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What is data automation? 

Data automation refers to optimizing data uploading and delivery procedures using automation tools that eliminate manual work. The traditional practice required manual labor from the IT department to manage and administer data updates on open data portals. Sometimes the responsibility would fall on the employees in different departments that had to handle the data while carrying on their other duties. The manual process is time-consuming and labor-intensive for enterprises. In addition, manual handling of data is error-prone and can affect strategic business insights derived from the data. Hence, data automation is a vital tool for any enterprise looking to upgrade its data integration to a more efficient level.

Do you need data automation in your business?

There are four clues to look for when deciding if you need to automate data automation in your business:

What are the approaches to data automation?

ETL is one of the most common data engineering approaches used by data professionals. According to the procedure, the data automation process includes three steps based on the function of the used tools. These three stages are commonly known by the abbreviation of ETL (Extract, Transform, Load). These stages include:

Roadmap to Data Automation 

Identify problems: Determine where the repetition occurs and prioritize the data sets based on their added value. It is significant to prioritize the datasets that create the most value for the company as they take more manual effort. 

Define data ownership within the organization: Determine which teams will handle different stages of the data automation process. There are three main approaches to data access and processing within an organization: 

With the centralized approach, the IT team handles the data automation process from A to Z. 

In a decentralized method, each agency processes their data, from extracting the data from source systems to loading them to data portals. 

There is also a combination of the two methods. The hybrid method allows different departments to work with the IT team. IT teams are responsible for loading the data into data portals through a hybrid approach.  

Define the required format for your data transformation: Define the required format for your data transformation. It is crucial to have a set data format policy, to secure data coherence for better insights. Moreover, ETL tools require users to define the preferred formatting of the data categorization.

Schedule updates: Dataset update allows businesses to make better decisions on their operations. Hence, It is crucial to schedule updates for consistent and up-to-date data for datasets. 

Determine the right vendors for your operations: Businesses can rely on automation consultants’ expertise to help them identify the best vendor according to the business needs and the business structure. 

Explore our ETL tools list if you believe your data integration tasks may benefit from automation.

To gain a more comprehensive overview of workload automation, download our whitepaper on the topic:

Contact us for guidance on the process: If you need more customized recommendations.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





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Data Collection Automation: Pros, Cons, & 3 Methods In 2023

Data collection has become a core function for many businesses. From gaining consumer insights to developing and improving on AI/ML models in the business, fresh data is regularly required. 

However, manual data collection can be challenging, especially when the use case is unique and complex.

Automating your data collection process can help bypass some of those challenges by streamlining the process. Prior to leveraging automation in your data pipelines, learning its pros and cons can be worthwhile before initiating any investments in automation tools.

To help you better understand data collection automation, this article explores:

What is data collection automation? 

What are its pros and cons?

What are the methods of automation data collection?

What is data collection automation?

Automated data collection involves harvesting data from multiple sources without any human intervention.

This is done with automation software powered by machine learning. The machine learning model is trained through an algorithm that extracts the required type of data from online sources. Usually, in data collection automation, various methods are used to automatically extract data from online websites. 

This data can be structured or unstructured. In the latter case, the unstructured data is collected and processed into structured data. This can also be automated by combining  RPA and OCR.

What are some data collection automation pros and cons?

This section highlights some pros and cons of automating data collection in your business:

Pros of data collection automation 1. Reduced human errors

To err is human. Manually collecting data can be tedious and error-prone, leading to: 

Mistyping of data,

Duplication of data,

Missing out data, etc.

It can be commonplace. Automation can eliminate such errors.  

2. Improved data quality

Reducing the aforementioned errors can have a significantly positive impact on the overall quality of the dataset. This will ultimately result in more accurate results in data-hungry projects, such as a higher-performing machine learning model. To learn more about data collection quality assurance, check our this quick read.

3. Saved time and maintenance costs

Gathering data is a time-consuming and labor-intensive task if done in-house, especially in use cases where the data required is diverse.

For instance, if you wish to implement a speech recognition model in China, assigning your workforce to record audio data in Mandarin Chinese can be a challenge. Automating this can save your team’s time and allow them to tend to higher-value tasks. 

Using data collection automation tools also reduces maintenance costs. This is because the data needs to be regularly updated. If this is done manually, the data collector will have to recruit new contributors to maintain the dataset, which will increase the costs. 

Using automation tools can save the time that is consumed in maintaining such datasets.

