Role-based access control (RBAC) and attribute-based access control (ABAC) access right management software tools restrict or limit employees’ access to Information Technology (IT) resources and services. Access right management software helps businesses approve or deny access to IT resources or services based on employee-assigned duties, which also helps prevent potential internal threats. Before comparing RBAC vs. ABAC, it’s important to fully understand the benefits and drawbacks of each.

What is role-based access control?

Role-based access control limits or restricts employees’ access to specific IT resources and services based on the roles and responsibilities of an employee. Roles can be based on job responsibilities, specific tasks, and required job skills. Depending on the user role, an employee can have limited network access or restricted access to sensitive information on a cloud or premises server. 

Using RBAC helps companies manage employee access to IT resources and services based on a subset of tasks assigned to a role.

ALSO READ: Why Role-based Access is Fundamental for Enterprise Project Management

How does a role work in an RBAC?

A database administrator will be authorized to create, modify, and delete tables in a database, so the database administrator will have these permissions enabled. The database administrator can also change a field name from LName to LastName, which a regular user cannot. 

A typical database user will be authorized to view and add or modify the contents of a database table field value within a record. Still, the user cannot modify the structure of a database table because those permissions are restricted. For example, users can change the contents of the LastName value of a record from Smith to Jones, but the user cannot change the field name.

What are the types of roles in an RBAC?

Employees should only be assigned roles that allow them to do their jobs. 

System administrators use four levels of role-based control that provide an employee with a specific subset of permissions defined in a role. For example, a Human Resource (HR) employee will be assigned to a compensation and benefits role but not assigned to the HR compliance role. However, a person in HR compliance can be assigned to the compensation and benefits and workplace safety roles to ensure the HR office is compliant in these two areas; employees can be assigned multiple roles. 

Here are the four levels of roles organizations can use:

• Flat RBAC – Before users can access any IT services or resources, they must be assigned to a role with associated permissions.   
• Hierarchical RBAC – The hierarchical role is set up so management personnel can inherit the same permissions as their employees.
• Constrained RBAC – Purposely creating several roles to complete one task, so no one person can complete a task. 
  • This separation of duties prevents nefarious activities by separating roles so no person can complete a single task. 
• Symmetric RBAC – This occurs when an administrator routinely reviews permission settings for each role with the intent of moving permissions from one user to another user if required.

What is attribute-based access control?

Attribute-based access control focuses on the characteristics of the user, the requested access, the requested resource, and the conditions or environment of the request. The user, the request, the resource, and the environment in which the request is submitted are all attributes.

How does an attribute work in an ABAC?

When using ABAC, decisions are made based on four attributes. The attributes are subjects, resources, actions, and the environment. For example, when a user submits a request to perform an action, the ABAC software tool will approve or deny it based on the user, the activity, the established policies, and the environment or conditions.

What are the types of attributes in an ABAC

There are four attributes: 

• Subjects – Unique employee information. 
• Resources – IT resource being accessed.
• Actions – The action the user will perform on the requested IT resource.
• Environmental – the time, place, or established policies will determine if access is granted or denied. This can be a policy, time window, specific device, or location.

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What are the key differences between RBAC vs ABAC?

Though RBAC and ABAC are effective access rights management tools, they grant access rights differently. Besides the noticeable difference between RBAC using roles to grant access to resources and ABAC using attributes, the most significant difference is that ABAC can dynamically assign permissions based on the four attributes. 

ABAC uses eXtensible Access Markup Language (XACML) to assign access control rules. ABAC can also use conditional IF/THEN statements that decide a user’s access rights based on the value of an attribute. Another difference is ABAC requires an administrator with expert-level knowledge in setting up rules because it provides a more granular level of control that is more specific than just roles that RBAC provides.

What are the pros and cons of RBAC vs ABAC?

IT managers need to understand their organizations when evaluating the pros and cons of these access right management tools. Whatever tool an IT manager selects, the pros must have a decided advantage over the cons.

RBAC vs. ABAC pros and cons

Overall, RBAC is easier to set up and use. 

