What is an Infocube in SAP BW & How to Create One: The Complete Guide

Hi there! As an SAP data warehousing expert, I am often asked – what is an infocube and how do I create one from scratch?

In this comprehensive guide, we will unpack everything you need to know to design, build and leverage infocubes for business analytics.

Here is an overview of what we will cover:

• Infocube Concepts & Components
• Types & Use Cases
• Step-by-Step Creation
• Real-World Examples
• Design Best Practices
• Integration & Workflow

Let‘s get started!

Infocube Concepts & Components

An infocube can be thought of as a self-contained dataset or data warehouse within SAP BW. It enables multi-dimensional reporting and analytics of granular, historical data.

Key Functionalities:

• Stores aggregated, summarized data over long periods
• Acts as a data target and reporting object
• Analyze data across different attributes and perspectives

An infocube contains a fact table surrounded by dimension tables, as shown below:

• Fact Table: Stores quantitative metrics and KPIs like revenue, cost etc.
• Dimensions: Qualitative attributes like customer, product, region etc. Provide context for measures.
• Key Figures: Numeric metrics to analyze like sales quantity, profit margin.

In essence, infocubes provide the foundation for powerful BI within SAP BW.

You might be wondering – how is this different than standard relational databases or reporting tables?

Key Differentiators:

• Optimized for reporting performance
• Handles huge data volumes with ease
• Allows flexible analysis from various angles
• Integrates smoothly with upstream data sources

This makes infocubes ideal for enterprise-grade analytics, business intelligence and decision support.

Now that we have the building blocks, next let‘s explore some common infocube use cases and types.

Infocube Types & Use Cases

There are three main variants of infocubes within SAP BW:

Type	Description	Use Cases
Standard	Physically stores data in cube	Analyze large historical datasets
Virtual	Pulls data real-time from source systems	Avoid replicating data or aggregation
Real-time	Stores data, enables write operations	Planning and simulation

The needs of your reporting use case determines the optimal infocube type.

Let‘s explore some common examples for each:

Standard Infocubes

Standard infocubes physically persist data in a compressed, optimized way designed for lightning fast slicing and dicing.

For example, a Global Sales infocube containing 5 years of historical transaction data could provide insights like:

• Total revenue over the past 5 years
• Revenue by region over time
• Top performing products by quantity sold

This enables long term trend analysis, forecasts and data-driven decisions leveraging years of granular data.

Virtual Infocubes

In some cases, duplicating data into a separate reporting structure creates excessive ETL complexity. This is where virtual infocubes add value.

For instance, a human resources infocube could link dimension data real time from SAP HCM system of record tables. This allows access to employee headcount, turnover metrics, compensation analysis without manually loading this data.

Virtual infocubes eliminate aggregation as data is consumed real-time. This helps reduce latency and storage needs while preventing manual replication.

Real-Time Infocubes

Real-time infocubes contain write capabilities on top of physical storage for enabling simulation and planning scenarios.

A Budget Planning infocube could leverage real-time functionality. As finance updates budget targets for next year, impact on headcount, operating cost KPIs is instantly visible based on embedded analytics logic. This allows dynamic modeling of different budget scenarios and assumptions.

Now that we have covered the key concepts, variants and use cases let‘s shift gears into actually building an infocube from start to finish.

Step-by-Step: How to Create a Standard Infocube

The best way to understand infocube creation is follow along with an example.

We will build a standard "Sales Analysis" infocube from scratch containing historical transaction data.

Step 1: Initialize Infocube

Launch transaction RSA1 in BW. Right click the target InfoArea and select "Create InfoCube":

Let‘s define technical name ZSALES01 with description ‘Sales Infocube‘. Ensure type is Standard:

Click Create to initialize structure.

Step 2: Define Dimensions

Next, configure four dimension attributes:

• Customer
• Material
• Date
• Region

Using infoobject browser, drag and drop characteristics like customer ID, product ID, calendar year/month etc. I recommend keeping it to level 1 characteristics without hierarchies initially. We can enrich later as needed.

Step 3: Add Key Figures

Now let‘s bring in some key metrics into our fact table like Revenue, Sales Quantity and Gross Margin %. Simply search for these kefigures and add to infocube.

This structures our infocube with dimension context around business metrics to analyze.

Step 4: Activate & Load Data

Last step is to activate the infocube to finalize structure. Once activated, inception load jobs can populate historic data from source systems. Within hours raw transaction records get aggregated into our optimized sales analysis infocube!

And there we have it – our own custom infocube ready for slicing, dicing and analytics!

