Automated data extraction is rapidly becoming the go-to method for companies handling large volumes of data.
But what exactly is it?
Understanding automated data extraction means first knowing what it isn’t. It’s easy to confuse data extraction with data harvesting, or even data mining.
Let’s distinguish these terms from one another. Firstly, data harvesting refers to assembling data for later use, such as training AI models. In contrast, data mining refers to identifying patterns in large, stored datasets.
Finally, data extraction simply means turning unstructured data into structured data. For example, automatically converting a scan of a financial statement into a CSV file is a standard use of automated data extraction technology.
To begin, it’s vital to understand the logistics surrounding data sources. In particular, consider the unstructured data you want to extract from – web pages, scans or PDFs, etc. Then consider what is the most helpful format for the outputted data – Excel, CSV or JSON?
Choosing your file types essentially defines the start and end goals of the project. Now, it’s time to work out the middle—or, rather, how to make the extraction process automatic. By integrating automation technology, you can upload your documents and then receive the structured files in one motion.
There are several automated data extraction solutions on the market and numerous ways of defining the best fit for your project’s requirements: budget and resources, the vendor’s case studies and the features of the technology.
Did you know it takes approximately five dollars to manually enter data from a single document? However, when it comes to calculating the costs of an automated data extraction solution, it's not always clear-cut. For example, you may encounter hidden costs, such as setup and maintenance fees or user licensing costs. Finding an automated data extraction vendor with transparent pricing is essential for establishing a budget.
To calculate the likely costs of your project, consider the average volume of documents you’ll extract from per month, the type of document, and any additional costs (e.g., data transformation, validation, etc.).
Also consider whether your company’s IT architecture can support an intelligent document processing platform. Generally, you can integrate AI technology easily: all you need is an existing workflow to access and store data. However, there are two other resources involved with an extraction automation project that you’ll need to take into account – time and management.
If you’re extracting from a unique type of financial document or report, it’s likely that you’ll need to train the model. Although training generally only takes two days (at the most) and can be arranged by the vendor, you’ll need to factor this into the project’s timeline.
Communicating the project’s requirements and securing buy-in from stakeholders will require management oversight. Effective management, from finding solutions to implementing them both technically and culturally in the workplace, is crucial for the success of AI data extraction technology.
Finding a case study that parallels your company's use case is an essential step of the research process. It's also worth noting that a reputable vendor will always have a selection of case studies. When reviewing them, check whether the demonstrated results (e.g., reduction in time-to-data, increased accuracy and reduced costs) align with the objectives of your organisation's project.
A good case study will answer:
✔️ Why the data extraction solution was initially chosen
✔️ The implementation process
✔️ The effects of the technology (e.g. decreased costs, increased time-to-decision)
✔️ The future of the technology in the organisation
Since automated data extraction involves using a third-party vendor, ensure that the vendor operates with the highest level of security. Ideally, they should have ISO/IEC 27001 certification, which demonstrates that they can handle information assets securely.
A vendor could deploy other data security measures, such as an initial data sharing agreement, encrypting the data during transit, or enabling access controls. During the initial demo, it’s best to initially communicate any notable privacy requirements.
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