Vantage 3.0
Introducing a hybrid approach to using Document AI and GenAI
Supercharge AI automation with the power of reliable, accurate OCR
Increase straight-through document processing with data-driven insights
Integrate reliable Document AI in your automation workflows with just a few lines of code
PROCESS UNDERSTANDING
PROCESS OPTIMIZATION
Purpose-built AI for limitless automation.
Kick-start your automation with pre-trained AI extraction models.
Meet our contributors, explore assets, and more.
BY INDUSTRY
BY BUSINESS PROCESS
BY TECHNOLOGY
Build
Integrate advanced text recognition capabilities into your applications and workflows via API.
AI-ready document data for context grounded GenAI output with RAG.
Explore purpose-built AI for Intelligent Automation.
Grow
Connect with peers and experienced OCR, IDP, and AI professionals.
A distinguished title awarded to developers who demonstrate exceptional expertise in ABBYY AI.
Explore
Insights
Implementation
September 27, 2018
Digital data pervades virtually every aspect of our lives. IDC estimates that by 2025 digital data will grow to 163 zettabytes, 80% of which will be created by businesses. From autonomous cars, robotic process automation, intelligent personal assistants to smart home devices, the world around us is undergoing a fundamental change, transforming the way we live, work, and play.
The confluence of big data, cloud computing, social media, mobile devices collect and aggregate diverse data sets, which taken together, such as internet search habits and GPS tracking information may expose personally identifiable information.
There is an even more vexing challenge — data analytics — powerful algorithms that cut through vast amounts of data. Predictive analytics is fundamentally changing the definition of data. It consists of not only consent based data collected from data subjects but also extends to observed data, for example, video data from surveillance sensors and inferred data, aggregated from diverse data sets that creates a digital fingerprint of data subject sentiments, preferences and behaviors. Increasing use of machine learning technologies is also generating vast amounts of data about individuals without their knowledge let alone affirmative consent, as required by GDPR.
The General Data Protection Regulation considerably strengthens the accountability principle which requires organizations to institute “appropriate technical and organizational measures” to safeguard privacy rights, maintain a record of processing activities and have in place adequate internal controls to demonstrate compliance if requested by supervisory authorities. Compliance with the accountability principle implies having better visibility to the data, how it is collected and processed and the steps taken to minimize the amount of personal information collected.
It is then not surprising that a recently published survey found that 64% of organizations are planning to overhaul their business processes given GDPRs onerous enforcement mechanisms, fines and penalties. However, 47% of the same survey participants do not have a clear understanding of how to prioritize their compliance initiatives.
A useful starting point is to consider a unified information governance strategy based on the over-arching principle that safeguarding privacy rights is not just about risk mitigation but also an opportunity to strengthen corporate brand and foster enduring customer loyalty.
A holistic information governance strategy demands cross functional participation from the business leadership. A potentially useful governance framework is the IGRM reference model. This model provides a framework for aligning the key business functions so that:
This article is an abridged version of Andrew Pery’s article on “GDPR and The Data Governance Imperative,” published in AIIM.org. To read the full-length version, please visit: https://info.aiim.org/digital-landfill/gdpr-and-the-data-governance-imperative.