Welcome to our knowledge base, where you can find information about a wide range of topics within the information management field. If you are looking for something specific, but you can’t find it here? Feel free to contact us!
In this section, you will find the most encountered questions regarding content services.
Contract data management is the digital management of contracts across all functions, organizations, and systems. It effectively empowers in-house legal teams to automate their contract management processes and continually extract business intelligence from their contracts, even when they are archived. Ultimately, it supports an organization with clear, actionable insights that generally lead to better decision-making.
A contract intelligence platform is a new approach to Contract Lifecycle Management (CLM) that provides organizations with the ability to dynamically analyze contracts in the context of the systems and processes that drive the business forward.
Contract Intelligence is the use of Artifical Intelligence (AI) tools in contract analysis. The power of AI tools is used to extract valuable business assets from static contracts and can protect all stakeholders from legal issues and contract breach risks, and enhance productivity.
First of all, there is no definitive answer to this question — as this largely depends on your specific situation and needs. Digital signatures are typically used when you want to guarantee a level of security and ease of use for your transactions — for example, for legal, healthcare, or HR documents.
Digital signatures are used to meet three important goals of information security: integrity, authentication, and non-repudiation. As such, digital signatures help enforce security during data transfers.
Yes, there is. A digital signature is a more secure electronic signature that is generated using a digital certificate and cryptographically bound to the document using public key infrastructure (PKI). The digital certificate is unique to the signer and obtained from a trusted 3rd party such as a trust service provider (TSP) or certificate authority (CA) after verification of the signer’s identity.
- Availability: modern document management systems ensure that users have access to business content from anywhere, from any device, and at any time.
- Search: a DMS helps in storing documents and emails in an organized way so that they can be easily retrieved by a user when needed.
- Single source of truth: no more duplicates of documents that roam in various systems, as a DMS provides a central repository to store all business content in.
- Workflow automation: automate document-related processes to eliminate repetitive, manual actions to save resources and increase employee satisfaction.
- Security: ensure that only persons who should have access to certain content, actually have access to minimize the risk of a data breach. Typically, all actions of users are logged to provide a full audit trail at any time. Furthermore, share content with external parties as clients or partners easily and securely.
- Knowledge management: unlock the value that resides in business content to extract, preserve and repurpose knowledge. This particular development has been gaining a lot of ground lately, as knowledge management can be seen as a critical tool for any organization that wants to increase its bottom line and market share.
A document management system is used to automatically organize, secure, digitize and classify business content as documents and emails, making them easy to access, edit and share. Therefore, a DMS supports an organization and its teams to not only streamline document-related processes – but also provide information governance to comply with data privacy and security laws and legislations.
Document management is the use of a computer system and software to store, manage and track electronic documents and electronic images of paper-based information captured through the use of a document scanner. The system and software to do so are often referred to as Document Management Systems (DMS).
There are many reasons to implement a Knowledge Management System in an organization, but the main goal is usually to help people access knowledge and use it to perform tasks better.
To gain a deeper understanding of knowledge work in law firms, iManage recently commissioned Metia Group to conduct in-depth research, with close to 1100 global respondents from across the legal industry to gain a deeper understanding of knowledge work and discovered that:
- 68% of survey respondents said the information in digital documents and files is the most important thing to their business.
- 28% of survey respondents said that most or all of their documents are scattered and siloed across multiple systems.
- 30% of respondents said that documents reach their organisation via five or more channels.
The above mentioned statistics show that knowledge is one of the most important assets of an organization. Yet knowledge is too often undocumented, difficult to access and has the risk of disappearing:
- Because teams use different apps within the same company, this results in isolated and fragmented information that is difficult to find when customers or agents need it most. That’s why knowledge workers spend 30% of their time looking for or recreating information that already exists. When knowledge isn’t shared and accessible, employees waste time reimagining solutions, make mistakes people have made before, don’t gain the insights they need to be productive, and answer the same questions over and over.
- Employees retire or quit a company, taking decades of company knowledge with them. And even if they train their replacement before leaving, departing employees can never pass on everything they know because some knowledge is tacit. Having a process helps capture that tacit knowledge that might otherwise disappear.
- Knowledge stored in emails or local drives can disappear due to system failures or due to loss or theft of devices.
All of these situations are unavoidable which highlights the need for a good knowledge management system even more.
Increasing digitization makes it possible to store and share knowledge easily and inexpensively. In other words, employees can access information at any time and from anywhere and also develop it further and integrate it into their everyday work. Rather, employees no longer have to ask their colleagues for this knowledge.