RBAC pros:

• Out-the-box implementation time is much quicker. 
• Best used in small to midsize business environments that have a simple organizational structure.

RBAC cons:

• Role expansion can quickly get out of control.
• Permissions can be assigned to user roles only and not objects or actions.
• No ability to restrict access to a sensitive data file on a server or in a folder.

ABAC pros:

• Offers more granular restrictions by using attributes such as time of the day, location, and file access rights.
• Policies that establish rules using conditional statements that can dynamically make decisions based on an employee’s permissions. 

ABAC cons:

• Setting up rules to take advantage of the four attributes can be complex and time-consuming.
• Prior experience and expertise are required to set up an ABAC deployment; if not done correctly, it can be difficult to undo and time-consuming.

The five best practices for using access right management tools

To get the most out of using access right management tools, IT managers need to consider implementing best practice concepts. Companies will significantly improve their internal cybersecurity posture when using access right management tools with the best practices. The five best practices when using access right management tools are the following:

• The principle of least privilege – provides the minimum access needed to allow employees to do their job.
• Multi-factor authentication – verifies the employee’s identification twice by having them log in initially with a username and password followed by a six-digit code sent to their work email or a biometric verification like a fingerprint.
• Removing obsolete accounts – having an effective offboarding process that revokes access to IT resources immediately after an employee is no longer employed.
• Identify vulnerable high-risk IT systems – Identifying and removing any IT system that cannot be adequately protected by an access right management tool.
• Zero-Trust security – forces all employees to authenticate their identities whenever they access an IT resource.

IT managers now have additional knowledge on how these access right management tools operate and what they can secure. Role-based access control is for small to midsize businesses that will not constantly add roles in the organization. An RBAC solution is recommended if a company has a limited budget, time, or IT staffing.

The RBAC vs. ABAC debate takes into account many factors. ABAC is the recommended solution for large organizations that grow annually with a diverse workforce and a large IT staff. An IT manager must select an ABAC solution if your business wants to grant or deny access to data files. SolarWinds Access Right Manager and other access right manager software tools for 2023 can be found in our IT Software Buyer’s Guide.

The post Role-Based Access Control (RBAC) vs. Attribute-Based Access Control (ABAC): What’s the Difference? appeared first on TechnologyAdvice.

• Structured data is stored in a schema, a plan of how the data will be stored and used by software that manipulates structured data.
• Unstructured data is not defined and stored in its native format. 
• Microsoft Word saves its file with a .doc extension, and an adobe file is saved with a .pdf extension, the native format for these software tools.

To get the most out of an organization’s data, Information Technology (IT) managers and executives need to understand the types of data used by an organization. IT managers that understand the types of structured vs. unstructured data available will help them make better decisions when aggregate data is used from both data categories.

What are structured and unstructured data types?

Structured data is created from a pre-defined format when a user has some idea of what data columns will be included in the structured data. Structured data is stored in a tabular form with specific columns that can be text, numeric, or date columns. Each of these columns can be formatted to accept data in particular formats. 

Relational Database Management Systems (RDBMS) such as Microsoft (MS) SQL Server or an Oracle Database are popular software tools for structured data for large organizations. SQLite or MySQL are RDBMS tools that small businesses can use. 

ALSO READ: How Businesses use Structured vs. Unstructured Data

Unstructured data types are word documents, emails, adobe PDF files, social media posts, and video or audio files. Any data that is not considered structured data can fall into the category of unstructured data, such as presentations, chats, sensor data, and satellite imagery. 

Unstructured data is stored in its native format. All unstructured data is saved in its native format by the software that created the file, and 80-90 percent of all business data is unstructured. 

Structured vs. unstructured data advantages and disadvantages

Regardless of the type of data businesses use, IT managers need to know the strength and weaknesses of the data types to exploit the advantages of structured and unstructured data.

