Tia  Gottlieb

Tia Gottlieb

1595608500

How Can I Automate Data Extraction From Complex Documents? - DZone AI

Business processes fed by complex documents are a bear.

NO!! Not that type of bear…This type of bear!

Why?

Complex documents.

In a place where complicated can slow things down to a crawl, complex documents suck the life out of productivity.

Sure, you might have an OCR systemin place that processes your documents.

And OCR is a good technology…for structured documents. But what about those complex, unstructured docs?

Or heck, maybe you’re still manually processing your documents. Good ol’ human effort is a tried and true way to key a document into the system that runs your business process. A human can even find the right data in a sea of complex data. Eventually.

But, humans are slow, error-prone, inconsistent, and expensive. (And, in some cases, perhaps not so excellent after all!)

Then, there are all the challenges.

Complex documents:

  • Can have multiple formats
  • Can’t be forced into a template
  • Maybe free-flowing
  • Might have tables…or worse! Nested tables!
  • Could feature images
  • Might include hand-writing…or worse! Messy handwriting!
  • [FILL IN YOUR OWN FAVORITE EXTRACTION PAIN HERE!]

The worst part? OCR systems definitely hit a wall when documents get too complex.

So much for automation, right?

(Alas, fine reader…there is hope.)

What Is a Document-Centric Workflow?

In its simplest form, a document-centric workflow is one that executes a business process. In almost all cases, documents feed the process, which includes capturing content, extracting information from the content, and taking some action based on that information.

For example, here’s a document feed process that probably sounds familiar…

I submit a healthcare expense to my health insurance to get reimbursed. I have to:

  • Copy the receipt
  • Print out forms
  • Fill out the forms
  • Get an envelope and stamp
  • Figure out the address
  • Mail it

And, that’s just my end.

In process-centric workflow use cases, content contains data and information that’s contextually relevant to the process and the business.

The content we’re all using has value trapped in it…value that’s tough to release.

Document Classification

Documents can be classified into various forms and types. Documents can be images, text, numbers, videos, or a mix of types.

Classification can be based on any number of things, including:

  • Images
  • Emails
  • Text
  • SMS
  • Annual Reports
  • Receipts
  • Invoices
  • Bank statements
  • Stamps
  • ACORD forms
  • Claims
  • Handwritten forms
  • Utility bills
  • Electrical panel
  • And a whole lot more!

Data Extraction

Information trapped in the documents can be extracted using a manual process, OCR, or some other technology. When deciding which of these to use, it’s important to know if we can extract all the information in the doc and how accurate that information is.

Then, extracted data and information are fed into a process. Think Mortgage Processing, Itinerary Processing, Loan Processing, Claims Processing, RFP Response Processing, Financial Compliance, Auditing, Expense Management, Invoice Processing, and so on.

You likely have been executing processes that require data extraction for some time. If you’re like most, you’ve run into roadblocks. And because of those roadblocks, your automation plans are stuck.

The culprit? It’s probably complex data.

How to Tell if Your Complex Data Blocks Your Automation Goals?

There’s a good reason for more process automation where possible. 10x+ improvement in efficiency, productivity, and/or cost savings sounds incredible, right?!

If your goal is to automate more of these document-fed processes that now require humans for data entry…or the ones that OCR proves it can’t handle, how do you diagnose the problem so you can meet your goals?

And, how do you know when complex data is creating a process bottleneck?

The complexity of your data likely indicates the level of difficulty you’ll face when trying to extract the data and draw insights from it.

What are some factors that make documents complex to process?

  • Content is free-flowing
  • The document is unstructured
  • It contains handwriting
  • It is made up of multiple document types
  • Formats change in the same doc
  • Fonts change in the same doc
  • The document has complex tables
  • Tables are in different locations
  • There is missing information
  • Pictures and images are present

These are document types where OCR fails, and manual processing becomes overly complicated.

**What Is the Business Result of Complex Documents? **

When you have complex documents that cannot be automated, your business suffers.

What does it look like?

  • High operational costs
  • Low process efficiency
  • Long process completion times
  • Extraction accuracy that’s too low to be useful

I think these customers nailed it when they said…

_“As a financial company, our employees spend a lot of time rewriting invoices.” _

And…

“We want to extract all the info from docs, so we can automate more processes and use all the info to build insights. But our analysts use only 10-20% of the data in the documents because we cannot extract the rest.”

