Understanding Platform Analytics: A Recipe for Better Dashboards

[Donta]

When people first learn Platform Analytics, they often encounter a long list of new terms:

• Indicators
• Indicator Sources
• Breakdowns
• Breakdown Sources
• Data Collections
• Dashboards

The challenge is that these concepts are typically introduced individually, making it difficult to understand how they work together. One way I like to explain Platform Analytics is by comparing it to baking a cake. Just as a recipe combines ingredients to create a finished product, Platform Analytics combines data, calculations, and visualizations to create meaningful dashboards. Let's walk through a Hardware Asset Management example.

Start With the Desired Outcome

Imagine an Asset Manager asks:

Which locations have the highest number of devices eligible for refresh?

This is the business question we want to answer and notice that nobody asked for:

• An indicator
• A dashboard
• A breakdown

The Finished Cake: The Dashboard

When someone sees a dashboard, they're seeing the finished product. Just like a cake on display in a bakery, they don't see:

• The ingredients
• The recipe
• The measuring cups
• The baking process

They only see the final result and in Platform Analytics, the dashboard is simply the presentation layer.

Everything else exists to support it.

The Ingredients: Your Data

Before you can bake a cake, you need ingredients and before you can build analytics, you need data.

For our refresh example, some potential ingredients include:

• Asset records
• Asset model
• Location
• Purchase date
• Lifecycle stage
• Refresh eligibility flag

Without data, nothing else matters. Just as a baker cannot make a cake without ingredients, Platform Analytics cannot produce meaningful insights without quality data.

The Recipe: Indicator Source

This is where many people begin to understand Platform Analytics. A recipe tells you:

• Which ingredients to use
• Which ingredients to ignore
• How to combine them
• How to measure them

An Indicator Source does exactly the same thing:

Table: Hardware Asset

Condition: Refresh Eligible = True

Aggregation: Count

In plain English, we're telling Platform Analytics: "Look at hardware assets, find the ones that are refresh eligible, and count them."

The Indicator Source is the recipe and without it, Platform Analytics doesn't know what to calculate.

The Measuring Cup: Aggregation

Recipes don't just list ingredients, they tell you how much of each ingredient to use. Platform Analytics does something similar through aggregation.

Common aggregations include:

• Count
• Sum
• Average
• Maximum
• Minimum

For our example:

Count Refresh Eligible Assets

We're simply counting records  and for a different KPI, we might calculate:

Sum of Asset Value

or

Average Device Age

Aggregation determines how Platform Analytics measures the data.

The KPI: Indicator

If the Indicator Source is the recipe, the Indicator is the finished KPI.

For example: Indicator Name: Refresh Eligible Assets

• The Indicator is the metric stakeholders ultimately care about and  you can think of it as the item listed on the bakery menu.

Customers don't ask: "Can you show me the recipe?"

• They ask: "How many refresh-eligible assets do we have?"
• That's the Indicator's purpose.

The Flavors: Breakdowns

Let's say we bake one vanilla cake and at the end of the party, someone asks: "Who ate the cake?" We could break the cake consumption down by age group:

• Children (2-12)  ate 25% of the cake
• Teenagers (13-19) ate 15% of the cake
• Adults (20-50) ate 40% of the cake 
• Seniors ate (60+) 20% of the cake 

Without a breakdown:

Refresh Eligible Assets = 1,200

That's useful but limited, but with a breakdown by Location:

• Austin = 300
• Dallas = 450
• Chicago = 250
• London = 200

Where Do the Flavors Come From? Breakdown Sources

If the breakdown is "Location," where does Platform Analytics get those locations? That's the role of the Breakdown Source.

Examples:

• Location
• Department
• Manufacturer
• Model Category
• Stockroom

The Breakdown Source supplies the values used to categorize the KPI. Just as age groups help us understand how the vanilla cake was consumed across different groups of people, Breakdown Sources help us understand how a KPI is distributed across different categories. The cake itself doesn't change, but the perspective does. Similarly, different Breakdown Sources provide different ways to analyze the same metric.

The Oven: Data Collection

Let's continue the baking analogy. Even with ingredients and a recipe, the cake isn't ready until it's baked. In Platform Analytics, Data Collection is that baking process. The platform periodically executes the Indicator Source and stores the results over time.

This is what enables:

• Historical trending
• Time-series analysis
• Performance tracking

Without Data Collection, you'd only know the value right now, but with Data Collection, you can answer:

• Is refresh eligibility increasing?
• Is compliance improving?
• Are fulfillment rates declining?

Conclusion

One of the biggest breakthroughs for new Platform Analytics users happens when they stop viewing Indicators, Indicator Sources, Breakdowns, and Dashboards as separate features. They are all parts of the same process.

Remember the recipe analogy:

• Data = Ingredients
• Indicator Source = Recipe
• Aggregation = Measuring Cup
• Indicator = KPI
• Breakdown = Flavor
• Data Collection = Oven
• Dashboard = Cake

Once you understand how those components work together, Platform Analytics becomes much easier to design, troubleshoot, and explain. And just like baking, the quality of the final product depends on understanding the recipe behind it.