If you’re aiming to boost decision-making and enhance your business processes by implementing a Business Intelligence (BI) solution, having a Business Intelligence Data Warehouse is crucial. Just linking a source database and a BI solution isn’t sufficient.
You must consider where to aggregate, precalculate data, and store those calculations. Technical limitations prevent you from housing this information in the source database. Therefore, you’re left with one option: storing calculation data in RAM, which results in data loss and performance problems. That is precisely why the concept of a data warehouse (DWH) was introduced.
During the various stages of utilizing a BI tool, when might the loss of business data occur?
Flaws in updating BI data from a database source
Imagine hitting the “refresh” button in POWER BI to check your dashboard’s real-time data from your accounting software or CRM. However, here’s a scenario you might encounter.
Risk of data loss during a temporary disconnection in the loading process
Live data loading takes time, approximately 1 minute. Since data exchange commonly occurs over a network, whether local or Internet-based, any glitch during this process could result in incomplete data loading in POWER BI.
That could lead to missing sales in reports, causing inaccurate analytical conclusions. Without going through a DWH before reaching POWER BI, errors may go unnoticed.
A DWH ensures consistency through automatic checks, like checksum verification, and loads missing data in case of flaws.
Restricted access to the data source because of request limitations
Suppose you launch a Google Ads marketing campaign for multiple product groups. You aim to assess the efficiency of your advertising materials for each group, not just track clicked ads. You also want to identify which clicks convert to revenue, guiding budget allocation for maximum ROI.
To address this, you must upload QuickBooks sales data filtered by dates and commodity groups and integrate it with Google Ads data on clicks for each ad.
To access Google Ads data, you rely on its API, which comes with set limits and quotas. The more reports your employees need and the higher the daily report compilation, the faster you deplete your quota. Attempting to fetch another report after hitting the limit results in no data retrieval, making the source inaccessible.
Loading Google Ads data into a DWH before transferring it to POWER BI eliminates the need for multiple requests and prevents exceeding your limits. You can simply use the data requested once and stored in the DWH, saving resources and ensuring continued access.
Inability to access the data source and having no connection to a remote database
Imagine needing a report urgently, but you can’t connect to a remote database, like QuickBooks Online or Salesforce CRM, due to ongoing maintenance. Consequently, at 9 AM on Monday, you can’t access Friday’s reports, let alone the current ones.
If your CRM or accounting software data passed through the DWH before reaching POWER BI, you could still generate reports based on the latest upload. Typically, the upload occurs at least once per day, following a predetermined schedule in the DWH.
If the DWH can’t reach the source, it employs a scheduler that automates processes to run at set intervals. The DWH will keep attempting until it successfully retrieves the data. You can schedule synchronizations to occur as often as every 5 minutes.
Inability to access historical data directly from the source
When obtaining data from third-party resources like SaaS platforms, the owners might intentionally delete your historical data and old transactions they consider irrelevant.
Alternatively, they might encourage you to upgrade to a more costly subscription plan. That is often done to avoid expanding their database servers, incurring additional expenses.
Sending data from third-party resources to the DWH before forwarding it to POWER BI ensures your independence from the storage policies of these third-party providers.
Flaws in Entering Data into the Source Database
For instance, your team member might mistakenly input a customer into the CRM or accounting software as ‘Robin Newlan’ instead of the existing ‘Robin Nowlan,’ with just one letter difference. QuickBooks or Salesforce would treat them as distinct clients. Consequently, you’ll have ‘Robin Newlan’ as a new customer, while ‘Robin Nowlan’ could be considered a lost client due to the absence of new transactions.
If you’re using regression to forecast individual customer behavior using historical data, having incorrect data for both client profiles could lead to inaccurate sales estimates. If your accounting software or CRM data went through a DWH before reaching POWER BI, your data warehouse expert would catch the spelling error in QuickBooks and might identify it as a potential duplicate. They could then verify and correct this issue either in the QuickBooks database or within the DWH.
Merely connecting a source database and a business intelligence tool falls short, highlighting the need to consider where to aggregate, precalculate data, and store calculations. Technical constraints prevent housing this information in the source database, pushing the only viable option of storing calculation data in RAM, leading to data loss and performance issues. This underscores the importance of the data warehouse (DWH) concept.