Cons of data collection automation  1. Quality issues

While automated data collection tools reduce human errors, they can also reduce the quality of the overall dataset. This is because raw data requires a quality screening process. For instance, when automated tools are gathering large-scale data without any human intervention, it can become difficult to screen the data for quality.

2. Costs of automating

While automation can be cost-effective in the long run, implementing automation tools can be expensive, and not everyone can afford them. Although the costs vary from the scale of the solutions that are being bought, Therefore, it is important to calculate ROI on such investments, and if the costs are unjustifiable, then other options should be considered. 


If quality screening and costs are important factors for your data-hungry project, then working with third-party data collection service providers can be beneficial. 

What are the three methods of data collection automation?

There are a few different methods of automating data collection, but the three most common are:

1. Data or web scraping

This method is used to extract data from sources that are not intended to be accessed or read by machines. Web scraping can be done manually but is often automated through the use of scraping bot that can mimic human interactions with a website or application.

2. Data or web crawling

Web crawling is a technique that involves automatically visiting websites and extracting data from them. This is different from web scraping because web crawlers will typically follow links from one page to another, while web scrapers will only extract data from the pages they are explicitly told to access.

See our guide on web crawling vs. web scraping to learn more about the differences between them.

3. Using APIs

Another common method is using APIs to extract data from online sources. Most data sources provide an API that can be used to access their data. This is the most direct way to collect data from a source and is usually the easiest to automate. That’s because data is typically collected in a structured format that can be easily parsed by a computer. 

For example, an API can be used to retrieve data from a remote database and then format it in a way that is usable by the local software program. This can save a lot of time and effort that would otherwise be required to manually collect and process the data. 

However, some data sources do not have an API, or the API is not well documented, making it difficult to use this method. 

You can also check our data-driven list of data collection/harvesting services to find the option that best suits your project needs.

For more in-depth knowledge on data collection, feel free to download our whitepaper:

Further reading

If you need help finding a vendor, or have any questions, feel free to contact us:

Shehmir Javaid

Shehmir Javaid is an industry analyst at AIMultiple. He has a background in logistics and supply chain management research and loves learning about innovative technology and sustainability. He completed his MSc in logistics and operations management from Cardiff University UK and Bachelor’s in international business administration From Cardiff Metropolitan University UK.





Data Management In ‘23: What It Is & How Can Ai Improve It?

According to Statista, the total data volume worldwide has been on an increasing trend since 2010 and is expected to keep rising until at least 2025 (see Figure 1).

With this increase in volume comes an increase in the need to determine a way to manage it. Subsequently, a wealth of data management techniques, tools, vendors, and careers, have emerged to support this need.

To better help your business achieve effective data management, it is necessary to first understand what exactly is data management and how it can become beneficial for your organization.

What is Data Management?

Data has a lifecycle that requires careful management from the day it is created until the day it is no longer in use. By managing this data properly, the risks are greatly decreased and the usability and quality of the data are greatly increased. Ultimately these two things together lead to a better and more profitable business no matter the industry or topic.

Some of the biggest focus points in data management include:

1. Data quality

Availability and usability of data for its desired purpose. Maintaining data quality is not a one-time effort, but instead requires regular ‘maintenance’ at logical times in the cycle.

2. Data access

Being able to access and retrieve data from its current location.

3. Data governance

Having data that is aligned with the greater goals of the business. Outlines the processes for determining data owners, their roles and responsibilities, and how they work with data users. Clarifies the role of compliance in data management. For example, due to regulatory limitations, some data can only be analyzed after anonymization and aggregation.

4. Data integration

Different steps and methods for combining different types of data.

5. Master data management (MDM)

Defining, unifying, and managing the data that is essential across an organization.

To learn more about master data management, feel free to read master data management: best practices & real-life examples.

There is a wide range of tools available to support all of the above and more for industries with data volumes that are both small and large. One example of this can be seen with ETL tools, which ‘extract’ incoming data from multiple sources, ‘transform’ it into the required format, and then ‘load’ it into its final destination, often a data warehouse.

To improve data management, businesses can leverage workload automation tools that automate the scheduling and execution of batch processes on different platforms from a single point. This enables better visibility and transparency, optimizes data storage strategies, creates an audit trail of all processes, and provides a single source of truth. Scroll down our data-driven list of workload automation tools to get a comprehensive view of the ecosystem.

Advantages of Effective Data Management

Aside from the intrinsic benefits associated with data management in terms of factors such as cleanliness and availability, there are a growing number of benefits that can be felt across the business.