The advantages and disadvantages of structured data

The advantage of structured data is it’s generated by a variety of business applications that are used daily in a business environment. Entry-level users can use basic software tools like MS Access, Excel, and more experienced users can use MS SQL Server or business intelligence (BI) tools to manipulate data. 

Structured data has a wider variety of RDBMS software and analytical tools available to support it since it has been around for decades. Artificial Intelligence (AI) tools can quickly generate queries due to how structured data is stored.

Structured data is not flexible and can only be used for its intended purpose, which  is a significant disadvantage. Another pain point of structured data is the complex alteration it must go through before a flexible data store can use it. As a business grows, the number of databases and tables proliferates, lending itself to overlapping datasets and redundant data with complex relationships between tables.

The advantages and disadvantages of unstructured data

Since unstructured data is in its native format, no processing is required before using it. As a result, unstructured data is flexible and can be used for different purposes. 

Another advantage of unstructured data is the low overhead associated with storing and processing the data. When appropriately used, unstructured data can provide better insight into a business’s overall progress that can become a competitive advantage.

A disadvantage of unstructured data is that it requires advanced analytics to derive meaningful information for a business. In addition, retrieving, processing, and analyzing unstructured data requires advanced tools and data science skills to generate insightful information.

Quick summary:  

• Structured data has been around for a long time and is easy to use.
• Getting relevant information from unstructured data requires an experienced data scientist familiar with the latest AI tools.

ALSO READ: 5 Best Data Storage Solutions for BI

Similarities and differences between structured and unstructured data

All data belongs to a business and can add value to a company using quantitative or qualitative data. Each data type can represent a comprehensive business overview from an employee, supplier, and customer perspective. 

Similarities between the two data types

The similarity between the structured vs. unstructured data is they belong to a business. The proprietary business data needs to be securely protected by the owning organization with the proper cybersecurity controls in place. 

Both data types can be used to improve the business through continuous process improvement (CPI) practices, making the data valuable to businesses.

Differences between the two data types

The main difference between structured and unstructured data is that structured data uses a defined format, and unstructured is saved in its native format. Structured data is quantitative and is used to show monetary gains and losses for organizations, which is numbers-based, countable, or measurable. 

Unstructured data is qualitative and generally descriptive or interpretation-based, so it can tell the why, how, and what happened in certain business situations.

Another difference between the two data types is that structured data is easier to search by a person or a created algorithm. However, to exploit and retrieve meaningful data from unstructured data, businesses will require a person with data scientist skills that can use advanced analytical techniques like data mining and data stacking. 

Additionally, structured data is stored in a data warehouse, while unstructured data is stored in a data lake, which has more storage capacity for all data types.

Quick summary:  

• Structured and unstructured data add value to a business but are used differently.
• Combining quantitative and qualitative data allows management to make decisions beyond raw Return on Investment (ROI) gains.
• A third category of data that may be stored in a data lake is called semi-structured data. Semi-structured data does not have a fixed schema but uses tags and business metadata to help define its semi-structured data. HTML code and XML documents are examples of semi-structured data.

Software tools used to manipulate structured vs. unstructured data

Relational database management tools have been around as long as structured data. Businesses can use analytical tools to manipulate and analyze structured and unstructured data in today’s environment. 

Artificial Intelligence and its associated cohorts under AI are Machine Learning (ML) and Natural Language Processing (NLP), which play a crucial role in extracting insightful data from structured and unstructured data. 

Software tools used for structured data

Any RDBMS software like Microsoft SQL Server can manipulate structured data. Zoho Analytics can connect to an organization’s structured and unstructured data and blend the two data types to provide meaningful information to executives and IT managers. Zoho Analytics also offers the following features:

• Dashboards – visual analysis information with drag-and-drop options.
• Zai AI – Zia intelligent assistant that uses ML and NLP to generate responses.
• Library of mathematical and statistical functions – uses a user-friendly formula engine to help extract business metrics.

