Step 1: Identify Where Dark Data Hides
Dark data is everywhere: CRM interaction logs that nobody queries, web analytics events beyond page views, social signals that never leave the social team, support ticket text that is categorized but never analyzed for belief patterns, IoT and sensor data that is stored but never correlated with business outcomes. The first step is a data landscape assessment: map every data source, its volume, its refresh frequency, and its current utilization rate. In most organizations, this assessment reveals that 60-90% of collected data is dark - stored but never used for strategic decisions.
Step 2: Prioritize by Business Impact
Not all dark data is equally valuable. Prioritize by business impact: which unused signals would most improve your highest-value decisions? For retail, that might be real-time demand signals that improve inventory allocation. For FMCG, it might be regional behavioral patterns that inform trade spend. For media, it might be content engagement depth that improves programming decisions. Map dark data sources to business decisions and prioritize the connections with the highest potential impact.
Step 3: Connect Without Disruption
Sentient OS connects to dark data sources through standard interfaces - REST APIs, SQL, Kafka, S3. No rip-and-replace. No schema migration. No disruption to existing workflows. The Sensor ingests signals from your existing data infrastructure, normalizes them, and feeds them into the 5-Layer Architecture. Your data warehouse, CDP, and BI tools continue to function. Sentient adds a decision layer on top.
Step 4: Run a Focused Pilot
Start with a single use case. Choose the dark data source with the highest potential impact and the lowest integration complexity. Run a pilot: ingest the signals, let the 5-Layer Architecture process them, and validate the Command Center outputs against known outcomes. A focused pilot - one data source, one use case, one set of Command Center modules - can be live in weeks.
Step 5: Validate and Expand
Validate pilot results against your success metrics. Did activating dark data improve the target decision? By how much? What was the ROI? Use these results to justify expansion to additional data sources and use cases. Each new connection adds signals to the vector space, improving archetype definitions and causal models across the entire stack. The system compounds: more data makes better decisions, which justify more data integration.
Step 6: Make It Continuous
Dark data activation is not a one-time project. New data sources emerge. Existing sources evolve. Business priorities shift. Build the connection between data sources and the decision layer as a continuous pipeline, not a project with an end date. Nami orchestrates the ongoing flow. The Sensor captures new signals as they appear. The system stays current because the architecture is designed for continuous ingestion, not batch processing.