Pre‑Contract Data Audit for AIaaS (Machinery Maintenance)

Why a pre‑contract data audit is necessary

Before signing a PoC or production contract, a joint data audit acts as due diligence to match what the vendor’s pre‑trained service needs with what the client can actually provide. Failure to address data readiness is a leading cause of AI project underperformance or failure, and industry guidance increasingly centers on data‑centric AI practices that prioritize data quality and governance.

Feasibility verification

Confirm that the necessary data exists in sufficient quantity and quality (coverage, frequency, labels) to meet objectives. Discover gaps early (e.g., missing labels or sensors) and adjust scope before signing.

Expectation alignment

Make assumptions explicit: what the vendor requires vs. what the client has (sources, formats, time span, accessibility). Avoid surprises by documenting reality and responsibilities.

Risk reduction

Identify quality issues (missingness, drift, inconsistent coding) and integration constraints (cloud/on‑prem, bandwidth, security) to prevent delays and renegotiations.

Shared understanding & trust

Use the audit as a collaborative discovery workshop that builds a common language about data readiness and earns stakeholder buy‑in.

Case focus: predictive maintenance in machinery

For AIaaS offerings in predictive maintenance, vendor models typically expect time‑series sensor data (e.g., vibration, temperature, pressure, current), maintenance/failure histories (CMMS), and asset metadata. The audit matches these requirements to what’s available, noting coverage, sampling frequency, label quality, and linkages across systems.

Sensor & operational data: inventory sensors, units, sampling rates; confirm historical coverage and gaps.
Failure/maintenance records: confirm labels, codes, and consistency in CMMS/work orders.
Asset context: machine type, age, load, environment; ensure keys link across data sets.
Volume & balance: quantify history length and the rarity of failures; consider anomaly detection where labels are scarce.

Collaborative data audit process

Pre‑audit prep: client gathers data inventories; vendor provides a requirement checklist; align on scope (pilot fleet vs. single asset).
Kickoff workshop: clarify the business objective, review sources, discuss access methods and deployment constraints (cloud vs on‑prem).
Sampling & EDA: share sample extracts or secure access; profile completeness, consistency, timeliness; document quirks (sensor swaps, firmware changes).
Feedback & iteration: agree on mitigations (cleaning, mapping, proxies), and on pipeline architecture (batch vs. streaming, edge vs. cloud).
Documentation & sign‑off: record findings, gaps, actions, and responsibilities; include a data‑readiness checkpoint as a milestone before model work.

Data audit template (tabular)

This table replaces the previous checklist and is intended for joint client–vendor use. Add rows as needed.

Data Audit
Vendor				Customer
Item Number	Data Description	Data Format	Data Example	Data Example	Data Format	Data Fit to Vendor	Costs to Fix Data Gap
Service 1
1
2
3
4
5
6
7
8
9
10
Service 2
11
12
n+1

Sources

Note: Reported project failure rates vary by study and definition. Treat figures as directional and validate against your organization’s context.