Cognitive AI and the Power of Intelligent Data Digitalization

by July 19, 2020

Cognitive AI

How can enterprises harness the vast sources of non-digital data from unstructured documents like claims, receipts, invoices and scanned paper forms?

In a quest to decode what keeps the world moving, enterprises across the world are baffled. It is not precious metals or even cryptocurrency – it is data. The adage that data is the new oil holds true and soon, every company in the world will either buy or sell data, and the value of this corporate asset would gain prominence with each passing day.

Data fuels digital transformation that drives a mammoth disruption across all industries. It is the key differentiator, coming at a massive speed characterised by volume, variety, velocity and veracity in a very live environment.

The question remains unanswered, how do enterprises gain the most from this valuable resource? The answer is through Data Digitalization. As the name suggests, data digitization is the process by which physical or manual data files like text, audio, images, video are converted into digital forms. The perks of digitized data are plenty, starting from-

  • Document preservation
  • Sequential record maintenance
  • Customized information access
  • Seamless information access through data pipelines.


Pathway to Data Digitalization

  • Ultimate Data Use Assessment

The initial step of data digitalization starts with the identification of data needs based on client requirements. This is based on system analysis, requirement specifications and system design.

  • Developing a Pilot Application

After an enterprise assesses its data requirements, the next step is to develop a technology roadmap and send it for approval and testing. After the technology roadmap is approved, the data source chart is developed, which may include a printed format converted into a digital format.

  • Gaining Intelligence from Images

The old images are scanned, while the faded images are recovered using advanced digital correction software. The sound and video data are retrieved through the data capturing software which is ultimately converted in the digital format.

  • Checking for Document Accuracy

The printed documents are checked for physical accuracy. The process imbibes Optical Character Recognition (OCR) software scanning, where the output is checked manually by proof-readers, subsequently converted into PDF, MS-Word, ASCII and HTML formats.

The takeaways from Data Digitalization

  • Multi-user access at any time
  • Systemic data management
  • Visible cost savings
  • Operational efficiency through labour processes and data input control

Into Data digitization, the physical documents are uploaded online and scanned to a virtual digital medium, which is then digitized to a high-quality format and structured according to the customer’s needs.

Enterprises can also opt for cases in which these documents are transferred to an electronic archive which complies with the stringent security requirements and provides an option to manage data round the clock from any computer anywhere in the world using web applications.

In a crux, leveraging the power of cognitive computing algorithms, enterprises can synthesize raw data from various information sources, weigh multiple options to arrive at conclusive answers. To achieve this, cognitive systems encapsulate self-learning models using data mining, pattern recognition and natural language processing (NLP) algorithms.

For enterprises to use data digitalization systems they require vast amounts of structured and unstructured data, fed to machine learning algorithms. Over time, these cognitive systems refine the way they identify patterns, process data to become self-sufficient anticipate new problems and possible alternative solutions for the model.

While AI relies on algorithms for problem-solving, patten identification from the hidden data, cognitive computing systems have the loftier goal of creating models that mimic the brain’s reasoning process to solve the modern concerns of data digitalization and data adaptability.

The increased resilience towards data and digitalization will change the world of data forever. Is your organisation ready with its own digitalized data pipelines?