What‘s Covered:

We live in a data-driven world. Organizations across industries rely on data to guide strategic decisions, improve efficiency, and better serve customers. However, much of today‘s most valuable data remains trapped in unstructured or semi-structured documents not easily accessible for analysis.

This is where data extraction comes in…

• What is Data Extraction?
• The State of Data Extraction Adoption
• Technical Approaches to Data Extraction
• The Business Case for Data Extraction
• Key Capabilities to Look For
• Specialized Use Cases
• Emerging Innovations
• How to Evaluate Solutions

Let‘s get started.

What is Data Extraction?

Defining Data Extraction

Data extraction is the automated process of pulling structured data from documents or databases and transforming it into a standardized, machine-readable format. This allows the data to be loaded into target databases or applications for analysis and operations.

Some examples include:

• Converting details from scanned purchase orders or supplier invoices into digital records
• Ingesting product specs from spec sheets into product databases
• Parsing transaction histories from bank statements for financial reporting

Data extraction liberates proprietary data trapped in unstructured documents – an enormously valuable asset for most companies.

The High Costs of Manual Data Entry

Per recent research, Fortune 500 companies still process over 1 million invoices annually via manual data entry.[1] This tedious process leads to:

• Incorrect, incomplete or duplicate records
• Long turnaround times delaying business insights
• Significant payroll and opportunity costs

Even stringent verification checks fail to eliminate data quality issues at large volumes. The rising tide of unstructured data now outpaces human capacity. Automation is imperative to efficient extraction.

The Strategic Importance of Data

Today‘s most innovative companies embrace data-driven decision making, relying on analytics around:

• Market opportunities
• Customer segmentation
• Personalized products and services
• Demand forecasts
• Inventory optimization
• Customized marketing campaigns

However, this analytics can‘t happen without clean, structured data powering databases. Data extraction serves as the crucial bridge liberating insights previously locked away in scattered documents.

Organizations that capture this capability position themselves to significantly outperform competitors still mired in manual approaches.

The State of Data Extraction Adoption

Current Usage Statistics

Data extraction solutions are seeing rising adoption but still far from ubiquitous. Per IBM‘s 2021 survey of 700 data professionals:[2]

• 57% of organizations use data extraction, up from 49% in 2020
• Larger companies show much higher usage at 68% vs. 43% for small businesses
• Adoption leader industries include high tech (62%), financial services (60%), and manufacturing (55%)

So while over half of respondents leverage extraction, significant room for expansion remains, especially among smaller firms.

Drivers and Barriers to Adoption

Gartner highlights core drivers for adoption:[3]

• Cost control – reducing manual labor expenses
• Compliance – meeting regulatory reporting obligations
• Business agility – accelerating critical data flows

Meanwhile, obstacles deterring adoption include:

• Perceived tool complexity requiring scarce technical skills
• Syncing with multiple legacy systems
• Associated process change management

Vendors that provide intuitive, low-code solutions integrated across core business systems help overcome these barriers.

Technical Approaches to Data Extraction

Before evaluating specific solutions, consider what underlying technical approaches best suit your needs:

Rule-Based Extraction

Simple rules-engines apply preset patterns to identify and parse relevant data points. This approach excels for structured documents like financial statements with known field locations.

However, rule-based methods lack flexibility handling variability across less standardized documents. AI-based data extraction often supersedes rules for enhanced accuracy.

AI and Machine Learning

AI algorithms automatically classify document types, extract tables and fields, and even determine semantic meaning. Machine learning self-improves, applying lessons learned across millions of data points.

AI slashes manual configuration demands and boosts flexibility for semi-structured and unstructured documents relative to rule-based alternatives. Cloud platforms readily scale the computational powering AI extraction.

Cloud vs. On-Prem Distribution

Cloud delivery confers multiple advantages including:

• No hardware investment
• Limitless storage and computing expandability
• Global network hardening resilience
• Evergreen automatic updates

But some highly regulated sectors prefer on-premise options meeting strict data control policies. Multi-tenant cloud solutions now incorporate robust security and governance capabilities on par with on-prem options. Most organizations find cloud solutions superior.

The Business Case for Data Extraction

Deploying data extraction delivers both tactical efficiency improvements and strategic advantages:

Accelerates Processes and Cuts Costs

Per academic research, invoice data extraction alone improved process efficiency 83% over manual methods,[4] with similar large gains across document types. Better still, extracted data integrates into systems for real-time visibility vs. batch updates.

Multiplied across enterprises, these dramatic time savings translate to substantial cost reductions from lower headcount plus opportunity cost recoveries allowing skilled staff to undertake high-value tasks instead.

