According to Gartner, "The market for document capture, extraction, and processing is highly fragmented. Data and analytics leaders should use this research to understand the process flow and differentiated capabilities offered by intelligent document processing solutions". Gartner's recently released "Infographic: Understand Intelligent Document Processing" covers these 6 critical flows in IDP. 1. Capture or Ingestion 2. Document Preprocessing 3. Document Classification 4. Data Extraction 5. Validation and Feedback Loop 6. Integration https://hubs.ly/Q011Bk6F0

1. Understanding IDP: Data
Integration
According to Gartner, "The market for document capture,
extraction, and processing is highly fragmented. Data and analytics
leaders should use this research to understand the process flow
and differentiated capabilities offered by intelligent document
processing solutions". Gartner's recently released "Infographic:
Understand Intelligent Document Processing" covers these 6 critical
flows in IDP.
1. Capture or Ingestion
2. Document Preprocessing
3. Document Classification
4. Data Extraction
5. Validation and Feedback Loop
6. Integration

2. 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.
Why Integration?
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.

3. 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

4. homes in the bay area yesterday?” And you would get the right
answer.
Integration Features
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,

5. 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.
Integration methods
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:

6. Feature Infrrd's IDP
Data Mining
✔
Transactional Integration
✔
Search Integration
✔
NLQ