However, employees have to clearly categorize their information and tag them with keywords to enable other colleagues to quickly find answers using a keyword search on selected topics. As soon as a piece of knowledge – for example, software code or a manual – has been developed, it can be used (or improved) again and again, which saves work and thus costs. There is less (unnecessary) communication. You can learn from mistakes or hurdles that have already occurred and nobody misses a development because it is “lying around” somewhere.
In the best case, professional knowledge management creates the breeding ground for innovations or deeper knowledge with which tangible company-related problems can be solved. Employees can make better, faster and more data-based decisions.
In short, organizations can improve their operating results if they manage their knowledge intelligently They succeed in increasing the productivity and competences of employees. Last but not least: your customers are happier!
Every organization has its own structure and knowledge. Therefore every implementation is different. Our Knowledge Management experts are more than happy to discuss what works best for your organization, without obligation. Talk to an expert!
Knowledge management is all about capturing, sharing, using and managing tangible and intangible knowledge in an organization. The purpose of managing knowledge is to facilitate so organizations can work as efficiently as possible towards their goals.
It is a way to perform the tasks as good and efficiently as possible. If you are considering introducing knowledge management into your organization, keep in mind that this is not a responsibility of one department, for example, HR or IT. In fact, it should be widely supported throughout the organization. Managing knowledge is a journey, not a destiny.
Privacy & Data Protection
In this section, you will find the most encountered questions regarding privacy & data protection.
- Data Access Management
- Data Classification
- Rentention & Records Management
- Threat Detection & Monitoring
The basic principles of records management are:
- Reasonable cost
In essence, these principles dictate what a ‘good’ records management system is, and represent a structure to be sought after.
A record contains information that is made, produced, executed, or received in connection with transactional business activities, and supports an organization in conducting its business overall. Records are hard evidence of an organization’s unique policies, procedures, and decisions, and often hold significant administrative, historical, and legal value.
Generally speaking, there are two types of records management systems: traditional paper-based record management systems, and electronic record management systems. As the name might imply, traditional paper record management systems involve the management and storage of hard-copy documents.
Due to the clear benefits of electronic records management, a growing amount of organizations are adopting this method to manage the lifecycle of their records. In most cases, this is caused by the desire to reduce or even eliminate paper-based processes.
Records Management ensures that institutional records of vital historical, fiscal, and legal value are identified and preserved, and that non-essential records are discarded in a timely manner according to established guidelines and identified legislation, for instance, the GDPR.
- Penetration testing: By thinking the way a cybercriminal would, organizations should regularly scan their IT environments for vulnerabilities, such as unpatched software, authentication errors, and more.
- Automated monitoring systems: Alongside manual processes, organizations should enhance their cybersecurity by integrating automated threat detection systems. These platforms can help organizations by tracking device performance and activity, monitoring web traffic, and notifying the cybersecurity team when irregularities are detected.
- User behavior analytics: By analyzing user behavior, an organization can better understand what normal behavior for an employee would look like. This includes the kind of data they access, the time of day during which they log on, and their physical location. That way, any outlying behavior will stand out as unusual, and it will be easier for a security analyst to know what behavior to investigate.
Threat intelligence benefits organizations of all shapes and sizes by helping process threat data to understand their attackers better, respond faster to incidents, and proactively get ahead of a threat actor’s next move. For almost every organization, this data is crucial to achieve a level of protection that would otherwise be out of reach.
Threat detection describes the ability of organizations to quickly and accurately identify threats to the network, applications, or other assets within the network. The first step to an effective threat detection and response process is understanding what threats are present in the current environment.
- Open: This principle states that a user shall have access to all the information in a system if the user has access to the system or repository in which the information is being held. This type of data access is by far the least safe, as (in most cases) it’s not relevant for the user to have access to all the data.
- Open, unless: This principle states that a user shall have access to all the information in a system unless there are certain settings to shield specific data. As an example, it could be that HR or finance-related data only can be accessed by the HR or finance teams. This type of data access is safer than the previous, however, users could still have access to data that they don’t specifically need to do their day-to-day activities.
- Need-to-know: This principle states that a user shall only have access to the information that their job function requires, regardless of their security clearance level or other approvals. In other words: a user needs permissions AND a need-to-know in order to gain access to specific data which makes this type of data access the most secure – as it limits the potential impact of a data breach, if one may occur.
Access control protects data by ensuring that only authorized entities can retrieve data from an organization’s data repositories. When effectively implemented, access controls prevent unauthorized and compromised users from accessing sensitive data. Nowadays, proper data access management is typically expected from organizations by either clients or partners, but also due to a growing number of laws and regulations in this area.
Data Access Management is a set of processes and technologies used to control access to applications or data. It involves the creation of groups or roles with defined access privileges and then controlling access by defining group memberships.
Standard data classification include the following categories:
- Public information. Data in this category is typically maintained by state institutions and subject to disclosure of public data as part of certain laws.
