High-Level Data Requirements Template – Free Word Download

High-Level Data Requirements Template – Free Word Download – Copy and Paste Wording Below

Introduction

In the early stages of a project, the focus is often on features, timelines, and budgets. However, in our digital economy, data is the fuel that powers every feature and informs every decision. The High-Level Data Requirements (HLDR) document is a critical bridge between the business stakeholders and the technical implementation teams. It is not a database schema or a technical specification; rather, it is a business-centric definition of the “what, where, how much, and how good” of the data involved in the project.

This template is designed to capture data needs before a single line of code is written or a server is provisioned. By defining these requirements early, you avoid the common pitfall of building a beautiful application that cannot handle the volume of data required, or producing reports that lack the specific fields the CEO needs to see. This document forces the project team to think about data not just as a byproduct of the software, but as a core asset that must be managed, protected, and utilized effectively.

The HLDR focuses on “Conceptual Data Modeling.” It asks questions like: What are the main “things” (entities) we are tracking? How do they relate to each other? How much history do we need to keep? What are the rules for data quality? Completing this document ensures that the Data Architects and Database Administrators (DBAs) have a clear blueprint to build the underlying structures that will support your project’s success.

Section 1: Project Context and Data Objectives

1.1 Project Overview

Instructions:

Provide a brief summary of the project with a specific lens on data. Why is this project being undertaken from a data perspective?

  • Project Name: [Enter Name]
  • Project Sponsor: [Name]
  • Key Data Stakeholders: [List the business units that will use this data, e.g., Marketing, Finance, Logistics]

Example:

“The ‘Global Logistics Tracker’ project aims to consolidate shipment data from three regional systems into a single global view. The primary data objective is to provide real-time visibility into inventory levels and reduce data latency from 24 hours to under 15 minutes.”

1.2 Business Questions to Answer

Instructions:

Data exists to answer questions. List the top 5-10 critical business questions that this system must be able to answer. This helps the technical team understand the “Why” behind the data requirements.

  1. Example: “Which suppliers have the highest late-delivery rates?”
  2. Example: “What is the average lifetime value of a customer by region?”
  3. [Insert Question]
  4. [Insert Question]
  5. [Insert Question]

Guidance:

If you cannot list these questions, you are not ready to build the system. These questions determine how the data must be structured and indexed for reporting.

Section 2: Data Sources and Integration

2.1 Data Origins (Inbound)

Instructions:

Where will the data come from? Is it being typed in by humans? Is it coming from sensors? Is it being pulled from a legacy mainframe?

Table 2.1: Data Source Inventory

Source NameTypeDescriptionFrequencyOwner
Example: SAP ERPInternal SystemMaster customer recordsNightly BatchIT Finance
Example: Web FormsUser InputNew lead registrationsReal-timeMarketing
Example: Twitter APIExternal FeedSocial sentiment dataEvery 10 minsDigital Team
[Source Name][Type][Description][Frequency][Owner]

Tips for Success:

Be wary of “Excel Spreadsheets” as a data source. If a business process relies on a manual Excel file stored on someone’s desktop, this is a high-risk integration point. Flag it immediately.

2.2 Data Destinations (Outbound)

Instructions:

Where does the data go after we process it? Does it feed a dashboard? Does it go to a partner?

  • Destination 1: [e.g., Executive Tableau Dashboard]
  • Destination 2: [e.g., Data Warehouse / Data Lake]
  • Destination 3: [e.g., Third-party Logistics Provider (via API)]

Section 3: Conceptual Data Model (Entities and Relationships)

Instructions:

This is the most critical section. You need to identify the core “Entities” (the nouns) of the system and how they relate to each other. You do not need to list every single attribute (like “Phone Number” or “Zip Code”), but you must identify the major objects.Getty Images

3.1 Key Data Entities

List the major objects:

  • Entity A: [e.g., Customer] – Definition: An individual or company that purchases goods.
  • Entity B: [e.g., Order] – Definition: A confirmed request for goods.
  • Entity C: [e.g., Product] – Definition: An item available for sale.
  • Entity D: [e.g., Invoice] – Definition: A request for payment linked to an Order.

3.2 High-Level Relationships

Describe how they interact:

  • Relationship: One Customer can place many Orders. (1:Many)
  • Relationship: One Order can contain many Products. (Many:Many)
  • Relationship: One Order generates exactly one Invoice. (1:1)

Why is this important?

Defining “Many-to-Many” relationships (like Products in Orders) is complex to build. Identifying these early helps the architect design the correct “join tables” or data structures.

Section 4: Volumetrics and Performance

4.1 Storage Estimates (Volume)

Instructions:

How big will this get? This determines the infrastructure budget (disk space, cloud storage tiers).

  • Initial Data Load: [How much historical data are we migrating? e.g., 5 Years of history = 10 TB]
  • Daily Growth Rate: [How many new records per day? e.g., 10,000 orders/day]
  • Estimated Annual Growth: [e.g., Database will grow by 500 GB per year]

Guidance:

Always add a buffer (typically 20-30%) to your estimates. Data tends to grow faster than expected.

4.2 Velocity and Latency

Instructions:

How fast does the data need to move?

