“Data has no value until it changes the way you act.” — Doug Laney, author of Infonomics
From Shadows to Strategy: Unlocking Dark Data as a Product Manager
When product managers talk about “data,” most of the conversation revolves around dashboards, metrics, and structured analytics pipelines. But lurking beneath the surface is a vast and underutilized resource: dark data—the information organizations collect but rarely use. Gartner estimates that more than 80% of enterprise data falls into this category. Emails, customer support transcripts, IoT logs, survey free-text, or even unused attributes in a CRM—this is the gray matter of business intelligence, waiting to be tapped.
A Brief History of Shadows
The term “dark data” first gained traction in the early 2010s, alongside the Big Data wave. While the hype focused on Hadoop clusters and structured analytics, leaders like Doug Laney (author of Infonomics) pointed out that organizations were leaving tremendous value on the table by ignoring unstructured and underutilized information. Meanwhile, thought leaders in product and design—Marty Cagan (Inspired), Teresa Torres (Continuous Discovery Habits)—were evangelizing that successful products depend on customer insight. These two threads—latent data and product discovery—intersect in today’s mandate for product managers: treat dark data not as noise, but as an asset to be shaped into value.
What Good Looks Like
The best examples of unlocking dark data come from companies that treat it as a product in its own right.
- Customer Support to Product Roadmap: Slack famously mined its support tickets and feedback channels, tagging qualitative data to identify recurring pain points. Instead of waiting for survey data, they used “dark” free-text support logs to guide roadmap prioritization. The result? Faster iteration cycles and features that aligned with what customers actually struggled with.
- IoT in Industrial Equipment: Caterpillar, long before “AI in heavy industry” was trendy, recognized that engine logs and machine telemetry—once discarded—could inform predictive maintenance. By unlocking this data, they created not just cost savings but entirely new service offerings.
These companies didn’t just expose data—they transformed it into a better alternative to what stakeholders already had: clearer signals, proactive insights, and new revenue streams.
The Dark Side of Dark Data
Of course, the opposite also exists. Organizations often stumble when they:
- Dump Instead of Design: A telecom giant once rolled out a “dark data portal” with raw logs and no business context. Adoption cratered because executives didn’t know what to do with terabytes of JSON.
- Overpromise AI, Underdeliver Insight: Retailers have tried to pitch sentiment analysis dashboards with half-baked NLP. The output was so generic (“customers are unhappy about returns”) that leaders ignored it, retreating to Excel reports they already trusted.
The lesson: exposure without utility is noise. Unlocking dark data means giving people something better than what they already have.
Frameworks for Framing the Conversation
- McKinsey’s Three Horizons – Horizon 1: reduce risk & cost (compliance, storage); Horizon 2: enable insights (analytics, ML); Horizon 3: new revenue (data products, ecosystems).
- DAMA-DMBOK – Use the governance wheel (data quality, metadata, lineage) to show maturity steps.
- BCG Value Acceleration – Tie each initiative to one of three business levers: Revenue, Cost, Risk.
- Forrester’s Data Economy Lens – Classify dark data into “internal optimization” vs. “external monetization.”
Patterns for Unlocking Value
- Data Catalog + Lineage First – Without visibility, you can’t sell the story. Use Collibra, Alation, or OpenMetadata.
- Quick-Win Risk Reduction – Identify dark data in regulated domains (PII, HIPAA, PCI) → show risk avoidance value.
- Domain Data Products – Package subsets of dark data into consumable APIs or curated datasets for business units.
- ML Bootstrapping – Use logs, support tickets, or IoT data as training sets for predictive maintenance, churn, or fraud models.
- Data Mesh / Data Fabric – Position dark data as raw material feeding distributed ownership models.
Tools for Execution
- Discovery & Classification: BigID, Immuta, Microsoft Purview, Apache Atlas.
- Quality & Profiling: Great Expectations, Soda, dbt tests.
- Storage & Access: Data lakehouse (Databricks, Snowflake, Synapse).
- APIs & Sharing: PostgREST, GraphQL gateways, Azure Data Share.
- Governance & Security: OPA/Cerbos for policy, Vault for secrets, Ranger for access.
Selling to the Business: Timing & Approach
- Right Timing:
- Budget cycles → tie dark data ROI to upcoming fiscal planning.
- Compliance deadlines → frame as risk mitigation.
- Product launches → frame as insight acceleration.
- Approach:
- CFO: emphasize cost containment and risk avoidance.
- CMO/Product: emphasize faster insights and competitive differentiation.
- CEO/Board: emphasize new revenue streams and future-proofing.
- Patterned Pitches:
- “Compliance First”: Prevent fines by classifying dark data.
- “Efficiency Now”: Optimize cloud spend by reducing cold storage.
- “Monetize Later”: Build an internal data marketplace once foundations are set.
Broader Technologist Playbook
- Run a Maturity Assessment (CMMI for Data, Gartner D&A Maturity Model).
- Build a Data Product Roadmap – sequence risk → efficiency → revenue.
- Use Business Storytelling – frame each dark data story as a “hidden asset” being converted to a “profitable product.”
- Pilot with Guardrails – start with 90-day pilots that deliver measurable outcomes (storage savings, reduced time-to-insight, risk reports).
- Overcommunicate in Business Language – avoid “ETL, schema, Kafka” → use “faster answers, lower costs, new revenue.”
The Product Manager’s Playbook
So, how does a product manager unlock gray or dark data in a way that creates real value?
- Frame the Business Case – Start not with the data, but with the pain point. What decision is being made today in the dark? Is marketing relying on anecdote? Is operations missing early warning signals? Tie the initiative to cost reduction, risk mitigation, or revenue expansion.
- Find and Shape the Data – Audit what’s collected but not used: call transcripts, IoT logs, unstructured notes, survey free text. Work with data engineers to wrangle it into usable form. Importantly, don’t present stakeholders with raw feeds—transform them into signals or stories.
- Prototype Value Quickly – Just as you would prototype an app, prototype insights. Create a lightweight dashboard, an automated report, or a single predictive alert. This lowers the barrier to stakeholder buy-in.
- Sell the Idea with Better-than-Before – Executives don’t abandon old reports unless you give them something better: faster, more accurate, easier to use. When Salesforce rolled out Einstein, they didn’t just expose activity logs—they turned them into pipeline recommendations, directly improving sales productivity.
- Operationalize and Govern – Dark data projects risk fading into “innovation theater” unless they’re operationalized. Work with data governance to ensure security, compliance, and sustainability. Treat the dataset and insights like a product—roadmap, iterate, sunset what doesn’t stick.
Wrapping up…
Unlocking dark or gray data isn’t about building a bigger data lake. It’s about shifting the conversation from availability to utility. A product manager’s job is to transform shadows into signals, noise into narrative.
The organizations that get it right don’t just illuminate hidden data—they empower their leaders to make faster, sharper, and more confident decisions. Those that fail? They drown stakeholders in JSON and buzzwords, proving that darkness isn’t the absence of data, but the absence of clarity.