- Confidential information. This data may have legal restrictions about the way it is handled, or there may be other consequences around the way confidential data is handled.
- Sensitive information. This data is any information stored or handled by state or other institutions that have authorization requirements and other rules around its use.
- Personal information. Generally, personal information or PII is protected by law and must be handled following certain protocols. Sometimes there are gaps between the moral requirements and contemporary legislative protections for their use.
Systematic classification of data helps organizations manipulate, track and analyze individual pieces of data. Data professionals typically have a specific goal when categorizing data. The goal affects the approach they take and the classification levels they use.
Some common business objectives for these projects include the following:
- Confidentiality. A classification system safeguards highly sensitive data, such as customers’ personally identifiable information (PII), including credit card numbers, Social Security numbers, and other vulnerable data types. Establishing a classification system helps an organization focus on confidentiality and security policy requirements, such as user permissions and encryption.
- Data integrity. A system that focuses on data integrity will require more storage, user permissions, and proper channels of access.
- Data availability. Addressing and ensuring information security and integrity makes it easier to know what data can be shared with specific users.
Data classification is the process of organizing data into categories that make it easy to retrieve, sort, and store for future use. A well-planned data classification system makes essential data easy to find and retrieve. Generally, this is of particular importance for risk management, legal discovery, and regulatory compliance.
Intelligent Process Automation
In this section, you will find the most encountered questions regarding intelligent process automation.
When picking a data extraction solution for your business, you should take into account that there are a lot of solutions available – each with their own set of features and capabilities. Therefore, it’s smart to have a good overview of the needs and requirements for your specific situation so you are confident that data extraction solution you choose, delivers in regard to what your business needs. We recommended to have the following parameters in mind when making a purchasing decision:
1. Intelligent data capturing
The data extraction tool must be able to extract data without losing information from different document types such as contracts, delivery notes, accounts payable, and more, and be able to categorize them in their respective blueprints.
2. Accuracy in results
Companies prefer a data extraction tool that delivers swift results; however, it must also be high in terms of accuracy. The extracted output must retain information, and the tool must be able to extract tables, fonts, and crucial parameters without compromising the layout.
3. Storage options
Pick a data extraction platform that offers secure storage along with seamless backup options. Cloud-based extraction enables you to extract data from websites seamlessly at any time.
Cloud servers can swiftly extract data relative to a single computer. The quickness of automated web data extraction affects the speed of your reaction to any rapid events that impact your enterprise.
4. Simplistic UI and robust features
Advanced automated data extraction software must operate on a simplistic UI. The layout of the software interface at launch must be simple enough to navigate you through executing a grinding task. Besides providing an easy-to-use UI experience, the platform must also not compromise on the essential features.
Pricing might not be the most crucial factor, but it is a thoughtful consideration. It might not be a wise decision to invest in exorbitantly expensive software with extravagant features that do not apply to your company or choose the wrong pricing plan. Consider evaluating the features of the software while ensuring that the cost stays within your budget.
As you can imagine, there are a lot of use cases in which automated data extraction can be beneficial. The word automated is key here, as already is proven that software can extract data in a better way than humans possibly ever can. Faster, more accurate and capable of providing the data immediately to one or more systems.
Back to the use cases, here are some examples of how automated data extraction is being used already;
- Legal professionals extract clauses from contracts for review and or knowledge management purposes.
- Accounts payable extract data from invoices, to subsequently check and approve invoices.
- Logistics service providers extract and analyze heaps of data from invoices, bills of ladings, as well as other documents, and manually feed in updates to the TMS or ERP.
Automated data extraction is the process of transforming unstructured or semi-structured data into structured information, in an automated way. Structured data provides organizations and their teams with meaningful insights to be available for reporting, analytics, and other business activities.
As with any automation, the main benefits of service automation are convenience, time savings, predictability, and expedience — which results in cost savings. Service automation is also important for facilitation as workplaces become more complex and dynamic.
It’s impossible for humans to track every agile variable in real-time. Automating the ways employees interact with their workplace and how the workplace responds buffers potential friction. While task automation is always useful, even more useful is the ability to automate a service in a similar way.
In its very essence, service automation is the delivery of a service, but then in a completely automated manner. That means that you, as a user of that service, can decide when you want to use a specific service. It also means that you make all the arrangements to use that service through some sort of app or portal (i.e. a self-service solution). If the service is adequately designed, it means that you don’t need to speak to anyone from the service provider.
Which tools you should use to automate your business will depend on the specific processes that most need it. By mapping out your needs in advance, and by identifying the key areas of your business that are ripe for automation, you will be able to select the best solution for the specific problem.
When looking for areas to automate, try to identify the ones in which employees are:
- Managing a high volume of r