From PDF to Power BI

There is a bitter paradox in the world of Business Intelligence. We have tools with tremendous computing power - Power BI, Tableau, SQL databases in the cloud - and yet 80% of our time is still consumed by hand-to-matter. Why? Because the most valuable financial data is trapped in a format that is dead to the analyst - PDF files.

Every financial controller knows this pain. The CFO asks about changes in steel unit prices, and the answer, while lying within the company, is technically "invisible." The data rests in thousands of PDF invoices archived as "attachments" rather than structured records. Instead of analyzing, you become"data transcriber".

Why is PDF the "digital concrete" for your Business Intelligence?

Technically, the PDF is a presentation layer, not a data layer. For a BI engine, it is a collection of vectors, not a table with relationships. For the analyst, this means working withunstructured data (Unstructured Data).

Power BI loves structure: columns, rows, data types. Trying to feed a model directly from PDFs using standard connectors often fails - all it takes is for a vendor to move a table by 2 millimeters, and your ETL script in Power Query "lays out."
To turn this data into Actionable Insights, we need to change their focus state - from a static image to a dynamic database.

The missing link in the ETL process: Where to plug in the parser?

In the classical processETL (Extract, Transform, Load) the problem arises at the "Extract" stage, when the source is PDFs. This is where the Dokum as a critical element of the architecture.

The architecture of the solution is as follows:

1. Source(s): PDF invoices on email/FTP.
2. Intelligent Extraction: Dokum uses AI to identify key-value pairs and, most importantly tabular data (Line Items).
3. Transformation: The parser converts "digital concrete" into JSON, XML or CSV.
4. Integration: Power BI connects to the parser result directly through the API.

As a result, you get clean, normalized tables, ready for relational linking to your data model.

From chaos to charts. 3 analyses you won't do without automation

When you "liberate" data from PDFs, a world opens up to youPurchasing Analytics, which was previously unavailable.

1. Unit Price Variance analysis

Most ERP systems only record the total amount. By extracting items from invoices, you can track the unit price of a specific SKU.

• Insight: You can detect "creeping inflation," where a supplier raises the price by 1-2% per month. This is a powerful argument for renegotiation.

2. Track on-time payments and lost discounts

Many suppliers offer a discount for prompt payment, but this information is hidden in the PDF footers.

• Insight: Lost Discounts Opportunity Report. You can show the CFO how much money the company loses annually through tardiness in processing invoices.

3. Purchasing geography and supply chain risk

By pulling addresses from invoice headers, you can visualize expenses on a map.

• Insight: Concentration risk assessment. If 80% of key components come from a politically vulnerable region, this is a warning signal.

How do you convince a CFO that "fudging the data" is a waste of analytical talent?

The argument for the Board is simple:Opportunity Cost. If an analyst spends four hours a day transcribing data into Excel, the company is overpaying. The real cost, however, is what that analyst does not - Does not analyze margins, does not look for anomalies.

The investment in automatic parsing is a shift of resources fromData Entry (cost) toData Analysis (value-added). This is a change in the role of controlling from "historians" to "navigators."

Dokum is a data drill

It is often said that "data is the new oil." In the case of financial documents, that oil is trapped deep in shale rock (PDF). Dokum is the drill that breaks through the layer of digital concrete and lets the data flow right into your charts.

Stop cleaning the data. Start analyzing them eventually.