#artificial intelligence #data extraction #data analysis

What is GEEK

Buddha Community

How Can I Automate Data Extraction From Complex Documents? - DZone AI
Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Tia  Gottlieb

Tia Gottlieb

1595608500

How Can I Automate Data Extraction From Complex Documents? - DZone AI

Business processes fed by complex documents are a bear.

NO!! Not that type of bear…This type of bear!

Why?

Complex documents.

In a place where complicated can slow things down to a crawl, complex documents suck the life out of productivity.

Sure, you might have an OCR systemin place that processes your documents.

And OCR is a good technology…for structured documents. But what about those complex, unstructured docs?

Or heck, maybe you’re still manually processing your documents. Good ol’ human effort is a tried and true way to key a document into the system that runs your business process. A human can even find the right data in a sea of complex data. Eventually.

But, humans are slow, error-prone, inconsistent, and expensive. (And, in some cases, perhaps not so excellent after all!)

Then, there are all the challenges.

Complex documents:

  • Can have multiple formats
  • Can’t be forced into a template
  • Maybe free-flowing
  • Might have tables…or worse! Nested tables!
  • Could feature images
  • Might include hand-writing…or worse! Messy handwriting!
  • [FILL IN YOUR OWN FAVORITE EXTRACTION PAIN HERE!]

The worst part? OCR systems definitely hit a wall when documents get too complex.

So much for automation, right?

(Alas, fine reader…there is hope.)

What Is a Document-Centric Workflow?

In its simplest form, a document-centric workflow is one that executes a business process. In almost all cases, documents feed the process, which includes capturing content, extracting information from the content, and taking some action based on that information.

For example, here’s a document feed process that probably sounds familiar…

I submit a healthcare expense to my health insurance to get reimbursed. I have to:

  • Copy the receipt
  • Print out forms
  • Fill out the forms
  • Get an envelope and stamp
  • Figure out the address
  • Mail it

And, that’s just my end.

In process-centric workflow use cases, content contains data and information that’s contextually relevant to the process and the business.

The content we’re all using has value trapped in it…value that’s tough to release.

Document Classification

Documents can be classified into various forms and types. Documents can be images, text, numbers, videos, or a mix of types.

Classification can be based on any number of things, including:

  • Images
  • Emails
  • Text
  • SMS
  • Annual Reports
  • Receipts
  • Invoices
  • Bank statements
  • Stamps
  • ACORD forms
  • Claims
  • Handwritten forms
  • Utility bills
  • Electrical panel
  • And a whole lot more!

Data Extraction

Information trapped in the documents can be extracted using a manual process, OCR, or some other technology. When deciding which of these to use, it’s important to know if we can extract all the information in the doc and how accurate that information is.

Then, extracted data and information are fed into a process. Think Mortgage Processing, Itinerary Processing, Loan Processing, Claims Processing, RFP Response Processing, Financial Compliance, Auditing, Expense Management, Invoice Processing, and so on.

You likely have been executing processes that require data extraction for some time. If you’re like most, you’ve run into roadblocks. And because of those roadblocks, your automation plans are stuck.

The culprit? It’s probably complex data.

How to Tell if Your Complex Data Blocks Your Automation Goals?

There’s a good reason for more process automation where possible. 10x+ improvement in efficiency, productivity, and/or cost savings sounds incredible, right?!

If your goal is to automate more of these document-fed processes that now require humans for data entry…or the ones that OCR proves it can’t handle, how do you diagnose the problem so you can meet your goals?

And, how do you know when complex data is creating a process bottleneck?

The complexity of your data likely indicates the level of difficulty you’ll face when trying to extract the data and draw insights from it.

What are some factors that make documents complex to process?

  • Content is free-flowing
  • The document is unstructured
  • It contains handwriting
  • It is made up of multiple document types
  • Formats change in the same doc
  • Fonts change in the same doc
  • The document has complex tables
  • Tables are in different locations
  • There is missing information
  • Pictures and images are present

These are document types where OCR fails, and manual processing becomes overly complicated.