While simple in concept, effectively designing infocubes takes practice and business understanding. Let‘s move on to some real-world examples and best practices.

Infocube Design Best Practices

Well-designed infocubes require forethought to enable effective analysis vs. just storing data.

Let‘s explore some common design pitfalls and recommendations:

Manage Granularity

Storing overly granular data in infocubes strains performance and throughput. Best practice is to aggregate to higher hierarchy levels along larger dimensions like customer, product.

However, minimize aggregation on analysis attributes like date, location to allow drilling by month or city. Strike balance between grain and flexibility.

For example:

Customer, Product - Aggregate to Level 2/3 
Date, Region - Limit aggregation

Optimize Cardinality

Cardinality defines relationship between dimensions. High degree infocubes perform slower with more complex joins.

Watch out for M:N relations within same dimension like customer-to-product. Optimal approach splits these into separate dimensions to improve response times.

For example:

Customer → many products 
Product → many customers

Accommodate Change

It is shortsighted to design an infocube just to answer today‘s questions vs. business dynamics across years.

Leave room for expansion during early design in case new KPIs, attributes added over system lifetime without requiring rework.

While meeting initial requirements, ensure adequate flexibility for potential enhancements down the road through a thoughtful design paradigm upfront.

Comparing Infocubes to Other Options

SAP BW offers flexible options for modeling and reporting analytical data beyond infocubes. How do we know when infocubes are the right tool for the job?

Let‘s compare some common scenarios of infocubes vs. alternatives:

Infocubes vs. Reporting Tables

Reporting tables act as standalone datasets unrelated to infocubes. These directly access transaction data.

When to use infocubes instead:

• Need high performance aggregates
• Require historical trend analysis
• Desire business-friendly star schema
• Want lower TCO for large datasets

Infocubes vs. Data Marts

Data marts provide focused subset of data for specific use case vs. enterprise-wide.

Key difference is infocubes are optimized from ground up to enable fast slicing and dicing from various angles. Data marts are more narrow, predefined analysis using standard tables.

Therefore, leverage infocubes for broad analytics. Use data marts for targeted departmental reporting.

Infocubes vs. SAP HANA

Both serve as platforms for analytics. However, SAP HANA relies on its column-oriented database while infocubes are multidimensional structures with aggregation built-in.

HANA reduces some aggregation needs but reporting queries require joining various tables unlike infocubes with dimensions pre-integrated.

In summary, modern data warehousing provides flexible options. Align tool to specific analytics use case through an informed evaluation.

Real-World Infocube Examples

Seeing real-world infocubes illustrates these concepts in action:

Sales Analysis Infocube

Enables regional managers to analyze 5 years trends across customers, products, geographies to guide marketing campaigns, pricing changes and new market expansion.

Workforce Planning Infocube

HR analytics team leverages headcount data integrated from SAP HCM system blended with budget, cost and salary metrics. Supports what-if modeling for restructures and scenario planning.

Customer Churn Infocube

Marketing analytics infocube blends customer data from CRM system with billing events and segmentation attributes. Identifies upsell opportunities based on likelihood to churn algorithms.

There is no limit to building innovative infocubes unlocking unique business insights!

Integration Overview & End-to-End Workflow

Now that we have a handle on infocube concepts and creation, let’s discuss integration.

Typically, infocubes are part of broader data warehousing workflow spanning capabilities:

• Data Sources: SAP and non-SAP systems containing transactions
• Extraction: Periodic data pulls via batch or real-time
• Transformation: Mapping, validation, cleansing
• Loading: Populate infocubes with scrubbed, aggregated datasets
• Reporting: Analyze infocube data with BEx, Analysis Office etc.

With this sequence, raw source data gets structured, enriched and loaded into infocubes optimized for flexibility, performance and insights!

Key Takeaways

We covered a ton of ground explaining infocube concepts, uses cases, design considerations and overall value.

Let‘s recap the key points:

• Infocubes enable multidimensional reporting in SAP BW via centralized fact + dimension data
• Top use cases are historical analytics, simulations, budgeting
• Steps for creating standard infocubes storing aggregated data
• Compare to reporting tables, HANA views, datamarts for right tool
• Design considerations around granularity, cardinality, flexibility
• End-to-end data flow from source systems into infocubes

As you can see, infocubes are incredibly versatile and a foundational building block within enterprise data warehouse environments.

I hope this guide served as comprehensive overview for you to take the next steps on your analytics journey leveraging the power of SAP BW infocubes!

Let me know if any other questions come up. Happy to help!