The Guide to Unstructured Data Automation - Indico Data

The Guide to Unstructured Data Automation

Unlocking the Value in Your Unstructured Data

How the Indico Unstructured Data Platform delivers new corporate insights from your troves of unstructured data.

Industry experts estimate 85% of all data residing in an organization is unstructured, and they expect unstructured data to grow by over 100% in the next few years. All this unstructured data contains valuable insights, but only if you can unlock it. To date, companies are falling short of that unstructured data analytics goal, as only about 2% of it is being used in a meaningful way.

Indico Data can change that dynamic. Our Unstructured Data Platform turns unstructured data into structured data, enabling companies to:

Automate document-intensive processes involving unstructured data
Analyze unstructured data to glean actionable intelligence
Apply unstructured data to mission critical enterprise workflows

What you’ll find in this guide:

Unstructured Data Challenges

Manually Extracting

Relevant information is laborious and error-prone

Document Variation

Makes rule-based workflows and robotic process automation impractical

Intelligent Automation

Is needed to understand data context

Unstructured Data Fast Facts

85% of data

In the enterprise is unstructured.

Less than 2%

Is leveraged by unstructured data analytics tools.

80% reduction

In resources required with AI.

Unstructured data vs. structured and semi-structured data

When comparing unstructured data to structured and semi-structured data, there are key differences.

Understanding the different data types:

Structured data is highly organized, typically in a database or spreadsheet with rows and columns. Each piece of data is mapped to a specific fixed field or location.
Unstructured data comes in many forms, including Word documents, PDFs, emails, images, and videos. Any data that is not in a highly structured format is unstructured.
Semi-structured data falls somewhere between the two extremes, such as an email where the text is unstructured but the header contains structured elements.

If you think about the data in your organization, it’s easy to see most of it is unstructured.

Why unstructured data is valuable:

All this data is valuable because it holds years’ worth of corporate intelligence. Unlocking this data by transforming it into a structured format and feeding it to an analytics engine is game-changing. Unstructured data analytics enables numerous use cases that extract value from all of your data, including:

Examining news feeds to generate market intelligence.
Analyzing unstructured call center recordings to better understand brand loyalty.
Applying unstructured data analysis to financial records to identify fraud.
Automating data-centric processes to improve efficiency.
Extracting data from insurance claims to inform new underwriting models.

Unstructured Data Automation and Analytics Use Cases

Lease Abstraction: Extract critical details from unstructured lease documents.
Deal Management: Automate abstracting commercial lease details into deal management systems.
CAM Reconciliation: Automate compilation of common area maintenance allocation rules.
Rent Rolls: Extracting data from rent rolls enables insights into cash flow, turnover rates, and opportunities.
Mortgage Processing: Speed review of reports and apply analytics for informed underwriting decisions.

Learn how to unlock your unstructured data

Unlocking the value in unstructured data requires an unstructured data solution that can ingest unstructured data, extract valuable data, and translate it to a structured format. The Indico Unstructured Data platform does this by taking advantage of AI technologies including machine learning, transfer learning, and natural language processing.

The platform can read and understand unstructured data just as your employees would, making it easy for you to label documents and identify what sorts of data to extract.

Once labeled, the Indico Data platform builds a model that automates the extraction of relevant data from your unstructured documents, translating the data to a structured format, typically JSON or .csv. This prepares the data for various applications including automation and analytics.