**What Is the Business Result of Complex Documents? **

When you have complex documents that cannot be automated, your business suffers.

What does it look like?

  • High operational costs
  • Low process efficiency
  • Long process completion times
  • Extraction accuracy that’s too low to be useful

I think these customers nailed it when they said…

_“As a financial company, our employees spend a lot of time rewriting invoices.” _

And…

“We want to extract all the info from docs, so we can automate more processes and use all the info to build insights. But our analysts use only 10-20% of the data in the documents because we cannot extract the rest.”

#artificial intelligence #data extraction #data analysis

Angela  Dickens

Angela Dickens

1595608560

How Can I Automate Data Extraction From Complex Documents? - DZone AI

Business processes fed by complex documents are a bear.

NO!! Not that type of bear…This type of bear!

Why?

Complex documents.

In a place where complicated can slow things down to a crawl, complex documents suck the life out of productivity.

Sure, you might have an OCR systemin place that processes your documents.

And OCR is a good technology…for structured documents. But what about those complex, unstructured docs?

Or heck, maybe you’re still manually processing your documents. Good ol’ human effort is a tried and true way to key a document into the system that runs your business process. A human can even find the right data in a sea of complex data. Eventually.

But, humans are slow, error-prone, inconsistent, and expensive. (And, in some cases, perhaps not so excellent after all!)

Then, there are all the challenges.

Complex documents:

  • Can have multiple formats
  • Can’t be forced into a template
  • Maybe free-flowing
  • Might have tables…or worse! Nested tables!
  • Could feature images
  • Might include hand-writing…or worse! Messy handwriting!
  • [FILL IN YOUR OWN FAVORITE EXTRACTION PAIN HERE!]

The worst part? OCR systems definitely hit a wall when documents get too complex.

So much for automation, right?

(Alas, fine reader…there is hope.)

What Is a Document-Centric Workflow?

In its simplest form, a document-centric workflow is one that executes a business process. In almost all cases, documents feed the process, which includes capturing content, extracting information from the content, and taking some action based on that information.

For example, here’s a document feed process that probably sounds familiar…

I submit a healthcare expense to my health insurance to get reimbursed. I have to:

  • Copy the receipt
  • Print out forms
  • Fill out the forms
  • Get an envelope and stamp
  • Figure out the address
  • Mail it

And, that’s just my end.

In process-centric workflow use cases, content contains data and information that’s contextually relevant to the process and the business.

The content we’re all using has value trapped in it…value that’s tough to release.

Document Classification

Documents can be classified into various forms and types. Documents can be images, text, numbers, videos, or a mix of types.

Classification can be based on any number of things, including:

  • Images
  • Emails
  • Text
  • SMS
  • Annual Reports
  • Receipts
  • Invoices
  • Bank statements
  • Stamps
  • ACORD forms
  • Claims
  • Handwritten forms
  • Utility bills
  • Electrical panel
  • And a whole lot more!

Data Extraction

Information trapped in the documents can be extracted using a manual process, OCR, or some other technology. When deciding which of these to use, it’s important to know if we can extract all the information in the doc and how accurate that information is.

Then, extracted data and information are fed into a process. Think Mortgage Processing, Itinerary Processing, Loan Processing, Claims Processing, RFP Response Processing, Financial Compliance, Auditing, Expense Management, Invoice Processing, and so on.

You likely have been executing processes that require data extraction for some time. If you’re like most, you’ve run into roadblocks. And because of those roadblocks, your automation plans are stuck.

The culprit? It’s probably complex data.

How to Tell if Your Complex Data Blocks Your Automation Goals?

There’s a good reason for more process automation where possible. 10x+ improvement in efficiency, productivity, and/or cost savings sounds incredible, right?!

If your goal is to automate more of these document-fed processes that now require humans for data entry…or the ones that OCR proves it can’t handle, how do you diagnose the problem so you can meet your goals?

And, how do you know when complex data is creating a process bottleneck?

The complexity of your data likely indicates the level of difficulty you’ll face when trying to extract the data and draw insights from it.

What are some factors that make documents complex to process?

