Why Excel files with mostly numbers are excluded from training data for Relativity Project Management materials.

Relativity Project Management Specialist: What kind of documents should you include in training data—and what should you leave out?

Let me explain a small, important idea that often trips people up: not all documents are created equal when you’re building training data for language-aware tools. In project environments, we juggle reports, slides, spiffy Word briefs, and yes, plenty of Excel spreadsheets filled with numbers. The question isn’t about aesthetics or neatness; it’s about how the data helps a model understand language, context, and meaning. When you’re shaping a training set, you want narratives, descriptions, and explanations. You don’t want to drown the model in raw numbers that say little about how people talk, argue, persuade, or decide.

Here’s the thing about training data in a PM context

• Text carries meaning. Documents with words tell stories about goals, constraints, risks, and decisions. They reveal terminology, phrasing, and how experts communicate complex ideas concisely.

• Numbers alone are shadows of meaning. Spreadsheets, dashboards, and numeric logs are fantastic for tracking metrics, but they don’t teach a model how to interpret language, nuance, or the way people describe a risk versus a constraint.

• Real-world work blends both. A polished project brief might summarize milestones with a table of numbers, but the textual portion explains the why and the how. If you want a model that can help with summarization, questions, or planning guidance, you need robust textual content as part of the training mix.

What types of documents usually help (and why)

Think of your training set as a library of language cues. You want documents that show how people frame ideas, define terms, and communicate decisions. Here are the common types that tend to contribute value:

• Word documents and PDFs with narratives. These are where definitions, requirements, and rationale live. They show how professionals describe objectives, constraints, and trade-offs.

• Reports that weave context with visuals. A well-crafted report isn’t just data – it explains what the data means, what happened, and what’s next. The writing in these reports helps models learn to connect numbers to commentary.

• Presentations with notes. Slides are rich with key messages and succinct explanations. Their speaker notes often reveal how to translate a complex concept into plain language.

• Project deliverables that describe processes. Documentation outlining governance, risk management, or change control demonstrates how language is used to guide action.

Why Excel files with mostly numbers should be left out of the primary training source

Now, to the core point: which document type is best excluded from the main training set for this kind of work? Excel files that are predominantly numeric. Here’s the rationale, plainly:

• They teach you less about language and semantics. If a dataset is mostly numbers, you miss the chance to learn about how people describe trends, explain causes, or articulate constraints in words.

• Language models need textual variety. Textual files expose the model to different sentence structures, synonyms, and domain-specific terms. That variety is what makes the model better at understanding context and nuance.

• Training efficiency and clarity. If you flood the training with numeric-heavy files, you risk the model focusing on patterns in numbers rather than language. That can dilute performance on tasks that involve reading, summarizing, and interpreting textual content.

A practical way to think about it: you’re training a guide for people who read, talk, and decide. Numbers are important for measurement and dashboards, yes. But the “how” of communication—the reasons, the implications, the planning language—lives in the words. You want the model to be fluent in that language.

How to curate a strong training set without getting lost in the weeds

If you’re building or refining a training set for Relativity-related workflows, here are some practical moves that keep the focus where it counts:

• Prioritize textual depth. Favor documents that explain, justify, or describe. Look for briefs that lay out goals, constraints, and decisions in plain language.

• Include a spectrum of document types. A mix of Word docs, reports, and slide decks ensures exposure to different writing styles and terminologies. But keep the primary emphasis on language-rich materials.

• Separate data types. If your repository contains Excel files with mostly numbers, treat them as data sources for other purposes (e.g., feature engineering in separate pipelines) rather than as the backbone of text-centric training.

• Annotate for context. If you can, add annotations that highlight key terms, definitions, and decision points. This helps the model learn not just what is being said, but why it matters.

• Use balanced samples. Avoid overloading the training set with a single document style. A balanced mix improves robustness when the model encounters real-world documents in PM tasks.

A quick, practical checklist for document selection

• Does the document primarily tell a story, explain a decision, or define terms? If yes, it earns a front-row seat.

• Does it include domain-specific vocabulary you’d want the model to recognize, such as risk, constraint, stakeholder, milestone, or deliverable? If yes, great.

• Are there diagrams or charts? They’re useful, but the accompanying narrative matters more for training language understanding.

• Is the content reusable across multiple projects, not just a single case study? Broad relevance helps generalization.

• Is the document free from sensitive or confidential details that can’t be shared in a training environment? Always a must.

What about Excel files? How to handle them responsibly

Excel sheets aren’t villains. They’re perfect for numeric tracking, formulas, and scenario analysis. The trick is to keep them separate from the core language-learning streams. You can still extract value from them in other ways:

• Use them for numeric features in models that handle tabular data, separate from text-focused training.

• Create descriptive metadata for Excel datasets. A short, clear description of what each sheet represents can be a good companion for cross-domain models.

• If you must reference numbers in language tasks, pair the numbers with explanatory text. For example, a narrative paragraph that accompanies a chart helps connect the numbers to the story.

A few real-world analogies

• If a project doc is a conversation, Excel is a set of numbers on a scoreboard. The conversation instructs and persuades; the numbers show progress. You want the conversation to teach the model how language shapes action.

• Think of a training set like a chef’s pantry. You want a range of spices (terminology, context, semantics), not just a rack of plain salt (numbers). You can still cook meaningful dishes with numeric ingredients, but you’ll rely on the language-heavy items to define the recipe.

Debunking a few common myths

• More data always helps. Not true when the data isn’t aligned with the task. If the aim is language understanding and contextual reasoning, textual variety matters more than sheer volume of numbers.

• Any document can contribute equally. In practice, not all content adds value to language-focused training. Some documents are great for other aspects of analytics, but they won’t teach a model to read and interpret language as effectively.

• You must exclude all numbers. Not at all. Numbers are essential for dashboards, forecasting, and performance measurement. They just belong in separate data-processing pipelines rather than the core text-training stream.

Bringing it back to your day-to-day PM toolkit

The bottom line is simple: when building a training set for language-enabled tools that support Relativity-style project work, lean toward documents that convey meaning through text. Exclude Excel files with mostly numbers from the primary training data because they don’t teach the model how people talk, reason, and decide about projects. Keep the narrative-rich materials in the spotlight, and use numeric data where it belongs—alongside code, models, and analytics pipelines that handle numbers directly.

A small, thoughtful habit you can adopt

As you assemble documentation for training, ask this quick question before adding any file: Does this document teach the model how to interpret language in a project context? If the answer is yes, it stays. If the document mainly presents rows and columns without explaining the story behind them, it’s better kept separate for numeric modeling tasks.

To close with a sense of clarity and purpose

In the work of Relativity-style project management, language is the bridge that connects intention to action. Documents that speak in clear terms—without burying the point in jargon—are gold for training tools that need to understand, summarize, and guide. Excel sheets with mostly numbers? They’re valuable in their own right, but for a language-focused training set, they belong to a different corner of the data universe.

If you’d like, I can help outline a lightweight framework for curating a training data repository that prioritizes textual richness while still keeping numbers accessible for separate analytical tasks. After all, a well-organized data ecosystem makes everyone’s job smoother—and that’s good for any project, big or small.