Data forms the crux of any business process. And so, it follows that whatever trajectory a business takes very much depends on the data it has been fed. Getting access to the right data is vital for the success of a company. Say, you are processing a claims form. If due diligence with regards to the authenticity of the reimbursement claimed or the provider information is not done, one could end up overpaying or paying a fraudulent claimant. Successful companies are identified by their core competencies. But more importantly, successful companies are characterized by their track record in matters of compliance with regulations, industry benchmarks, auditing standards, etc. — and getting data right is very much part of being in compliance.
That’s why more and more companies are setting aside a definite budget for data management — the opportunities and risks associated with getting or not getting data right, respectively, are too extreme to turn a blind eye to. As a matter of fact, the cost of working with bad data is huge but the trouble is, many organizations fail to include this cost in their risk assessment process. Not surprisingly, poor data quality has resulted in considerable financial loss for many companies. Given how critical it is to get data right, it is necessary to invest in the right data quality tools and set up a definite data management framework.
Adding to the costs of getting data wrong is the complexity in data management, given the diverse sources of data. Just looking at the following data sources should give you an idea of the magnitude of data that needs curating and managing on a regular basis:
Needless to say, apart from the diversity in data sources, the complexities are compounded by the data types and formats as well. All the aforementioned data sources may come in unstructured (PDF, MP3, JPEG) or semi-structured (Mongodb, CSV, XML) formats that need to be processed into structured data types (SQL, Oracle) before entering them in host systems such as ERPs, accounting software for further business applications and use.
The trouble with getting access to the right data is that data comes in all forms, semi-structured and unstructured, and is difficult to capture. This is especially true for companies that deal with paper for all their business transactions. Siloed data from source documents such as invoices, statements, sales orders, etc., as well as from processes and workflows, is of little value if it cannot be captured instantly.
In today’s fiercely competitive marketplace, where companies are striving to be increasingly consumer-centric, not knowing what value your transactions documents hold can severely impede your chances of growth. But then, how do you capture transactional information? What companies need is a single platform for data capture, processing, and management. An intelligent document process automation platform with powerful intelligent data capture technology can assimilate data from the edges of your organization onto a common platform for processing. The platform employs digital transformation technologies such as AI and Machine Learning to capture data from semi-structured and unstructured data sources, validate data against known master data, and assign workflows to that data to process it into structured data sets for use in ERPs and other host systems.
Intelligent data capture makes use of OCR/ICR and Machine Learning technologies to extract relevant details from transactional and source documents. These details are verified before being fed to a business application like an accounting software or analytical tool for further analysis, inference, and insights.
A Document Process Automation solution meets the urgency required to discover, capture, process, and govern data and documents to ensure time-bound and accurate decision-making. Moreover, with all business data accounted for and in one place for anytime, anywhere access, you are assured of regulatory compliance and audit at all times.
Need to get a grip on your invoicing data? Talk to Artsyl — InvoiceAction.