Knowledge Discovery from Process Implementation

Knowledge Discovery from Process Implementation

From Information Chaos to a Knowledge Repository

Knowledge Discovery is not a new subject in either research or industry — wherever there is business, data and knowledge follows. The difference is, today that data is being mined using intelligent bots rather than human effort. The best information regarding the trajectory your company is taking and the opportunities to redirect it to boost revenues and growth can be found in your mission critical operations. Functions such as invoice processing, sales order processing, claims processing, etc. are critical to the smooth running of operations within your company. When it comes to getting knowledge out of these mission critical processes, you need to decipher and identify the metrics for each of the processes. In the case of invoice processing, your usual metrics would be Days Payable Outstanding (DPO), invoice cycle times, cost per invoice processing, and so on. By gathering data against each of these metrics, you will be able to gauge the state of your accounts payable operations and make informed decisions on changing or amending operations to improve your company’s overall finances.

From Information Chaos to a Knowledge Repository

Information chaos is a real thing. Accumulating data is one thing, tapping into it to identify meaningful information and clues is a whole different ball-game. With the plethora of data you gather from business processes these days, it is next to impossible to sort out, classify, and map it manually for further inference and business analysis. You need software, an Intelligent Process Automation (IPA) tool that will mimic human effort and extract and decipher the same data in large volumes and at heightened speed.

Intelligent Process Automation helps extract high-level knowledge from low-level data such as invoices and sales orders. IPA utilizes digital transformation technologies such as AI and Machine Learning that help detect patterns and process uncertain data to identify certain definite values along the process chain. The following are typical of the steps an Intelligent Bot executes before providing processed data that can be used for business use:

From Information Chaos to a Knowledge Repository

Data Acquisition: data from structured and unstructured sources is captured by an Intelligent Data Capture (IDC) tool that uses AI and Machine Learning to identify familiar data fields on a document. When encountered with new, unfamiliar documents such as an invoice from a new vendor, the IDC bot’s Machine Learning capabilities become useful where the software learns from human actions performed the first time to process that invoice, and recollects and applies those actions for processing subsequent invoices.

Preprocessing: the data that is collected may not be ready for inference. Large and inconsistent datasets need to be structured to allow IDC bots to capture them completely, without missing out on critical information. For this, data should be preprocessed by classifying and sorting it — by allocating or assigning data to specific fields and attributes, we can give datasets a proper structure.

Collect data by defining what fields, attributes, features are most informative

You define the fields and attributes based on the end-goal of implementing your mission critical processes. For example, when implementing the procure-to-pay process, your end goal may be to make all pending payments to your vendors, so one of the essential fields would be your invoice “TOTAL” alongside the “ITEMS” of purchase. This data structuring is essential when capturing data using IDC bots as it helps the bots easily identify the data from sources such as an invoice and match it to the pre-existing fields, after which it sorts and places data in those respective fields. The result is a structured data format.

Structured data obtained in preprocessing provides the initial knowledge that could be extracted and tied to a goal to draw out predictions about, say, a customer.

Application of that knowledge — Goal of Discovery

Postprocessing: The structured data obtained in preprocessing could be further evaluated and visualized for insights by incorporating it in an application such as a CRM or Accounting software. These applications plot data that give knowledge, which can be used directly for prediction and decision-making. By integrating intelligent data capture software with such business applications, we are essentially transforming the structured data to a form that is easily understandable by the end-user.

An Intelligent Process Automation platform with intelligent data capture capabilities applies these steps during processing of document content-dependent functions and outputs structured and processed data. With this, IPA facilitates easy data availability to ERPs and other business applications for further analysis and inference.

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