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Unleashing the Potential of Efficient Document Analysis and Review

The value of raw data (on its own) is near zero. Even if you have the number, what difference does it make when you don’t have a reference point? When you analyze it, however, you turn it into actionable information that you can use to enhance your decision-making process, better risk assessment, and even legal and regulatory compliance.

Analyzing documents is how you turn this data into actionable information. The problem is that the process for doing so isn’t always as simple as you would like it to be.

Fortunately, there are many tools that you can employ to improve this process. Here are some tools and how to use them to unleash the potential of efficient document analysis and review.

1.  Document management system

Previously, you would have to look for the data you want to be analyzed manually. Sure, if you had an established naming convention, this task would be a lot easier, but the truth is that even then, such a thing would be fairly complex.

In other words, the document analysis process hasn’t even started, and you’re already wasting time on something trivial – a step you could have skipped if you used a DMS.

DMS do this because they have centralized storage and optimized navigation so that you can always find what you’re looking for. This makes the search and retrieval so easy. Any decent document management system has a great version control feature installed if there are any updates. Still, using a more specialized tool for version control might be better. Speaking of which…

2.  Document comparison tool

Sometimes, you’ll have to analyze your documents compared to another file. In this scenario, a comparison tool like Draftable API is the most efficient tool type.

These platforms offer an elaborate text analysis. The process is fairly simple – the document is first broken down into analyzable components (words, sentences, and paragraphs). Then, it aligns both files to ensure that the appropriate parts of the documents are compared.

The most important stage is the detection of differences, and it heavily depends on the circumstances, file types, and the comparison algorithm that the tool uses. This is why different tools may give you different results (although, in theory, they use the same process).

Finally, you get the presentation of differences visually, usually through color-coding and side-by-side comparison of files. Once everything is done, you get reporting and exporting, which is vital for the administrative side of the task and future comparisons (historical recordkeeping).

3.  Intelligent information management systems

Sometimes, a regular document management system will just not be enough. This usually happens when the data you’re working with doesn’t come in structured formats. In this scenario, you need an IIMS instead.

These platforms are more potent and provide capabilities for version control, metadata management, and more secure access to the given data.

Aside from this, the IIMS tools are amazing for your workplace automation. With them, you can automate sequential or parallel tasks, assign responsibilities and track progress.

It’s also important to note that IIMS are invaluable as collaboration tools. Their use in seamless information exchange is just unparalleled, and you can use it to enhance both productivity and teamwork.

4.  The AI revolution

Many things have changed since the recent advancement of AI algorithms and tools. It’s only natural that document analysis and review were among these things.

  • First, an AI-powered tool uses NLP (natural language processing), which allows these platforms to understand the language at the near-human level of comprehension. At the same time, they’re doing it far more quickly and have an easier time detecting hints and developing logical insights.
  • The OCR (optical character recognition) is a technology that many people were eagerly awaiting, but it just wasn’t there yet. Today, it can recognize scanned text and turn image-based documents into texts that machines can accurately read and interpret. Invoices, forms, and contracts can be much easier to analyze.
  • NER (named entity recognition) is another massive advantage in document recognition. A platform can now recognize an individual, a location, or an organization, find it in the database and immediately make all the necessary parallels. This is especially useful in digital marketing and detecting unlinked brand mentions.
  • One of the things that a machine could never before do is the concept of sentiment analysis. Today, with AI-powered algorithms and machine learning, this is no longer as big an obstacle as it once was.

These are just some examples of how AI tools (in general) are getting more efficient at what they do. This involves document management systems, document comparison tools, and IIMS alike.

Wrap up

Imagining data-based decision-making without at least one document analysis and review instance is impossible. Fortunately, with the help of the right tools, this process can be made instantaneous and highly reliable. The best part is that, with the rapid pace at which AI algorithms are advancing, this will only pick up in the future.

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