In this blog series our guest contributor Michael Simon will share his perspectives on the challenges faced by organizations in surfacing business critical entities, attributes and terms within contracts as well suggest solutions that will streamline your obligations analysis and portfolio risk management processes.
Contracts are the engine of a business. They contain critical business intelligence needed to run the enterprise. Yet, enterprises continue to struggle to do something that seems simple: connect contracts to business value. Research by the International Association for Contract and Commercial Management found that inefficient contract life cycle management processes may cost organizations as much as 9% of their annual turnover.
While poor contract quality and negotiation cycle times contribute to cost inefficiencies an increasingly burdensome area is obligations and compliance analysis due to the prevalence of more onerous legislative and regulatory mandates.
For example, the Financial Accounting Standards Board (FASB) ASC 606 Regulation requires all companies, public and private to comply with new revenue recognition rules, based on the actual transfer of goods sold and services consumed and then recognize revenue proportionate to what was actually delivered and consumed. This change in revenue recognition rules requires companies to re-evaluate their performance obligations.
Then, there is the impact of the EU General Data Protection Regulation. It mandates all companies to meet rigorous requirements to safeguard privacy rights of EU residents, institute technical and organizational measures to protect the confidentiality, integrity and accessibility of personal information and implement appropriate breach response plans to mitigate privacy risks. Failure to do so may result in substantial fines and legal remedies that may also impact corporate reputation. Not surprisingly, a recent survey by Ernst & Young found that the world’s 500 largest corporations are forecasted to spend as much as $7.8 billion on GDPR related compliance activities.
First, let’s define content intelligence: : “Content intelligence is the systems and software that turn data into actionable insights.” Nearly 80% of organizational knowledge assets are unstructured. Content Analytics technologies are designed to help organizations surface business critical information contained in unstructured or semi structured content.
Now, let’s look at some of the challenges specific to content analytics. It probably goes without saying, but I’ll say it again just in case somebody reading this blog hasn’t read it a million times already, that contracts contain mission and business critical obligations and opportunities for organizations. A typical Fortune 1000 company manages anywhere between 20,000 to 40,00 active contracts at any given time, 10% of which are misplaced, difficult to find, still in paper form or on a file share somewhere, or buried in an email attachment not managed . Organizations continue to struggle to do something that, in the abstract, seems simple: connect business documents to business value, to mission-critical processes that drive revenue or mitigate risk. organizations continue to experience significant challenges in extracting value from their contracts. Per the Harvard Business Review earlier this year:
“Contracting is a common activity, but it is one that few companies do efficiently or effectively. In fact, it has been estimated that inefficient contracting causes firms to lose between 5% to 40% of value on a given deal.”
It’s time for organizations to get better at this. Fast. Because the need to put this business-critical information to use gets more and more pressing every day. The regulatory environment world-wide grows more onerous – and interconnected – every day. Consider just two examples:
- Not only does this far-reaching regulation have potentially powerful impact as it considerably strengthens privacy rights, while imposing more onerous obligations on data processors to safeguard personal information with significant penalties and enforcement mechanisms in the event on non-compliance. The broad extra territorial jurisdiction of GDPR means that US companies doing business with EU data subjects are required to adhere to the provisions of GDPR. It means an obligation to review all existing data processing agreements with 3rd party processors, identify possible gaps and take appropriate remediation steps to ensure adherence to GDPR. For additional insights to GDPR compliance I encourage you to check out this link where my colleague Andrew Pery an ABBYY consultant provides specific insights to GDPR related best practices and the application of privacy enhancing technologies.
- Accounting Standards Codification ASC 606 is a revenue recognition standard that affects all businesses that enter into contracts with customers to transfer goods or services. Compliance with this standard requires organizations to review all existing supplier agreements, ascertain performance obligations inherent in those agreements, determine the transaction price associated with goods and services and the amount that may be recognized as consideration for goods and services delivered. According to Deloitte, “the new standards significantly increase the amount of information companies are required to disclose about their revenue activities: It’s as if your teacher isn’t just demanding that you show your work, but also that you write an in-depth essay explaining the approach you chose, why you chose it, what assumptions you made, what tools you used, and what processes you followed to ensure nothing would go wrong.”
