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A lot has been done about the enormous value that businesses can derive for analytics from their structured data. Common applications with structured data include airline reservation systems, inventory control, sales transactions, and ATM activity. We take advantage of it to the maximum.

However, when it comes to weighing the benefits and challenges associated with leveraging data, it is easy to overlook a key underlying issue: More than 80% of all data collected by businesses does not reside in a standard relational database. Instead, it is trapped in unstructured documents, contracts, emails, social media posts, machine logs, etc.

Unstructured data presents both an opportunity and a challenge to businesses. Extracting information from PDF and scanned documents (and applying OCR), converting unstructured information into a machine readable format, combining structured and unstructured data, and ensuring data quality are a few roadblocks businesses face when it comes to making sense of unstructured data.

How do you know what you don’t know?

Natural Language Processing and Machine Learning (division of AI) present enormous potential for extracting information and mining insights from unstructured data for know-how.

However, having a vital use case and creating a strategy for integrating unstructured and structured data sources is essential to leverage data as a key strategic asset.

Here is a 1–2–3 step process of how data-intensive businesses can transform a “messy” data into knowledge driving business opportunities:

1. What’s the use case

Identifying the problem you are trying to solve is the first step of any successful data-driven initiative.
This might be identifying the root cause of decreasing employee productivity, overwhelming changes in governance or optimising the sales process.

However, the common misunderstanding about technology is that it won’t help you if your process is broken at its core. That’s why technologies like RPA (Robotic Process Automation) are not a magic remedy to such cases. If your process is inefficient by design, then by automating it you will just have an automated, but still broken process.

Focus at the core of the problem. Instead of mining information from terabytes of emails, call records, and documents, identify the information exchanges that are likely to contain key data about the exact problem you’re trying to solve — in our example case s— the sales process, workflows around  or employees’ performance.

2. Deriving value from unstructured data

Just having access to relevant unstructured data is not enough. Enterprises must have the ability to extract data trapped within emails, customer calls, scanned documents, spreadsheets, and other types of unstructured sources. To achieve this, businesses need a with optical character recognition (OCR) and report capabilities that can automatically identify and extract and summarise meaningful information for faster decision-making. Today’s Natural Language Processing and Machine Learning capabilities are enabling people to do meaningful, extraordinary work, helping them focus on the most relevant information in real time to make more informed, data-driven decisions.

3. Using structured and unstructured data to achieve optimal results

It is possible to make sense of unstructured data and draw conclusions only when it is integrated with structured data used for business decision making. For example, to gain insight into the sales process, contract management, procurement and purchase orders, legal and other operational documents need to be tied to product, customer or/and supplier data.

To get their head around unstructured data and use it to validate assumptions and support decision-making, businesses need to utilise the latest technologies which are built around human language — Machine Learning-powered solutions can improve and automate their ability to extract unstructured data, as well as populate and integrate it with databases, enterprise applications, and visualisation tools. Finding an ideal unstructured data solution may require some time and effort, but it will allow businesses and let employees focus on driving real value.

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 helps to unify information from different silos, automatically enriching it with context to derive business value.
Our software provides clarity and augments know-how in data you already have. By using Machine Learning it helps computers understand complexity of human language in contracts, reports, emails and other documents. Organisations get a better view of the most relevant information in real time to make more informed, data-driven decisions.

Curious to see what can we do for you? Arrange for a .

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