Big Data and analytics have recently gained traction, triggering enterprises to make more informed decisions with granular insights. The global enterprise data was last estimated to jump from one petabyte (PB) in 2020 to 2.02 petabytes in 2022, as per reports. With so much information at stake and opportunities left untapped, enterprises are increasingly looking for intelligent document data extraction solutions.
However, businesses often face challenges in the data extraction process, especially when most are catered to by humans manually.
Sheer volume of data: Enterprises have to deal with quintillion bytes of data every day, which presents a data management challenge for owners. And add to the problem is the domain complexity of data generated that increases issues pertaining to categorization, processing, and data extraction.
Siloed data repositories: The existence of multiple data storage and siloes between departments prevent the timely availability of data for improved decision-making.
Inconsistency in data captured: As per studies, nearly 80% of data captured are presented in the unstructured format. Deriving inputs from unstructured or semi-structured datasets manually is a time, cost, and labor-intensive process, delaying other workflows directly dependent on such data.
Lack of skilled resources: There is a major scarcity of skilled professionals who are expertise in data extraction processes and deriving analytics from various datasets. Wherein training entry-level personnel on data management technology can prove uneconomical for the enterprise.
Absence of tools and technology: Technology gap is yet another challenge confronted by most enterprises as they steer clear of intelligent tech-based solutions and rely heavily on human resources.
Some other challenges that enterprises need to overcome are:
Given the challenges mentioned above, an intelligent solution is the only way enterprises can prevent any opportunity leakage from data mismanagement and poor extraction processes. By harnessing the power of Artificial Intelligence and Automation, the time and labor-intensive factors are taken out of data management and subtle anomalies in large datasets are easily detected, which can escape the naked eyes of a human data extractor. Hence, the outcome is higher quality data, more accurate insights, and valuable business opportunities to support competitive decisions.
One of the biggest benefits of data management technology is its capability to organize data. With the help of a catalog software solution, a central repository of data is created to store files, and other documents in varying formats and are readily available as and when needed. The unstructured or semi-structured data is then converted into machine-ready datasets for easy consumption, accommodating a seamless data extraction process.
More importantly, the cloud-based infrastructure maintains a complete backup of all data collected in the repository, with robust security features preventing any data leakage. Manually catering to all the mentioned functionalities above-is no longer feasible, especially when competition is cut-throat and the company’s reputation is at stake.
As per reports, an estimated 90% of large datasets generated happen to be unstructured. And these massive unstructured datasets are a treasure trove of information, highly valuable for crucial decision-making. But unstructured data is hard to analyze; hence, the data extraction processes could be lengthy and cumbersome.
In order for enterprises to extract intelligence, they need tech-enabled solutions to convert unstructured or semi-structured data into structured and consumable information. Following is a list of essential steps put together to aid unstructured data conversion:
Data cleansing: Cleaning unstructured datasets allow enterprises to verify sources and organize databases for further analysis. During data cleansing, irrelevant information is omitted to prevent the loss of valued insights.
Extracting entities: Semantic analysis and natural language processing capabilities are leveraged to retrieve entities referred to as ‘person,’ ‘place,’ or ‘business.’ This demonstrates the correlation between various data elements further considered while deriving insights.
Data categorization: Data classification demonstrates the relation between the source and retrieved information. This allows for the seamless processing of unstructured data, where multiple words refer to one entity.
Sentence chunking: Categorization also covers sentence chunking, where the data is organized based on the relationships those words have with other words.
Design a clear roadmap: Before moving forward with data analysis, it is important to have a clear roadmap showcasing the actual objective. That’s how the outcome of the data extraction process can be put to commercial usage effectively.
Data analysis and storage: Once the raw data has been organized and the objectives determined, insights are mined and analyzed for sound business judgments. And the required data are securely stored for the future.
Document data extraction involves retrieving data from single or multiple sources, processing and combining various datasets for further analysis and informed decision-making. Data extraction allows enterprises to consolidate information into a centralized system for easy accessibility of granular insights.
10 effective strategies to modernize the document data extraction process
A tech-enabled document data extraction solution is the only way to upgrade the entire process. But, as mentioned earlier, AI and related technologies will not perform as expected without data and inputs. Hence, to fully leverage their benefits, enterprises need to embrace and practice the following strategies:
It is said that AI can work best in the presence of data. Powered by Machine Learning and Automation capabilities, it can perform the following tasks:
As technology advances, new capabilities are added to existing document data extraction solutions to build a future-proof model. The objective is to achieve the unification of all resources, including data. Hence, scalable platforms are sought so that existing models can rapidly align with changing digital standards. Furthermore, since many industries are finding various use cases to optimize enterprise data, modernizing the data extraction process seems the best way forward.