Your organization is planning to implement AI. The budget is approved. The teams are excited. But there is a problem hiding in your data — and if you don’t address it first, your AI investment will underperform before it starts. This demonstration walks through exactly what that problem looks like, how to find it, and how to fix it. No coding experience required.
Deltek Open Plan (OPP) is the scheduling engine behind some of the largest defense programs in the world. It manages Integrated Master Schedules — the detailed, task-by-task roadmaps that define how a program is planned, resourced, and executed. It tracks thousands of individual activities across the Work Breakdown Structure (WBS), assigns resources and budgets, and records planned versus actual performance over time.
The three numbers at the heart of every OPP export are the foundation of Earned Value Management. BCWS (Budgeted Cost of Work Scheduled) is what you planned to spend by this point in time — your budget baseline. BCWP (Budgeted Cost of Work Performed) is what you actually earned by completing work — the value you’ve produced. ACWP (Actual Cost of Work Performed) is what you actually spent to produce that work. Divide BCWP by BCWS and you get the Schedule Performance Index (SPI). Divide BCWP by ACWP and you get the Cost Performance Index (CPI). These are the vital signs of a program’s health.
This data is the primary input for any AI system that would predict schedule delays, forecast cost overruns, detect anomalies, or recommend program adjustments. The quality of this data directly determines the quality of any AI output built on top of it.
What you’re about to see is a realistic representation of what OPP export data looks like in practice — not after it has been cleaned, validated, or processed. This is the raw feed. Look closely.
This is your data. Imported directly from Open Plan. Exactly as the system exported it. Click NEXT to ask it a question.
SQL — Structured Query Language — is how we ask questions of a database. Think of it as a very precise, very literal assistant. You write out exactly what you want to find, what conditions it must meet, and how you want it organized. The database returns only what matches. Every modern reporting tool, dashboard, and AI system uses SQL (or something very similar) at its foundation.
The question we’re asking is one every program manager cares about: which active tasks are simultaneously behind schedule AND over budget? In Earned Value terms, we want tasks where both the Schedule Performance Index (SPI) and Cost Performance Index (CPI) fall below 1.0. These are the tasks that need immediate attention — and they’re the first thing a predictive AI model would focus on.
This query is well-written. The logic is sound. But the data it’s running against is not clean. Watch what happens.
Artificial intelligence doesn’t think. It learns patterns from examples. If those examples contain errors, inconsistencies, and gaps, the AI learns those errors as valid patterns. This is the single most underestimated risk in enterprise AI deployment — not that the AI will go rogue, but that it will quietly, confidently be wrong because the data it learned from was quietly, confidently wrong.
Below is your Integrated Master Schedule again. This time, every data quality issue has been identified and annotated. Click any highlighted cell or row to see exactly what the problem is, why it happened, and what it would do to an AI system built on this data. Watch your Data Health Score in the upper right as each issue is revealed.
A Python script is a set of instructions written in a programming language called Python — one of the most widely used languages in data science and AI. Think of it like a detailed checklist given to a very fast, very literal, very tireless assistant. You define the rules. The script applies them to every single row of data, every single time, in seconds — without missing anything, without getting tired, and without needing to understand context it wasn’t programmed to handle.
The AI Readiness Auditor below was written specifically for Deltek Open Plan exports. It codifies the 10 data quality rules we just examined into automated checks. Every function is annotated in plain English so any stakeholder can understand what the script is doing and why. This is not a black box — it is a documented, auditable, repeatable quality gate.
In practice, this script would run automatically each time a new OPP export is produced — as part of a scheduled task, a CI/CD pipeline, or a file-watcher monitoring your exports folder. It produces a structured JSON report that feeds directly into dashboards, alerts, and the AI pipeline gating system.
Finding data problems is the first half of the solution. Fixing them is the second. But not all data problems are the same kind of problem — and this distinction is critical in a defense program environment where data drives contractual reporting, government oversight, and earned value system compliance.
The AI Readiness Auditor separates its findings into two queues: AUTO-CORRECTABLE issues, where the fix is unambiguous and a computer can safely apply it, and HUMAN REVIEW REQUIRED issues, where a decision must be made by a credentialed program controls professional before anything is changed. Automating a decision that requires human judgment — especially on CPR or IPMR data — is not a feature. It’s a liability.
Data cleaning is not a one-time project. It is an ongoing operational discipline. In a live defense program, your Open Plan database is updated continuously — schedulers log progress, finance systems post actuals, subcontractors submit status. Every update is an opportunity for new bad data to enter the system. Without automated monitoring, data quality degrades silently, and the AI systems built on top of it degrade with it.
The monitoring script below is a watchdog. It runs continuously in the background, watching a designated folder where Open Plan exports are dropped. The moment a new export file appears, it automatically triggers the AI Readiness Auditor, applies auto-corrections, queues human review items, sends alerts to the program controls team, and forwards clean data to the AI pipeline — all within seconds of the file landing.
This is what a mature AI data pipeline looks like: not a one-time integration, but a self-monitoring, self-correcting quality gate that enforces data standards continuously, creates an auditable record of every data event, and ensures that the only data an AI model ever sees is data that has passed inspection.
The demonstrations you’ve just seen are not hypothetical. Every data quality failure shown here — the null WBS codes, the duplicate task IDs, the zero-cost anomalies, the names formatted five different ways — exists in production program data today. On programs that are preparing to implement AI. On programs that are already feeding this data into dashboards that leadership trusts. The question is not whether your data has these problems. The question is whether you know about them.