Better and faster decision making owing to higher quality data and a single version of the truth

Easier achievement of compliance and governance standards

Long-term preservation of data for a longer historical perspective

More efficient sharing and access generally within a web-based or cloud environment

Synchronization of data

Minimized security and fraud risks

New lines of business

An improved customer buying experience

Ease in change management

Better use of internal resources in terms of employees and tangible goods

Improved visibility and transparency

Image source: Avaali

Integrating Data Management in Your Business

Understanding the benefits of data management is a great place to start as it will help you decide from the beginning with a clear mind what benefits you aim to achieve. There are a few additional steps that can be taken to help integrate better data management into your business.

Start by deciding if a more extensive data management process is really right for your business. The best way to do so is by determining whether or not you have a need, pain, or problem that could be solved with data. One such type of problem would be in data governance where there are disruptive forces that can lead to data problems and challenges in demonstrating compliance. Some additional problems that can be solved with data management include:

Information security

IT/systems modernization

Strategic enablement


After determining the problem that requires solving, it is necessary to build a small model to act as proof of concept behind your idea. This should be small, measurable, and controlled, and helps to prove the value of your solution.

Once you have determined a solution to your problem that needs to be solved, the next step will be to get the executive approval that is necessary in order to proceed. The most effective way to do this is to connect a ‘data’ problem to a ‘business’ problem in order to clearly demonstrate the value. Some of the most common challenges incurred include:

Data is seen as an IT issue alone

Overall organizational silos

Unclear ROI

Data management will often require an organizational change in terms of internal team members, plus any consultants or vendors that may play essential roles in not only the integration but ongoing support of processes. Some additional roles that will become involved with data management (and its later analysis) include:

Image source: IBM

By having all of these different tasks and roles in mind from the start, it can be easier to demonstrate the value of data management in addition to creating an effective solution.

The Impact on Data Science and AI

Data science is the field of collecting, modeling, and interpreting data in order to make predictions. Data scientists work with the data using different tools and formats to reach a certain conclusion, and understandably, the data they are working with is key to finding these conclusions. Subsequently, having a well-executed data management solution is key to their success.

AI has largely become recognized as one of the biggest changes that we are part of with the ‘digital transformation’. However, before organizations can get the most from the latest in AI technologies, they must first have an effective BI solution. This is because AI is wholly dependent on the quality of data that it receives, and without proper data management, the quality of BI and related data will make it that much more difficult for AI to do its ‘job’.

These two integral fields in technology and their place in the future demonstrates the need to begin making changes to data processes that will be sustainable for changes to come.

For more on data management

And if you believe your business would benefit from leveraging a data management platform, we have a data-driven list of vendors prepared.

To gain a more comprehensive overview of workload automation, download our whitepaper on the topic:

We will help you choose the best one for your enterprise:

Featured Image Source

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





Is It Worth To Invest In The Workforce Automation Application?

Despite the debate about machines and automation systems replacing humans in the next ten to fifteen years, machines can be used as tools for increased productivity and better business outcomes. They can help people in managing everyday tasks that are both mundane and time-consuming. Workforce automation application is capable of managing multiple tasks with efficiency and divert the human workforce where it is precisely needed.

The factors explaining the worth of a workforce automation application are given here:

Easy Employee Analytics

Many companies are implementing a workforce management application that can gather employee data to understand their true potential. Businesses have realized that employee analytics is more than just statistics. This results in efficient task management as the managers can assign tasks to the employees whom they see fit for the job. Employee data analytics also includes organizing and characterizing sophisticated employee data which is further utilized for a detailed employee performance analysis. HR managers and decision-makers can use this data for predictive analysis and evaluation of employee potential and tenure.

Many departments in a company including human resource management are turning to data collection and management to deal with turnover, overtime, and headcount-related aspects.

Related: – What is Manufacturing & Technology and how it’s use?

Hassle-Free Hiring Process

Sending interview invitations to candidates manually is a tedious task for HR managers and sorting and shortlisting the right candidates is even more tiresome. Here, a workforce automation application comes to rescue as it can single-handedly manage these tasks. It can send automated messages to the candidates and shortlist the chosen ones. This reduced the task of HR managers to a macro level where the experts only have to choose the candidates with the required skills and qualifications, and the rest can be left to the software Workforce automation application also uses anti-bias technology which maintains transparency in the process and keeps it impartial.

