Source: Gartner, Infographic: Understand Intelligent Document Processing, Shubhangi Vashisth et al., 22 September 2021
This is the fifth and final post in the series where we explore Integration. Check out our earlier posts in this series, Capture and Preprocessing, Document Classification, Data Extraction, and Validation and Feedback Loop.
Meaningful data offers the best benefits when they are integrated with your business or enterprise systems, be it your on-premise or cloud system, or any incredibly complex system, such as an ERP. Today, businesses are focused on formulating comprehensive solutions for constantly-evolving customer problems or needs, and it is important to have an integrated system to ensure greater efficiency and business effectiveness
When it comes to Business Intelligence (BI) & Analytics, unstructured data has been kept outside of data mining for the longest time. If you run a retail clothing store, when you sell a dress, you record its sale, you capture details like selling price, payment method, discount, tax, etc but you do not record how the dress looked. Did it have half sleeves or full, what kind of neck design it had. All of this information is potentially in the photo of the dress. This limits you from understanding your customer behavior. Questions like what percentage of people who buy faded blue jeans pair it with belts featuring over-sized buckles.
In the absence of a system that can make sense of unstructured data, it was always kept outside the realm of BI and Analytics. Structured data, like your sales record, also happens to be a small fraction of the overall data that you have access to. The majority of data that any organization deals with is unstructured data such as emails, documents, receipts, and photos. Now that IDP platforms can convert this unstructured data into structured data, it opens up exciting new avenues of understanding your customers and their behavior better through data mining.
Here are a few examples:
From a receipt of other stores that you do not own, you can now figure out if people who buy a beer also buy wine. If you find they do, you could run a promotion selling them together.
From payslips in mortgage application documents, you can figure out that most people who work for sales in the manufacturing industry usually get only X% of their sales commissions.
From supporting insurance claim documents, you can automatically figure out what percent of a car repair cost is from body shop work vs replacement parts for a Toyota Prius serviced in Chicago.
You can take this analysis one step further by opening up your extracted data to search using Natural Language Query (NLQ) technologies. So, instead of setting up reports in advance, you can fire a query in natural language. If we had an automated assistant, you could ask, “How many mortgage applications did we receive for homes in the bay area yesterday?” And you would get the right answer.
A typical IDP integration architecture is as follows:
Some of the common features to check out in an IDP platform to evaluate their integration capabilities are as follows:
No code platform Plug and play or drag and drop options to connect upstream and downstream applications.
Question platform Option for sales and marketing team to ask any dynamic questions and get answers on the fly.
Multi-platform Integrations Support to raise queries from multiple platforms.
Data Synchronization Option to automatically synchronize the latest changes from third-party platforms.
UI configurations Options for users to configure integrations or data sources from the user interface.
Robotic Assistants Routine functions handled by robotic assistants (bots). Sometimes, even make decisions to ensure increased accuracy through STP.
Analytics Integration provides you an opportunity to have a holistic Analytics dashboard to evaluate the performance.
Some of the common methods used for IDP integration with third-party solutions are as follows:
API This is one of the most common code-based methods where multiple systems are connected through Application Programming Interfaces (APIs).
Webhooks Similar to APIs, webhooks can be considered lightweight APIs for sharing real-time information among applications.
Orchestration This is one of the effective integration methods where there are ambiguities or variations, such as the availability of semi-structured or unstructured data. It primarily focuses on automating a series of tasks to ensure seamless integration.
Here is a table that depicts the industry-relevant integration features and Infrrd’s capabilities:
Frequently asked questions
What does your pricing model look like?
We price based on the annual volume of pages and complexity of document type. We can get you preliminary pricing once we outlined a solution. Let's do this.
How can I try Infrrd before I commit to a full deployment?
Sure. The first step is to schedule a guided demo where you get to jump into the thick of it. After you explore our solution you can try a proof of concept. When you're ready, you can deploy the system to one use case. Then more use cases. Then across your enterprise.
Glad you asked. Our data extraction process runs on servers. We have found performance and accuracy decline when running on a desktop or mobile device. (Remember Infrrd is running a powerful AI stack).
In a fast-paced world filled with never-ending rivers of documents and data, organizations continuously need smarter ways to work. Teams need flexible solutions that enable them to work faster while delivering higher levels of reliable accuracy than ever before. At Infrrd, we empower teams with Intelligent Document Processing Solutions for Intelligent Work™.