The Building Blocks of Data Analysis
Slices and metrics are the fundamental components of every analysis in Zenlytic. Understanding these concepts unlocks your ability to explore data and build meaningful insights.
What You'll Learn
Differentiate between slices (dimensions) and metrics (measures)
Navigate the Explore interface
Build analyses from simple to complex
Understand how data aggregation works
See the SQL behind your selections
Key Concepts
Slices (Dimensions)
What they are: Attributes or characteristics of your data that you use to segment and group information.
Think of slices as: The "by" in your questions - "Show me sales by region" or "Count customers by state"
Examples:
Customer attributes: State, City, Customer ID
Product attributes: Category, Brand, SKU
Time attributes: Month, Quarter, Year
Transaction attributes: Payment method, Channel
Metrics (Measures)
What they are: Numerical values that you calculate, count, or aggregate across your data.
Think of metrics as: The "what" you're measuring - "Show me total revenue" or "Count number of customers"
Examples:
Counts: Number of customers, Order count
Sums: Total revenue, Gross profit
Averages: Average order value, Mean discount
Calculations: Conversion rate, Profit margin
Using the Explore Interface
Getting Started
Click the Explore tab in the left panel
Choose a topic (data subject area)
View available slices and metrics organized by table
Interface Layout
Left Panel: Shows all available slices and metrics
Organized by data tables (Customers, Orders, Products)
Slices appear with dimension icon
Metrics appear with calculator icon
Main Canvas: Where your analysis builds
Selected slices and metrics appear here
Results display below your selections
SQL preview available for technical users
Building Your First Analysis
Single Metric Analysis
Starting simple with just one metric:
Select "Count of Customers" metric
Click Run
Result: One row showing total customer count
What's happening: With no slices selected, you get a single aggregated value for your entire dataset.
SQL Generated:
sql
SELECT COUNT(DISTINCT customer_id) FROM customers
Adding Your First Slice
Segmenting data with a dimension:
Keep "Count of Customers" metric selected
Add "State" slice
Click Run
Result: One row per state showing customer count
What's happening: The metric is now calculated separately for each unique value in your slice.
SQL Generated:
sql
SELECT state, COUNT(DISTINCT customer_id) FROM customers GROUP BY state
Multi-Dimensional Analysis
Building complexity with multiple slices:
Keep previous selections
Add "City" as a second slice
Click Run
Result: One row per state-city combination
What's happening: Metrics are calculated for every unique combination of your selected slices.
Understanding row multiplication:
1 slice with 50 values = 50 rows
2 slices (50 states × 100 cities) = potentially 5,000 rows
Only combinations with data appear
Practical Examples
Sales Analysis
Question: "What's our revenue by product category this month?"
Slice: Product Category
Metric: Total Revenue
Filter: This Month
Customer Segmentation
Question: "How many customers do we have in each city by customer type?"
Slices: City, Customer Type
Metric: Count of Customers
Performance Tracking
Question: "What's the average order value by sales channel over time?"
Slices: Sales Channel, Order Month
Metric: Average Order Value
Common Metric Types
Count Metrics
Count of distinct items (customers, products)
Count of transactions
Count of events
Sum Metrics
Total revenue
Total costs
Total quantity
Average Metrics
Average order value
Average discount percentage
Average delivery time
Calculated Metrics
Profit margins (revenue - cost)
Growth rates (period over period)
Conversion rates (successes/attempts)
Best Practices
Choosing Slices
Start broad, then narrow:
Begin with one high-level slice (Region)
Add detail as needed (State, City)
Avoid too many slices (creates too many rows)
Selecting Metrics
Match metrics to questions:
"How many?" → Count metrics
"How much?" → Sum metrics
"What's typical?" → Average metrics
"What's the rate?" → Calculated metrics
Performance Tips
Limit slices to 2-3 for readable results
Use filters to focus on relevant data
Start with shorter time ranges
Add complexity gradually
Understanding Results
Reading Your Output
Each row represents:
A unique combination of your selected slice values
The calculated metric for that combination
Only combinations with data appear
Sorting and organizing:
Click column headers to sort
Highest/lowest values for quick insights
Export for further analysis
Advanced Concepts
Hierarchical Slices
Some slices have natural hierarchies:
Year > Quarter > Month > Day
Country > State > City
Category > Subcategory > Product
Use hierarchies to drill down progressively.
Metric Relationships
Understanding how metrics relate:
Revenue = Price × Quantity
Profit = Revenue - Costs
Margin = Profit / Revenue
Build analyses that show these relationships.
Troubleshooting
Too many rows returned?
Reduce the number of slices
Add filters to limit data
Choose less granular slices
Unexpected results?
Check if slices create valid combinations
Verify metric definitions
Review any applied filters
Performance issues?
Simplify your analysis first
Use time filters to limit data
Build complexity gradually
You're Ready!
Now you understand the foundation of data analysis in Zenlytic. Slices tell you "by what" to segment, metrics tell you "what to measure."
Your first challenge:
Go to the Explore interface
Choose a single metric you care about
Add one slice to segment it
Run the analysis
Add a second slice and see how the results change