There have been significant advancements in machine learning technologies, commonly referred to as robotics process automation, which are designed to drive out costs of labor-intensive and error-prone business processes.
Machine learning technologies, such as intelligent capture and classification, enables organizations to identify patterns in data collected, organize, preserve and protect data in a highly efficient and accurate manner. As a result, organizations are empowered to mitigate risk, as well as to leverage information as a strategic corporate asset.
Yet, it is troubling that only 20% of organizations surveyed employ effective meta-data and classification of their data. With the proliferation of incoming information from multiple channels, it is imperative for organizations to invest in capabilities to capture information at the source, digitize it as soon as it enters the organization and transform that information into actionable business processes. Document understanding of incoming information based on content and metadata is key to automating the document capture and classification process. Thus, organizations can reduce error-prone and labor-intensive tasks associated with the capture, extraction and classification of large volumes of information and accelerate the process of compliance with data classification, retention and compliance policies and regulations.
Of course, obtaining all of this information sounds fantastic – a goal that never before seemed reachable for many enterprises – but it’s all to no good unless there is a way to organize it. Otherwise, all of this information quickly becomes overwhelming, paralyzing decision-making instead of enhancing it.
So, how can organizations best organize their data to prevent information overload? Fortunately, contracts provide a number of inherent properties to assist with organization, which may be one of the reasons that so many organizations are starting their content capture strategy with contracts. Most contracts are divided into segments, with headings, often major and minor (the X.0 and X.X levels) headings to better organize them. Contract clauses can be compared against other contracts, or against standard and best practices. Finally, content classification using extracted entities and metadata helps to organize the universe of contracts – as well as other content – into a coherent overall structure.
But organizations cannot be satisfied with just organizing their information, they need to take the next step and action that information through the capture of the final, highest level of content: meaning. Meaning in content can come from the subject matter within sentences and paragraphs, from the objects being acted upon and of course from the overall context.
Being able to derive at least some semblance of meaning – especially since even the best AI is still a ways off from matching human beings at this – opens up a number of potential use cases.
I’ll provide a deeper look into these advanced use cases in my next blog. But first, I want to make sure that we all have a sufficient understanding that the first step in harnessing the value of your contracts is digitizing your contracts, converting them into machine encoded text, extraction of key contract meta-data and entities contract and classification of contracts based on their subject matter.
It may be surprising that the availability of automated processes notwithstanding, a significant majority of organizations do not have visibility into where their contracts are located – be it on shared drives, email servers, content management repositories and even file cabinets. Moreover, contracts often enter an organization through multiple channels – email with PDF attachments, fax and electronic files. Document capture technologies, such as full text OCR digitize documents, extract relevant contract metadata and map them into transactional applications such as Contract Life Cycle Management (CLM) systems and to Enterprise Content Management repositories delivering efficiency gains as well as empower organizations to improve the management of contracts.
For further information link to: https://www.abbyy.com/en-ca/solutions/text-analytics-for-contracts/
About the author:
Michael Simon is the Principal of Seventh Samurai, an e-Discovery and Information Governance expert consulting firm. As a trial attorney in Chicago, he was an early innovator in using electronic evidence to win cases for his clients. He is an adjunct professor at Michigan State University College of Law (and formerly at Boston University School of Law), teaching classes in e-Discovery. He has advised a number of companies and government agencies on how to best mitigate the risks arising from their information while best optimizing value, and provides strategic consulting for companies in the analytics, security, privacy, and legal technology markets.
Michael is a legal technology thought leader, having made over 100 presentations and written dozens of articles on e-Discovery and legal technology topics, including a book on Internet Law in 2002. He received his J.D. from Loyola University Chicago School of Law, and his B.A. cum laude from Tufts University.