Article Summary: A paraphrased excerpt from The Dashboard Effect highlighting the benefits of implementing a data warehouse to support Power BI dashboard reporting. These essentials and best practices apply to data lakehouses as well, which is our current approach that allows companies to prepare their data for the additional benefit of AI.
Despite best efforts and investments, few companies understand how to sustainably and strategically manage their data and use it to increase growth and margins.
As a PE-backed company plans to reap more insights from their data, understanding that the quality of insights will be limited to how well your data warehouse mirrors your business structure and logic is the right place to start. To be report-ready, data needs to be gathered, organized, and cleaned in response to the tactics you want to implement, the problems you want to fix, and ultimately, the metrics that drive those efforts. In other words, a data warehouse starts with your business strategy. Approaching your data warehouse from a technical perspective will find you chasing your tail, indefinitely. And a technical approach leaves most companies stymied by the complexity of their data ecosystem.
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Data comes from countless systems - accounting, sales, operations, marketing, HR, etc. Even small start-ups can have ten or more data sources.
Though designed to help improve efficiency, software systems can also sabotage progress by creating silos of information.
When information is siloed across an organization, manually generating a cohesive view of performance can prove nearly impossible. Leaving data disconnected hinders your ability to compete in a market where companies are using data as a tool to drive their value creation plans by elevating measurement and accountability.
For data to be useful, it must be gathered, distilled, and connected in a way that reflects your business model.
This is the role of the data warehouse.
While a data warehouse is the best tool for gathering, distilling, and connecting data, it is not the most commonly used. Excel is. While virtually indispensable, Excel files are rarely governed and can therefore easily proliferate, having the ironic effect of exacerbating a company’s data entropy.
How many of these Excel headaches ring true at your company?
Trying to tame your business data through a melee of Excel files is decidedly frustrating. We’ve seen it over and over. Excel-dependent organizations are perpetually hampered by missing, faulty, and/or outdated data. Data warehouses overcome these issues.
While importing your Excel data into a single dashboarding tool (such as Tableau or Power BI) may cut back the Excel jungle to a degree, it won’t create the foundation needed for a scalable, data-driven business. Mashing-up your data in this way is a poor solution because BI tools aren’t specifically designed to organize and process data.
A data warehouse is the single, structured repository where all your data is organized so it can be put to good use. It’s the sole source of truth for your company and is foundational to The Dashboard Effect (using data intelligence to automate a culture of growth, profitability, and accountability).
Building a data warehouse can seem ominous. If you don’t have a data architect on staff, you may worry that data warehouses are the exclusive domain of other, uber-technical companies. Ten years ago, you would have been right, but today virtually any business can join the data revolution. Data warehouses are many times easier and more affordable to deploy than just a few years ago. Our current approach enables us to set up a data lakehouse in as little as two weeks.
There’s a cost and complexity threshold where migrating to a data warehouse makes obvious sense. Up until that point, however, a data warehouse may seem like an unnecessary expense.
Until you consider technical debt.
Technical debt is the recurring expense that results from taking shortcuts now. Without a data warehouse, you’re left mashing up your data from several sources into a single file. In this mash-up scenario, the painful cost comes when you want to add or change data sources or change reporting platforms. Even updating a source system to its latest version can break your reporting (which can be costly to fix).
If your business uses a BI tool to mash up your data, then your data architecture will look a lot like this:
Figure 1. High-level view of typical data mash-up.
In academic terms, this means your data model inevitably ends up encapsulating your report logic, and that in turn is encapsulated by the reporting platform, tightly binding the report logic to the report presentation layer.
In nonacademic terms, this means that if you ever want to change one part of the system, you end up having to change all the parts (remember those old TV-VCR mash-up units? Same issue). Also, if you change a field or column in your CRM or accounting system, your mash-up tool will simply return a system error. Moreover, if you ever want to migrate to a new reporting platform, chances are you’ll be able to reuse little of your work.
But if you use a data warehouse, your architecture will no longer look like the above classic bad example and will instead have a much better structure:
With a data warehouse, no single tier of your architecture is tightly bound to another. You can change your ETL, save money by switching your business software, or switch your reporting platform to the latest and greatest. Whatever the change, you only need to worry about that one component rather than the entire stack. And if you add or delete a column from your CRM or accounting tool, your reports will still work rather than returning a general error (though they will need updating if you want to include the changed data).
Here’s another important benefit of data warehouses that often gets overlooked: What happens when you want to use multiple reporting tools? With mash-up tools, you’ll have to recreate the data model in each, assuming those reporting tools even have ETL and data-modeling functionality. With a data warehouse, you can simply connect those reporting tools to the system.
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Blue Margin can help you transform your data into a gold mine of actionable insights.
We do this by building a data lakehouse, or in some instances a data warehouse, and providing ongoing data management through our Managed Data Service. Leveraging an agile, sprint-based approach, we can take your business from raw data into data models to support a fully functioning, validated semantic layer in just a few short weeks. Check out the benefits of our data modeling services here.