Software tools used for unstructured data

Over eighty percent of business data is unstructured. Since unstructured data is stored in multiple formats, a business can use an Extract, Transform, and Load (ETL) software tool to extract structured and unstructured data onto a data lake platform. ETL software can also store data in a data warehouse, a centralized database, or an analytical database for faster queries. Hadoop is one of the many software tools that use ELT to transform and load large amounts of data to a designated repository for further analysis. 

Quick summary:  

• Voluminous amounts of structured, semi-structured, and unstructured data are known as big data. 
• Big data software tools such as Sisense have built-in ETL tools, and Google Cloud Platform uses its embedded set of big data tools to analyze data in data lakes or warehouses.

Characteristics of Structured vs. Unstructured Data

Selecting the best tools for structured and unstructured data

Emails, reports, and surveys are as important as structured data in an RDBMS system. The benefit of equating unstructured data as valuable as structured data is realizing the value of unstructured data, especially with it being eighty percent of all business data. With a better understanding of all data types in a business, executives and IT managers can formulate a plan to eliminate any data silos. When deciding on a data storage solution, management needs to consider these key features:

• Space and scalability – as a business identifies more unstructured data, additional space may be needed, so scalability is necessary.
• Accessibility – a data storage solution must be easily accessible and able to interface with any other analytical tools used in your business environment.
• Management – understand how the selected data storage vendor will manage your organization’s data and what self-service options are available for a user.
• Security – ensure the selected data storage vendor uses updated security features, including end-to-end encryption.

To help IT managers get started with research for a data storage solution, here are some of the best data storage vendors identified in 2022.

The post Structured vs Unstructured Data Comparison 2023 appeared first on TechnologyAdvice.

• The five stages of the adoption curve can be included as milestones or added on a critical path for the five types of employees.
• Including the adoption curve in a project management effort helps the project manager meet expected goals and projected Return on Investment (ROI).

When implementing new Information Technology (IT) software, businesses want to maximize the full potential of the implemented technology on a defined timeline. Personnel can be the biggest hindrance to implementing and using the new technology, and the adoption curve addresses this personnel issue.

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What is the Technology Adoption Curve?

The technology adoption curve uses the bell curve system to categorize five types of employees and how they react to adopting, accepting, and using new kinds of implemented technology in a business environment. The five types of employees are innovators, early adopters, early majority, late majority, and laggards. In addition, each employee type possesses specific characteristics about learning and risk-taking that place them on the left, middle, or right of the technology adoption curve. 

How does the Technology Adoption Curve help businesses implement IT changes

Technological progress regularly occurs, allowing businesses to produce their goods and services more efficiently. The technology adoption curve evaluates employees that have a role in using the new technology to ensure an employee’s non-willingness does not impede expected productivity gains to use the technology. In addition, the technology adoption curve helps an organization meet any predefined goals or objectives due to the new technology.

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What are the Five Stages of the Technology Adoption Curve

Each employee’s perspective on using new technology is what the adoption curve addresses, and getting an employee to a point where they see the benefits of using the latest technology. An employee understanding the advantage of using the new technology with some degree of motivation to use the new technology is the ultimate goal of the adoption curve.

1. Innovators

Employees who are innovators are ideal for promoting the use of new technology, and they are very motivated to learn. The percentage of innovators is less than three percent, and they are the best advocates for promoting new technology among their peers. 

Innovators are considered tech enthusiasts, and they like trying new technology. Innovators are likely to have a personal IT project going on at home. Innovators are the first to upgrade to the latest IT hardware standard or use the newest application. Innovators can tolerate risk, and failing does not intimidate them. Using new and exciting technology far outweighs the risk of failure for innovators. Innovator characteristics are the following:

• Enthusiasm for using new technology
• Willing to tolerate the risk of failure
• Not concerned with the possibility of failing

What motivates the innovators?

Innovators are excited about the opportunity of using the technology. Advertising what the technology can do also motivates the innovator. Kotter’s 8-step change model is an excellent resource to help keep the innovator motivated. 

2. Early Adopters

Early adopters are motivated by being the first to know about any new technology. Like the innovators, early adopters are generally younger and not afraid to take risks. Early adopters are considered visionaries. Fourteen percent of employees are considered early adopters, another group of employees that can be advocates for new technology.

