Today’s offices generate new data at a dizzying pace, especially when it comes to law offices that need to keep track of reams of official documents, plus eDiscovery, and various legal due diligence files.
Just finding files and trying to correlate one subject with another can take hours of manual work. You would think that with files now mostly in digital format, it would make things easier to find. But if you haven’t put a structured data strategy together, locating the right digital file you need can be like looking for a needle in a haystack.
56% of organizations say that locating unstructured personal data is the most challenging issue related to data subject access requests.
The problem that many Chicago law offices have with their technology setup is that they’re using unstructured data and manual processes. This costs them days of productivity a year in unnecessary work.
A much better way is to automate processes and correlate data using a specific schema, i.e. structure.
What’s the Difference Between Structured & Unstructured Data?
Unstructured data is generally how most data comes in. For example, you receive an email on a particular subject, but that email isn’t using any code that ties it to another piece of data – it’s unstructured.
Data that is unstructured, is basically unconnected to any central file system or plan that allows various pieces of the data to be shared, reused, and located easily.
An example of unstructured data includes:
- Text files
- Social media
- Mobile data
- Voice or music files
- Data from business applications
If you have several eDiscovery documents and you need to locate the use of a company name in each of those documents, in most cases you would have to search them manually. This is because the company name could be in a different area in each one, and there could be different company names in different documents.
This time spent searching information is time-consuming and results in manual processes that eat up productivity time and can lead to user errors.
The average office professional spends 20% to 40% of their time searching for information.
Structured data has a plan and purpose. It’s a strategy that classifies different types of data so they can be automatically discovered in multiple documents and shared using a connective database.
For example, if your law firm used structured data, you would format documents to create fields of different types of information. If you have a field for “company name,” then any time you needed to pull a company name from structured data documents, no matter where that name was on the page, it could be instantly located.
Structured data is already used heavily on websites and by Google. When you see search results that have things like recipes or reviews showing up with the page information, that is an example of structured data in use. The page owner specifically formatted that information on the page using the structured data scheme, Google’s search bots read that data and displayed it in the search results.
How Structured Data Connects to Automation
There are any number of different blocks of information that would be helpful to designate and track using a structured data format.
These include things like:
- File name
- Key clauses
- Covenant types
- Phone number
- Law firm/lawyers involved
- Client number
- Billing data/code
- Transaction type
When you begin creating documents to use structured data, you’re setting up a system that has the appropriate type of data going into the right field. Not only does it make this data easy to populate on demand, it ensures vital information isn’t missing from a document.
Here are some ways that structured data and automation go together.
Populating Common “Boilerplate” Clauses
If you have a client that uses a specific non-disclosure clause in many of their documents, this would typically be a manual copy/paste process for whomever was creating the contract.
Someone new might actually leave it out if they’re unaware of it, or misword it, causing major problems.
If you have that clause set up as a structured data field and then use structured data on all your documents and contracts, that clause could simply be populated from a master database using the field name you’ve given that clause.
Fewer Billing Mistakes
Billing mistakes can cost law firms significantly, especially as they add up over time. If one piece of work done for a client is missed when their bill is prepared, it can be hard to recapture that money.
When you use a billing code in a structured data field, and connect your data using a shared cloud or on-premises database, the billing department could automate the process by pulling in all documents with a specific client billing code.
Those are just two examples. There are multiple things you can do to reduce costs, productivity losses, and mistakes using a structured data schema with automation.
Get Help Automating Your Processes & Reducing Manual Work
ProdigyTeks can help your Chicago area law firm prepare for the future by putting the technology frameworks in place that support automation and more efficient business processes.
Schedule a free phone consultation today! Call 312-600-8357 or reach us online.
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