  • Content is free-flowing
  • The document is unstructured
  • It contains handwriting
  • It is made up of multiple document types
  • Formats change in the same doc
  • Fonts change in the same doc
  • The document has complex tables
  • Tables are in different locations
  • There is missing information
  • Pictures and images are present

These are document types where OCR fails, and manual processing becomes overly complicated.

#artificial intelligence #data extraction #data analysis

Vern  Greenholt

Vern Greenholt

1595599620

How Can I Automate Data Extraction From Complex Documents? - DZone AI

Business processes fed by complex documents are a bear.

NO!! Not that type of bear…This type of bear!

Why?

Complex documents.

In a place where complicated can slow things down to a crawl, complex documents suck the life out of productivity.

Sure, you might have an OCR systemin place that processes your documents.

And OCR is a good technology…for structured documents. But what about those complex, unstructured docs?

Or heck, maybe you’re still manually processing your documents. Good ol’ human effort is a tried and true way to key a document into the system that runs your business process. A human can even find the right data in a sea of complex data. Eventually.

But, humans are slow, error-prone, inconsistent, and expensive. (And, in some cases, perhaps not so excellent after all!)

Then, there are all the challenges.

Complex documents:

  • Can have multiple formats
  • Can’t be forced into a template
  • Maybe free-flowing
  • Might have tables…or worse! Nested tables!
  • Could feature images
  • Might include hand-writing…or worse! Messy handwriting!
  • [FILL IN YOUR OWN FAVORITE EXTRACTION PAIN HERE!]

The worst part? OCR systems definitely hit a wall when documents get too complex.

So much for automation, right?

(Alas, fine reader…there is hope.)

What Is a Document-Centric Workflow?

In its simplest form, a document-centric workflow is one that executes a business process. In almost all cases, documents feed the process, which includes capturing content, extracting information from the content, and taking some action based on that information.

For example, here’s a document feed process that probably sounds familiar…

I submit a healthcare expense to my health insurance to get reimbursed. I have to:

  • Copy the receipt
  • Print out forms
  • Fill out the forms
  • Get an envelope and stamp
  • Figure out the address
  • Mail it

And, that’s just my end.

In process-centric workflow use cases, content contains data and information that’s contextually relevant to the process and the business.

The content we’re all using has value trapped in it…value that’s tough to release.

Document Classification

Documents can be classified into various forms and types. Documents can be images, text, numbers, videos, or a mix of types.

Classification can be based on any number of things, including:

  • Images
  • Emails
  • Text
  • SMS
  • Annual Reports
  • Receipts
  • Invoices
  • Bank statements
  • Stamps
  • ACORD forms
  • Claims
  • Handwritten forms
  • Utility bills
  • Electrical panel
  • And a whole lot more!

Data Extraction

Information trapped in the documents can be extracted using a manual process, OCR, or some other technology. When deciding which of these to use, it’s important to know if we can extract all the information in the doc and how accurate that information is.

Then, extracted data and information are fed into a process. Think Mortgage Processing, Itinerary Processing, Loan Processing, Claims Processing, RFP Response Processing, Financial Compliance, Auditing, Expense Management, Invoice Processing, and so on.

You likely have been executing processes that require data extraction for some time. If you’re like most, you’ve run into roadblocks. And because of those roadblocks, your automation plans are stuck.

The culprit? It’s probably complex data.

How to Tell if Your Complex Data Blocks Your Automation Goals?

There’s a good reason for more process automation where possible. 10x+ improvement in efficiency, productivity, and/or cost savings sounds incredible, right?!

If your goal is to automate more of these document-fed processes that now require humans for data entry…or the ones that OCR proves it can’t handle, how do you diagnose the problem so you can meet your goals?

And, how do you know when complex data is creating a process bottleneck?

The complexity of your data likely indicates the level of difficulty you’ll face when trying to extract the data and draw insights from it.

What are some factors that make documents complex to process?

  • Content is free-flowing
  • The document is unstructured
  • It contains handwriting
  • It is made up of multiple document types
  • Formats change in the same doc
  • Fonts change in the same doc
  • The document has complex tables
  • Tables are in different locations
  • There is missing information
  • Pictures and images are present

These are document types where OCR fails, and manual processing becomes overly complicated.

#artificial intelligence #data extraction #data analysis