap analysis and a blueprint for passive data governance, helping you prioritize steps to improve data accuracy across your systems.Â
Our framework combines qualitative service engagement with quantitative application-backed analysis. We perform statistical checks on your data to verify accuracy and completeness. For ERP users, we can also build fact-based reports within your Information Steward to help teams review and address errors effectively.Â
Yes. In regulated industries like oil & gas, aerospace, and utilities, data traceability is mandatory. The assessment identifies gaps in data governance and traceability, helping you meet strict regulatory mandates and ensuring your organization is audit-ready with reliable, data-driven reporting.
The timeline depends on the volume and complexity of your data, but the assessment is designed to be a rapid, high-impact engagement. It allows you to quickly understand your data landscape and prepare for larger data quality initiatives or system transformations without long delays.
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.