This technology only focuses on a candidate’s skill and qualification regardless of his/her background, college or university. For instance, a hiring manager may prefer certain candidates belonging to a university or college which can discourage other candidates and the company may also miss out on a deserving candidate just because he or she does not belong to a specific institution.

Easy Management of the Facility

Workforce management is not only for big companies and Multinational Corporations (MNCs). It can be readily employed in a relatively small workshop or a startup. For instance, the software can create, manage and maintain the technician’s shift patterns and gain visibility of their working hours.

This can be further utilized for analyzing their true activity hours and evaluating their productivity. This is achieved through virtual calendars that can track, maintain and manage employee data on a micro level. Therefore, a highly managed workplace results in high employee productivity, better utilization of resources and enhanced customer satisfaction.

In addition, it will automatically eliminate the need for a separate admin as the software itself can calculate leaves, working days, working hours, and compensation of employees and technicians. The manager only has to authenticate the outcome presented by the software and salaries of the employees as per their respective evaluation will be credited automatically.

Related: – What’s the Real Difference between AI and Automation?

Minimize Compliance Risks

Workforce automation platforms can help companies maintain records related to state and government bodies that are required to sustain legal compliance. Such compliance records are not easy to maintain as manual management may lead to errors that can be hazardous to a business.

Here, even the smallest errors can be hazardous to the health of a business as these can cost huge fines. Workforce automation software can maintain these documents and notify the management in case of any requirements. In addition, it is easy to keep track of the documents that are stored in one place.

However, managers may worry about the security of documents due to them being stored virtually. They do not have to worry as workforce automation platforms are equipped with high-tech security measures such as encryption and data tracking that require proper authorization before accessing crucial data and in the case of data theft, they can also track data.

Related: – Rise of Robots and use in Supply Chain Industry

Easy Customer Database Management and Support 

The primary function of workforce automation software is data collection and management. Therefore, it can help in customer database management and in facilitating excellent customer support. This is because decision-makers can integrate employee’s performance reviews to the company’s customer satisfaction index.

Smart workforce automation applications are not here to steal jobs. Instead, these have the potential to completely change the workplace management for good. The seamless integration of this software to the core systems is capable of facilitating the uninterrupted flow of processes.

Shagun bagra

Hey!!! My name is Shagun Bagra. My interest in researching and share information on the Digital Transformation and Technology.

What Is Data Integration & How Does It Work?

Data integration, which combines data from different sources, is essential in today’s data-driven economy because business competitiveness, customer satisfaction and operations depend on merging diverse data sets. As more organizations pursue digital transformation paths – using data integration tools – their ability to access and combine data becomes even more critical.

As data integration combines data from different inputs, it enables the user to drive more value from their data. This is central to Big Data work. Specifically, it provides a unified view across data sources and enables the analysis of combined data sets to unlock insights that were previously unavailable or not as economically feasible to obtain. Data integration is usually implemented in a data warehouse, cloud or hybrid environment where massive amounts of internal and perhaps external data reside.

In the case of mergers and acquisitions, data integration can result in the creation of a data warehouse that combines the information assets of the various entities so that those information assets can be leveraged more effectively.

Data integration platforms integrate enterprise data on-premises, in the cloud, or both. They provide users with a unified view of their data, which enables them to better understand their data assets. In addition, they may include various capabilities such as real-time, event-based and batch processing as well as support for legacy systems and Hadoop.

Although data integration platforms can vary in complexity and difficulty depending on the target audience, the general trend has been toward low-code and no-code tools that do not require specialized knowledge of query languages, programming languages, data management, data structure or data integration.

Importantly, these data integration platforms provide the ability to combine structured and unstructured data from internal data sources, as well as combine internal and external data sources. Structured data is data that’s stored in rows and columns in a relational database. Unstructured data is everything else, such as word processing documents, video, audio, graphics, etc.

In addition to enabling the combination of disparate data, some data integration platforms also enable users to cleanse data, monitor it, and transform it so the data is trustworthy and complies with data governance rules.

ETL platforms that extract data from a data source, transform it into a common format, and load it onto a target destination (may be part of a data integration solution or vice versa). Data integration and ETL tools can also be referred to synonymously.

Data catalogs that enable a common business language and facilitate the discovery, understanding and analysis of information.

Data governance tools that ensure the availability, usability, integrity and security of data.

Data cleansing tools that identify, correct, or remove incomplete, incorrect, inaccurate or irrelevant parts of the data.