Boosts Data Fidelity

Inevitable human errors at scale lead to inaccurate, incomplete or duplicate records. Per leading vendors, automated extraction slashes these manual error rates by 80% or more.[5]

Higher fidelity data leads to superior operational and strategic analytics.

Drives Digital Transformation

Transitioning critical workflows from analog to digital conveys game-changing performance improvements via:

• End-to-end digitized document flows minimizing delays
• Breaking down data silos into unified analytics
• Enterprise data literacy and self-service

Extraction obliterates roadblocks to foundational digital transformation success, knocking down data silos.

Projection: $1.46B Market by 2026

Third-party estimates forecast the global data extraction software market growing from $0.83B in 2021 to $1.46B by 2026 as adoption accelerates across industries.[6]

Key Capabilities to Look For

When evaluating solutions, seek platforms delivering:

1. Omni-Channel Input Flexibility

Modern unstructured data varies extensively. Seek broad support for emails, scanned docs, EDI messages, Word files, PDFs and more. Built-in OCR enables scanning huge volumes of paper records.

2. Interoperability and Integrations

To maximize value, extracted data must flow seamlessly across incumbent systems like:

• ERP
• CRM
• Accounts Payable/Receivable
• Contract Management
• HCM

Evaluate turnkey connectors or custom APIs preventing data silos.

3. Configurable Enrichment

Invoke custom rules tailoring and enriching extractions like:

• Standardizing customer name formats
• Attaching external demographic attributes
• Flagging outlier values for further review

Tools that connect external data services provide further contextualization.

4. Cloud-Native Scalability

As data mushrooms, ensure your platform leverages cloud economics benefiting from:

• Limitless storage and computing
• Savings from avoided hardware costs
• Built-in business continuity and security

Future-proof your architecture seeking solutions optimized for cloud execution.

Specialized Use Cases and Techniques

In addition to typical back-office use cases, purpose-built data extraction brings value across roles:

Extracting Transactional Data from Complex Documents

Whether bank statement PDFs or EDI order confirmations, transactional documents provide vital signals for operational and analytical systems. Look for tools with pre-built templates and validations tailored to your transaction sources and data schemas. Apply machine learning for additional customization minimizing manual configuration.

Informing Predictive Analytics and Demand Forecasting

Extracted sales order transaction details better forecast customer demand. Seek tools integrating directly with planning and analytics systems like Anaplan or Tableau. Analyze trends in extracted order values, frequencies, product mixes, geographic source and more for superior demand predictions.

Streamlining Accounts Payable and Receivable

Invoicing automation promises massive efficiency gains but requires reliable data extraction uplifting unstructured invoice scans. Encoding rules for vendor, date, amount and line-item details accelerates invoice handling while preventing fraud.

Look for purpose-built invoice extraction tools interoperating with leading AP/AR platforms. Configure validations like checking totals match line sums or flagging duplicates.

Emerging Innovations

While core capabilities provide table stakes, also examine cutting-edge solution innovations like:

Smarter AI and Machine Learning

Advanced neural networks parse complex document contexts and data structures better than humans can. Continual learning algorithms mean systems self-improve perpetually.

Prioritize modern solutions leveraging state-of-the-art AI to reduce manual training and configuration.

Natural Language Processing (NLP)

NLP algorithms understand text semantics, sentiment, and significance rather than just keywords. This empowers richer contextual extraction even from free-form sources like customer remarks fields.

Performance Analytics

Data extraction efficiencies provide key process metrics around throughput, uptime, accuracy and more.

Platforms with built-in analytics dashboards help benchmark extraction productivity and unlock continuous optimization opportunities.

How to Evaluate Data Extraction Solutions

Conduct exhaustive due diligence assessing provider options across dimensions like:

Total Cost of Ownership

Factor both direct licensing expenses and indirect ongoing costs like maintenance, IT support and training. Cloud solutions reduce ancillary fees but verify optimal sizing for long-term TCO.

Vendor Credibility and Stability

Examine factors like years in business, client roster marquee brands, and extraction breadth covering diverse back and front-office document use cases.

Implementation Timeframes

The accelerated pace of business necessitates rapid rollouts. Seek platforms with turnkey templates, connectors and cloud access accelerating deployment velocity.

Compliance and Data Security

Heavily regulated sectors often demand advanced data security, access controls and audit logging. Multi-tenant cloud options now deliver best-practice protocols meeting stringent compliance rulesets.

Global Capabilities

For multinational organizations, verify consistent performance, localization and responsive in-region vendor support across all your operational geographies.

Cutting across data platforms, analytics and business processes, data extraction conveys tremendous value creation opportunities while future-proofing data pipelines. Choose solutions positioning your organization for analytics leadership.

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Have questions on data extraction? Reach out to discuss requirements for your unique operational data challenges.

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