Session 12.4: Data Only You Have

Course → Module 12: USP & Positioning

Session 4 of 7

AI Can Analyze Data. AI Cannot Acquire Data.

You have data. Maybe it is sales data. Customer feedback. Production metrics. Survey results. Website analytics. Email response rates. Maybe it is informal: patterns you have noticed over years of work that nobody has published a paper about.

AI can analyze data you give it. It can find patterns, build visualizations, and generate insights. What it cannot do is walk into your business, observe your operations, talk to your customers, and collect the numbers that make your perspective unique. Data acquisition requires physical presence, business relationships, and time. AI has none of these.

Proprietary Data: Any information you have access to that is not publicly available on the internet. It does not need to be formal research data. Informal observations, internal metrics, client feedback, and pattern recognition from years of practice all qualify. When used in content (with appropriate anonymization), this data builds authority that generic AI content cannot match.

Categories of Data Assets

Most people underestimate how much proprietary data they have. The table below maps common data types to their content applications.

Data Type	Where It Lives	Content Application	Anonymization Needed?
Sales/revenue metrics	Accounting software, CRM	Case studies, trend analysis, benchmarking	Usually. Remove client names, round numbers if needed.
Customer feedback	Email, support tickets, surveys	Product insights, market analysis, problem identification	Yes. Remove all identifying information.
Production metrics	Internal dashboards, spreadsheets	Efficiency analysis, process improvement stories	Moderate. Share percentages, not absolute numbers if sensitive.
A/B test results	Analytics platforms, experiment logs	Evidence-based recommendations, myth-busting	Low. Results are usually safe to share.
Informal observations	Your memory, meeting notes, journals	Trend identification, contrarian takes, industry commentary	Low. But be specific about the observation context.
Process documentation	SOPs, internal wikis, training materials	How-to content, framework development, tool recommendations	Moderate. Strip proprietary methods if applicable.

The Data-to-Content Pipeline

flowchart TD A["Raw Data
(metrics, feedback, observations)"] --> B["Anonymize & Aggregate
(remove identifiers, round numbers)"] B --> C["Identify Insight
(what pattern does this reveal?)"] C --> D{"Does this contradict
conventional wisdom?"} D -->|Yes| E["High-Value Content
(contrarian piece with evidence)"] D -->|No| F{"Does this add
specificity to generic advice?"} F -->|Yes| G["Moderate-Value Content
(data-backed confirmation)"] F -->|No| H["Low-Value Content
(consider skipping)"] style E fill:#6b8f71,color:#111 style G fill:#c8a882,color:#111 style H fill:#c47a5a,color:#111

The highest-value data content contradicts conventional wisdom. When your numbers show that the commonly accepted approach does not work, or works differently than expected, you have content that only you can produce and that will attract attention precisely because it challenges the default.

How to Use Data in Content

Data without context is a spreadsheet. Data with context is authority. The format matters:

State the conventional wisdom. What does everyone believe? What would AI generate?
Present your data. Specific numbers, timeframes, conditions. Not "our results showed improvement." Specific: "over 6 months, across 47 client engagements, the approach produced a 23% increase in X measured by Y."
Explain what the data means. Not what it literally says (the reader can see that) but what it implies for their decisions.
Acknowledge limitations. Your data comes from your context. It may not generalize. Saying so builds credibility rather than undermining it.

The Data Assets Inventory

Before you can use your data in content, you need to know what you have. Most practitioners have never inventoried their data assets because they do not think of informal knowledge as "data."

Spend 30 minutes listing every source of information you have access to. Include formal data (analytics, CRM, financial records) and informal data (patterns you have noticed, customer conversations you remember, industry trends you have observed). For each item, note: what it is, where it lives, whether it is publishable (with anonymization), and what insight it contains that contradicts or extends conventional wisdom.

This inventory is a strategic document. Update it quarterly. It tells you what content you can produce that nobody else can.

Assignment

Audit your available data. Create a "Data Assets" inventory with columns: Data Source, Location, Publishable (Yes/No/With Anonymization), Key Insight, Contradicts Conventional Wisdom (Yes/No). Include at least 10 items across formal and informal data. For the 3 most interesting items, draft a one-paragraph content pitch that uses the data to make a specific, evidence-backed claim.

Data Only You Have