Data replication tools capable of replicating data across SQL and NoSQL (relational and non-relational) databases for the purposes of improving transactional integrity and performance.

Data warehouses – centralized data repositories used for reporting and data analysis.

Data migration tools that transport data between computers, storage devices or formats.

Master data management tools that enable common data definitions and unified data management.

Metadata management tools that enable the establishment of policies and processes that ensure information can be accessed, analyzed, integrated, linked, maintained and shared across the organization.

Data connectors that import or export data or convert them to another format.

Data profiling tools for understanding data and its potential uses.

Data integration started in the 1980’s with discussions about “data exchange” between different applications. If a system could leverage the data in another system, then it would not be necessary to replicate the data in the other system. At the time, the cost of data storage was higher than it is today because everything had to be physically stored on-premises since cloud environments were not yet available.

Exchanging or integrating data between or among systems has been a difficult and expensive proposition traditionally since data formats, data types, and even the way data is organized varies from one system to another. “Point-to-point” integrations were the norm until middleware, data integration platforms, and APIs became fashionable. The latter solutions gained popularity over the former because point-to-point integrations are time-intensive, expensive, and don’t scale.

Meanwhile, data usage patterns have evolved from periodic reporting using historical data to predictive analytics. To facilitate more efficient use of data, new technologies and techniques have continued to emerge over time including:

Data warehouses. The general practice was to extract data from different data sources using ETL, transform the data into a common format and load it into a data warehouse. However, as the volume and variety of data continued to expand and the velocity of data generation and use accelerated, data warehouse limitations caused organizations to look for more cost-effective and scalable cloud solutions. While data warehouses are still in use, more organizations increasingly rely on cloud solutions.

Data mapping. The differences in data types and formats necessitated “data mapping,” which makes it easier to understand the relationships between data. For example, D. Smith and David Smith could be the same customer and the differences in references would be attributable to the applications fields in which the data was entered.

Semantic mapping. Another challenge has been “semantic mapping” in which a common reference such as “product” or “customer” holds different meaning in different systems. These differences necessitated ontologies that define schema terms and resolve the differences.

Data lakes. Meanwhile, the explosion of Big Data has resulted in the creation of data lakes that store vast amounts of raw data.

The explosion of enterprise data coupled with the availability of third-party data sets enables insights and predictions that were too difficult, time consuming, or practical to do before. For example, consider the following use cases:

Companies combine data from sales, marketing, finance, fulfillment, customer support and technical support – or some combination of those elements – to understand customer journeys.

Public attractions such as zoos combine weather data with historical attendance data to better predict staffing requirements on specific dates.

Hotels use weather data and data about major events (e.g., professional sports playoff games, championships, or rock concerts) to more precisely allocate resources and maximize profits through dynamic pricing.

Data integration theories are a subset of database theories. They are based on first-order logic, which is a collection of formal systems used in mathematics, philosophy, linguistics and computer science. Data integration theories indicate the difficulty and feasibility of data integration problems.

Data integration is necessary for business competitiveness. Still, particularly in established businesses, data remains locked in systems and difficult to access. To help liberate that data, more types of data integration products have become available. Liberating the data enables companies to better understand:

Their operations and how to improve operational efficiencies.

The competitors.

Their customers and how to improve customer satisfaction/reduce churn.


Merger and acquisition targets.

Their target markets and the relative attractiveness of new markets.

How well their products and services are performing and whether the mix of products and services should change.

Business opportunities.

Business risks.

More effective collaboration.

Faster access to combined data sets than traditional methods such as manual integrations.

More comprehensive visibility into and across data assets.

Data syncing to ensure the delivery of timely, accurate data.

Error reduction as opposed to manual integrations.

Higher data quality over time.

Data integration combines data but does not necessarily result in a data warehouse. It provides a unified view of the data; however, the data may reside in different places.

Data integration results in a data warehouse when the data from two or more entities is combined into a central repository.

While data integration tools and techniques have improved over time, organizations can nevertheless face several challenges which can include:

Data created and housed in different systems tends to be in different formats and organized differently.

Data may be missing. For example, internal data may have more detail than external data or data residing in a mainframe may lack time and data information about activities.

Historically, data and applications have been tightly-coupled. That model is changing. Specifically, the application and data layers are being decoupled to enable more flexible data use.

Data integration isn’t just an IT problem; it’s a business problem.

Data itself can be problematic if it’s biased, corrupted, unavailable, or unusable (including uses precluded by data governance).

