Data is everywhere, but are you harnessing your information effectively? We show you the advantages of automated data processing.
The biggest challenge with data is access and understanding. While most law firms and GCs understand the value their contracts hold, accessing that information and making sense of it to support strategy or action is another matter.
Most firms use manual data entry for documents and contracts, firstly because it’s seemingly more convenient and secondly because they think it’s more efficient than using technology to automate this process.
However, there are some major disadvantages with manual data processing:
Quality: Ensuring absolute consistency is challenging with a manual process because people are prone to error when performing tedious and repetitive tasks such as manual processing.
Speed: People cannot beat technology when it comes to speed.
Cost: Manual data processing is not scalable as adding more staff to digitize contracts will increase operating expenses.
Risk: Security around manual data processing is more easily compromised, increasing risks that classified or sensitive information will be leaked.
With so many drawbacks to doing things manually, how do you get started automating this process?
Automated data processing consists of two parts:
- Data Extraction
- Text Classification
Automated Data Extraction
Optical Character Recognition (OCR) is a technical term that means computers can recognise the characters within a document and convert them into machine-encoded text.
From a legal perspective, it means that handwritten documents, court notes, or any printed documents can be digitized so they can be electronically edited, searched, stored and used by other artificial intelligence techniques for analysis.
Once the character is extracted, the output is matched to a lexicon (a dictionary of characters). OCR is a mostly automated process (most fonts are fully recognised) although for handwritten documentation it requires some initial ‘training’.
Now the data has been accessed (or digitized to a standard) we need to make sense of the information for it to be actionable.
Text classification is organising or classifying the information into interpretable groups. There are two types of data:
Structured: Excel, SQL, CMS, customer data, transaction history – data that can be easily labelled with an identifiable field such as name, date etc
Unstructured: Documents, contracts, texts, emails, chat conversations, websites social media – data that takes additional training in order to be classified with specific fields.
Deep learning is the best approach to quickly and effectively classify unlabelled and unstructured data – ie your contracts and supporting documents. Deep learning models perform automated feature extraction without any need for humans to intervene and assign a probability to classify based on a particular label. The classified data can be then stored in the repository as structured data.
This structured data is then in the right format for searching and analysis, which is where the real value from data is achieved.
Why does this matter?
Despite coining the phrase more than 250 years ago, Benjamin Franklin is still right today; time means money, and time spent searching for files or contracts that are improperly digitized or stored is time wasted.
But it’s not just about searching and finding documents. On a macro level risk, efficiencies, insights and strategy can all be managed through access and understanding of the data. While on a micro level, simply identifying a single document that deviates from the standard and poses an unknown risk to the business means it can be rectified before it becomes a liability.
Digitized data that’s accessible and actionable is the holy grail of organizations today because business leaders know that making decisions based on insight is smarter than making them on instinct.
Contact Exigent today for help improving access and understanding of your data.