The difference between early adopters and innovators is early adopters are concerned about their reputation. Early adopters are very thorough in collecting information from respected sources and documenting their personal experiences with the new technology. After early adopters have validated the latest technology as good, they will support and share the results with their peers. Early adopters’ characteristics are the following:

• Persuasive due to the positive experiences
• Willing participants to work through any issues
• Reputation is important for early adopters

What motivates the early adopters?

Providing the early adopters with how-to and user guide documentation will motivate them. In addition, early adopters are good candidates for doing any beta testing. Finally, once early adopters are comfortable with the new technology, they will help with the organizational change by sharing the results of the technology. 

3. Early Majority

The early majority employees are considered pragmatists. The Early majority will test the new technology and look for product reviews to read. The best way to convince this group is to demonstrate the latest technology and how it solves or improves a problem or a process.

Thirty-four percent of employees fall into the early majority group. Employees in this group tend to question the need for change and want to see evidence that the new technology is the best choice. Early majority characteristics are the following:

• Logical
• Practical
• Result-driven

What motivates the early majority?

The early majority is motivated by demonstrating how technology solves a problem. Therefore, this group wants statistical evidence that the new technology is the best solution.

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4. Late Majority

The Late majority group is similar in two ways with the early majority. First, they make up 34% of the employees who fall into this group, and they are logical. The late majority group will ask questions and quietly observe the implementation of the new technology before they get involved.

 Any technology trends do not influence the late majority personnel, and they are hesitant to take any risks. Personnel in this group will delay any software updates and seek peer input from personnel who have applied the latest software update. Late majority characteristics are the following:

• Wary of new technology
• Logical
• Are not risk takers

What motivates the late majority?

The late majority group must see the new technology successfully demonstrated before being motivated. The late majority employees need to see tangible benefits and how the new technology will help them. Once the late majority group sees the technology working, the innovators and early adopters can help answer any lingering questions this group may have. The Late majority personnel are motivated by results.

5. Laggards

Laggard employees comprise 16% of the technology adoption curve, and this group is on the far right of the bell curve. Laggards want everything to remain the same because they are familiar with the tools they are using. Laggards are the stubborn group and are easily frustrated with technology. Laggards want to know what they gain by using the new technology. Laggards’ characteristics are the following:

• Doubtful
• Resistant to change 
• Cautious of new technology

What motivates laggards?

Successful demonstration of how the new technology has helped other employees is the best way to motivate laggards. Laggards also need to see documented success from their peers to convince them to use the new technology. With laggards, focus on the benefits and how they are available once they start using the latest technology.

What is a Digital Adoption Platform?

A Digital Adoption Platform (DAP) is a software tool to help new users become familiar with a new software product. New software or a website is embedded within a DAP application, and the DAP provides guidance, critical tasks, and contextual information to expedite the learning process for the user. Digital application platforms are customizable and use AI technology that aids learning.

How does it help a business adopt new IT technology?

Digital application platforms help businesses in multiple ways. For example, the platform lowers user resistance to using new software, increases user satisfaction, provides better insight into what works and does not work, and enhances employee morale.

ALSO READ: Best Practices for CRM Implementation & Training

Managing the Technology Adoption Curve Lifecycle

The technology adoption curve lifecycle is essential enough to be a part of a project management effort for implementing new software. The five stages of the adoption curve can be included as milestones or added on a critical path for the five types of employees. Including the adoption curve in a project management effort helps the project manager meet expected goals and projected Return on Investment (ROI).

Looking for the latest in IT solutions? Check out our IT Software Buyer’s Guide.

FAQs

What is the Technology Adoption Curve? 

The technology adoption curve uses the bell curve system to categorize five types of employees and how they react to adopting, accepting, and using new kinds of implemented technology in a business environment.

What are the 5 stages of the Technology Adoption Curve?

Innovators, Early Adopters, Early Majority, Late Majority, Laggards.

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