The data is not available at all or for the specific purpose for which it will be used.

Data use restrictions – whether the data be used at all, or for the specific purpose.

Extraction rules may limit data availability.

Lack of a business purpose. Data integrations should support business objectives.

Service-level integrity falls short of the SLA.

Cost – will one entity bear the cost or will the cost be shared?

Short-term versus long-term value.

Software-related issues (function, performance, quality).

Testing is inadequate.

APIs aren’t perfect. Some are well-documented and functionally sound, while others are not.

Data integration implementations can be accomplished in several different ways including:

Manual integrations between source systems.

Application integrations that require the application publishers overcome the integration challenges of their respective systems.

Common storage integration data from different systems is replicated and stored in a common, independent system.

Middleware which transfers the data integration logic from the application to a separate middleware layer.

Virtual data integration or uniform access integration, which provide views of the data, but data remains in its original repository.

APIs which is a software intermediary that enables applications to communicate and share data.

Automation Testing Tools In 2023: Code

According to a 2023 survey, ±87% of development teams have adopted some level of test automation. Choosing the right tool is one of the first steps in automation testing. 70% of developers state that they use open-source tools like Selenium or Appium, while 52% use automation via frameworks. To choose the right automation tool, it is important to consider the nature and complexity of test cases, as well as ensure that the business infrastructure can accommodate the testing tools. Here we investigate the types of tools and how to choose the best fit:

Selecting the right tool

When selecting the tool for test automation, it is important to consider the following points:

Covered platforms (hardware/software and mobile/desktop)

Ease of installation and setup

Ease of use and user interface

Features like

Object storage and maintenance: Features for storage and maintenance of test data

Scripting languages (Java, Perl, Python, etc.) supported. While examining this, it is important to consider the level of programming and script-writing expertise of team members

Product support and documentation

Licensing model and total cost of ownership

It is also important to determine the scope of automation such as the complexity of tests and existing databases.

Fortune 500 companies rely on Testifi as a provider of test automation solutions, including Nokia, Amazon, and BMW. Web & API testing capabilities are provided via their CAST & PULSE products, which have tracking and a real-time performance dashboard.

There are several types of test automation tools, each has a different type of testing, level of programming, open source options:


Code-based testing automation tools enable testers to write a code for test cases, reuse it for multiple integration, and customize it according to business goal. Typically, code-based tools support different programming languages and frameworks, and are often open-source with a large community to support users and share codes for test cases. Some code-based automation testing tools also have record-and-playback frameworks to run scripts without coding. However, for complex test cases, testers need to have a high level of programming experience to write and run test case codes. Some of the most popular code-based tools are:

Selenium (web application testing)

Appium (web, iOS, and Android application testing)

Low code

Low code tools provide a GUI platform to enable non-technical team members to write and run test cases, hence reduce the need for developers in the team. However, low code automation testing platforms typically require set up and minimal coding to tweak built-in codes for specific test cases. Some examples of low-code test automation tools include:

Cerberus Testing

No code

No code tools are similar to low-code tools such that they allow for codeless test scripts which can be reused in different environment. Users build tests by using the Graphical User Interface (i.e. by dragging-and-dropping, visual development). Additionally, no code tools do not require any further coding on existing scripts. However, no-code and low-code automation testing tools may not be as customizable as code-based tools where the tester writes a code for specific test cases, therefore, most low-code and no-code tools come with a coding option. Some no-code tools include:


Katalon Studio







Here’s a list of features of code-based and code-less automation testing tools:

RPA bots are typically used to run repetitive processes in launched products. However, in test automation, bots eliminate the need for test script creation and maintenance, especially in GUI testing, web applications, and automated regression testing (ART) which aims to check for defects in existing features after software/system updates.

Bots can be integrated to a web browser and programmed to perform tasks typically done by the testers, such as:

Robot Framework


Test selection: AI/ML algorithms can scan the software and match it to training data in order to:

Suggest test cases

Estimate test design

Test maintenance: AI/ML algorithms are trained to:

Detect GUI defects

Monitor software changes

Update existing tests to align with changes

Update UI elements, field names, test gaps, etc.

Some AI-enabled tools include:

Applitools Eyes



Parasoft SOAtest

For more on quality assurance

To learn more about quality assurance automation, feel free to read our articles:

If you are ready to purchase a solution in this space, you can check out these vendor lists:

Or we can guide you through the process:

To get the latest guides on automation testing, download our whitepaper:

This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





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