• 4 min read

4 big challenges of analyzing unstructured invoice data

You can’t expect to win at poker if you could only see half of the cards in your hand. Unstructured Invoice Data presents exactly similar challenges for the business: Making decisions without all relevant information.

Every day, organizations and employees encounter invoice information in the form of emails, messages, PDFs, images, or a web article. Typically, these organizations have relied on paper invoices to process payments and maintain accounts. Analyzing data and reconciling invoices involves someone manually typing into software/ excel/ database/ ERP/ CRM/ledger which is one of the most common tasks among most companies. With such varying formats of invoices, most organizations face problems in accessing and processing the information.

Challenges Involved in Analysing Unstructured Invoice Data

Unstructured Invoice Data has immense business value, but most organizations have not been able to gain insights because there simply exist few challenges involved in analyzing these Unstructured Invoice Data.

  • The Invoice Data cannot be analysed with conventional systems

Unstructured Invoice Data cannot be analyzed with current databases because most data analytics databases are designed for structured data, and fails miserably with Unstructured Data. Therefore, data analytics experts need to find new methods to locate, extract, organize and store Invoice Data.

  • Unstructured Invoice Data keeps expanding

Unstructured Invoice Data continues to grow at an exponential rate and experts believe that it will make up over 93% of all data by 2022. Around 18 billion invoices are issued each year in the USA and Europe alone. This large volume is going to be a huge challenge in analyzing data because the larger the data set, the harder it is to store and analyze in a way that is timely and efficient.

  • Is the analysed Invoice data relevant?

Making sure Invoice Data is relevant is one of the biggest challenges when it comes to analyzing Unstructured Invoice Data. Your company might invest a lot in an analytics tool like Tableau or hire some data scientists to predict some data for making critical business decisions.

No analysis or analytics can be made till the time data is relevant and correct. Example There might be lots of data in invoices regarding spend analytics but only once all the data is entered into your system it will start making sense.

  • Not all Unstructured Invoice Data is high quality and accurate

Unstructured Invoice Data can be very uneven when it comes to quality. The lack of consistency in quality occurs because invoice data is difficult to verify and, therefore, is not always accurate. Furthermore, much of the Invoice Data may not be reliable because people have a tendency to exaggerate, distort or be dishonest about their information. If organizations feed this information into their analytics systems, then they will not get accurate findings, which will hurt the company’s fortunes down the line.

How AI works for Unstructured Invoice Data Extraction?

To conclude, although the task of unstructured data extraction from invoice is a tough challenge, the recent developments in AI allow us now not only to parse the text available in the document but also to capture the relationships across various fields and text and thus helps to automate generating end-to-end results which are much more generalized compared to creating manual heuristics traditionally.

In addition, AI methods evolve on a regular basis with the advancements in research, and with them outperforming traditional OCR methods already it becomes a no-brainer to invest in AI-based solutions for information extraction.

Are you ready to get the most from your Unstructured Data? 

Read the next blog to see how we can help!

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