  • Real-Time Requirements: [Does any part of the system need sub-second response? e.g., Stock trading, Fraud detection]
  • Batch Processing Windows: [If we process data at night, how much time do we have? e.g., “The nightly job must finish between 2:00 AM and 5:00 AM.”]
  • User Concurrency: [How many users will be querying the data at the same time? e.g., 500 concurrent support agents]

Section 5: Data Quality Requirements

Instructions:

Data is useless if it is wrong. Define the rules that the system must enforce to keep the data clean.

5.1 Quality Dimensions

Table 5.1: Data Quality Rules

DimensionRequirement / RuleExample
UniquenessNo duplicate records allowed for Key Entities.A customer cannot have two accounts with the same email address.
CompletenessMandatory fields must be populated.An Order cannot be saved without a Shipping Address.
ConsistencyData must match across systems.The ‘Total Price’ in the Order system must match the ‘Amount’ in the Invoice system.
ValidityData must conform to specific formats.Zip codes must be 5 digits (US) or valid alphanumeric (UK).
TimelinessData must be available within X minutes.Inventory levels must be updated on the website within 5 minutes of a sale.

5.2 Handling Bad Data

Instructions:

What happens when bad data tries to enter the system?

  • Reject: The system throws an error and refuses to save. (Best for critical errors).
  • Flag: The system saves the data but marks it as “Draft” or “Needs Review.”
  • Clean: The system attempts to auto-correct (e.g., formatting a phone number).

Recommendation:

Be careful with “Auto-Cleaning.” It can sometimes corrupt valid data. “Reject” or “Flag” is safer.

Section 6: Data Migration Strategy

Instructions:

If this is a replacement system, how are we moving the old data to the new home?

6.1 Historical Scope

  • How far back do we go? [e.g., “We will migrate the last 3 years of active orders. Older orders will be archived.”]
  • Justification: [Why 3 years? e.g., “Legal warranty period is 3 years.”]

6.2 Migration Approach

  • Big Bang: [Move everything over a weekend and switch on Monday.]
  • Phased: [Move one region or one module at a time.]
  • Parallel Run: [Enter data into both systems for a month to compare.]

Risk Note:

Data migration is traditionally the most underestimated task in software projects. It almost always takes longer than expected due to poor data quality in the legacy system (e.g., finding text in date fields).

Section 7: Data Retention and Archiving Policy

Instructions:

You cannot keep data forever. It costs money and creates legal liability.

7.1 Active Data (Hot)

  • Definition: Data needed for day-to-day operations.
  • Retention Period: [e.g., Current Fiscal Year + 1 Year]
  • Storage: High-speed, expensive SSD storage.

7.2 Archival Data (Cold)

  • Definition: Data needed only for regulatory reporting or occasional lookups.
  • Retention Period: [e.g., 7 Years for Tax records]
  • Storage: Low-cost, slower storage (e.g., Amazon Glacier, Tape).

7.3 Purge Policy

  • Requirement: [When is data deleted permanently?]
  • Mechanism: [e.g., “Automated script runs every quarter to hard-delete records older than 7 years.”]

Section 8: Reporting and Analytics Requirements

Instructions:

This section details the “output” side of the data.

8.1 Standard Reports

List the reports that must be pre-built:

  1. Report Name: [e.g., Monthly Sales Summary]
    • Frequency: Monthly
    • Audience: Finance Director
    • Key Metrics: Total Revenue, COGS, Margin %
  2. Report Name: [e.g., Daily Inventory Stock-out]
    • Frequency: Daily (Morning)
    • Audience: Warehouse Manager
    • Key Metrics: SKU, Location, Days out of stock

8.2 Ad-Hoc Capabilities

  • Requirement: Does the business need a tool to drag-and-drop fields to create their own reports? [Yes/No]
  • Tool Preference: [e.g., PowerBI, Tableau, Excel plugin]

Section 9: Security and Compliance Requirements

Instructions:

Refer back to the Information Classification Assessment and Privacy Screening. Summarize the high-level security needs here.

  • Encryption: [Must data be encrypted at rest?]
  • Masking: [Must credit card numbers be masked in reports?]
  • Residency: [Must the data stay within the EU/USA?]
  • Access Control: [Do we need Row-Level Security? e.g., A salesperson can only see their own sales, not the whole company’s.]

Section 10: Sign-Off and Approvals

Instructions:

The Business Lead must sign this to confirm that these requirements represent the business need. The Technical Lead must sign to confirm that these requirements are feasible to build.

10.1 Business Approval

“I confirm that the data requirements listed above accurately reflect the business needs of my department.”

  • Name: ___________________________
  • Title: ___________________________
  • Date: ____________________________

10.2 Technical Approval

“I confirm that these requirements are understood and technically feasible within the proposed architecture.”

  • Name: ___________________________
  • Title: ___________________________
  • Date: ____________________________

Conclusion – High-Level Data Requirements Template – Free Word Download

The High-Level Data Requirements document is a living specification. As the project moves into the Detailed Design phase, these requirements will be decomposed into specific field definitions, API contracts, and database schemas. However, the core principles defined here the entities, the quality rules, and the retention policies should remain stable.

By documenting these requirements now, you have created a safeguard against “Data Debt.” You are ensuring that the system is built on a solid foundation of clean, well-structured, and compliant data. This investment in planning will pay dividends throughout the life of the application, resulting in faster performance, more accurate reporting, and a higher level of trust from the business users.

Meta Description:

A template for defining high-level data requirements, covering data sources, conceptual models, volumetrics, quality rules, and retention policies for project planning.

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