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The Learning Loop

SkoutLab isn’t a static tool — it’s a system that continuously learns and improves:

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What Gets Learned

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Data Understanding

As Knowledge Builder runs, it learns:
CategoryExamples
StructureTables, columns, relationships, keys
SemanticsWhat fields mean in business terms
QualityMissing values, inconsistencies, errors
PatternsTypical values, distributions, seasonality

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Business Context

Over time, SkoutLab understands:
  • Which metrics matter most to you
  • How different data sources relate
  • What’s “normal” for your business
  • Seasonal and cyclical patterns

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Analytical Patterns

With each analysis, SkoutLab learns:
  • Which hypotheses tend to be fruitful
  • What types of findings you act on
  • Common patterns in your industry

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How Learning Improves Results

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Faster Analysis

First analysis of a new dataset might take time to understand structure. Later analyses are faster because:
  • Schema is already known
  • Relationships mapped
  • Quality issues documented

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Better Accuracy

Understanding context prevents errors:
  • Knows that “revenue” in Table A = “sales” in Table B
  • Remembers that negative values mean refunds
  • Aware of timezone issues in timestamps

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Richer Insights

Historical knowledge enables deeper insights:
  • Compare current patterns to historical baselines
  • Detect anomalies based on learned norms
  • Surface trends that span multiple analysis sessions

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The Knowledge Graph

Behind the scenes, SkoutLab builds a knowledge graph of your business:
Customers, Products, Orders, Employees — the “nouns” in your data
How entities connect: Customers place Orders, Orders contain Products
Key measures and how they’re calculated
Known behaviors, seasonality, trends
Past findings, validated hypotheses, actions taken

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Continuous vs On-Demand

ModeWhen to Use
ContinuousFresh data arriving regularly, need ongoing monitoring
On-DemandPeriodic analysis, cost-conscious, stable datasets
For most users, running Knowledge Builder periodically (e.g., daily or weekly) provides good results while managing costs.

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Privacy & Control

Your learning data is:
  • Isolated to your account
  • Never shared with other users
  • Deletable at any time
  • Not used to train general models