Businesses have become increasingly data-driven in recent years. Having factual information to back-up important business decisions can be a very powerful tool. In fact, a 2020 Forrester report found that 58% of organizations who adopt data intelligence are more likely to exceed revenue goals than non-data intelligent organizations. A vital component of engaging in data intelligence is storing your data properly. Data warehousing can be used at any point in the data intelligence process, whether you’re just getting started and want the raw data to be rescanned for analytics purposes, or you’re finished and simply need everything to be housed permanently for record-keeping or auditing. SAP Business Warehousing (BW) is a data warehousing tool I continually use with our clients because its capabilities go far beyond just being a place to store your data. Below are my top five benefits to using BW.
Broad functionality
SAP BW is shipped with BI Content, which is a set of tools including ready-made extraction routines, meta-data, InfoCubes, information models, reports, and channels that guarantee analysis and reporting capabilities right out of the box. A client of ours is using BI Content to measure their system performance, report optimization, and monitor security.
Built-in flexibility
While the beauty of BW is that it’s a “ready-to-go” solution, it’s still easy to adapt. You can modify or add data sources, meta-data, InfoCubes, and reports as and when you need to. For example, you’re able to write your own ABAP codes in multiple places (transformations, InfoPackages, DTPs, etc.). For one of our clients we implemented a BW InfoCube and wrote ABAP routines to customize and add extract fields to the Cube.
Complex analysis capabilities
You have the ability to analyze multidimensional data sources in BW by using the MultiProvider tool. We implemented a MultiProvider for a manufacturing client that shows which part numbers will be sold more than others, and what stock they need for the next three years.
Permanent storage
With BW, you get permanent data storage as opposed to transactional data that could be purged and disappear, meaning that without it, costs can be much higher to access historical data. Users are able to drill through the data across various time periods and do projections for the future accordingly, which generally isn’t easy to do in a transactional database.
Bad data identification
Bad data can derail a project. In my experience, I’ve seen invoice records when the data is way in the future due to human error. If BW couldn’t detect this error, a million-dollar sale would just fall off the radar for this year’s numbers. By utilizing tools within BW, you can determine where the bad data is coming from, allowing you to attack the root cause and correct it.
No matter how large of a company you are or where you’re at in the data intelligence process, by utilizing BW, you’ll be able to learn from past data so you can plan accordingly for the future. You’ll have the ability to go beyond the IT function and empower your marketing, sales, and operations teams to make more strategic decisions for your organization.
About the Author:
Sam Gassem is a Senior Principal Consultant with over 25 years of experience in SAP BW/BI, SAP HANA, SAP Business Intelligent Business Objects, and SAP Data Services. He also holds several Global SAP Certifications. He has designed and implemented SAP BW and SAP Business Intelligent Business Objects in a number of markets including the Manufacturing and Pharmaceutical industries. He likes to camp, work in the garden, and ride his bicycle.

Analysis Paralysis in AI Adoption
Learn why endless discussions and the relentless pursuit of flawless data are actually costing you valuable time, insights, and competitive advantage – just like it did for giants like Kodak and Blockbuster.

Don’t Take Product Out of the Equation: How to Nail Your AI Implementation
AI isn't just about the technology, it's about solving real problems and delivering real value. One way to do that is to keep product at the forefront during your AI implementation. Learn more about why having a product-first mindset is so important in this article by Principal Product Strategist Heather Harris.

Navigating AI in Banking and Financial Services: A Risk-Based Rebellion for Leaders
Every shiny AI use case in regulated industries has a shadow: governance, compliance, model risk, ethics, bias, explainability, cyberattack vectors and more. It's not that organizations and leaders don’t want AI, it’s that they’re paralyzed by the political, regulatory, and operational realities of deploying it. Sparq's Chief Technology Officer Derek Perry and VP, BFSI Industry Leader Rob Murray argue we need to change that. Check out this article to learn how to actually ship production AI use cases in regulated environments.

Five Important Questions to Ask Before Starting Your AI Implementation
Creating a lasting impact with AI requires more than just technical output. In this article by Principal Product Strategist Heather Harris, learn five questions to ask before starting an AI implementation so it can deliver long-term business value.