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Test automation has become essential to the software development life cycle (SDLC) in the fast-paced software development industry, providing faster and more efficient tests than manual testing.

It has become increasingly important to have a well-designed and well-executed test automation strategy to ensure that the software product meets the desired quality standards. In this article, we will discuss some of the best practices for test automation that can help you achieve optimal results and maximize the value of your testing efforts.

13 test automation best practices 1- Define your objectives

The first step in test automation planning is to define your objectives. Determine what you want to achieve with your automated tests. Ask your QA team the following questions:

Are you seeking to reduce manual testing efforts in your software development process?

Do you want to increase test coverage across your application’s features and functionalities?

Are you aiming to improve the overall quality of your software through more efficient testing methods?

Defining your objectives will help you determine what types of tests you need to automate and how to prioritize them.

2- Choose the right test automation tool

Choosing the right test automation tool is the second step toward successful automation. Selecting a tool that fits the project’s specific needs and requirements is essential. The testing tool should preferably be:

Easy to use 


Codeless (depending on your team’s knowledge of coding)

Provide good documentation and support.

You can check our article “Top 10 Test Automation Tools for 2023: Detailed Benchmarking” for an in-depth guide to test automation tools. 

Several Fortune 500 companies, including Nokia, Amazon, and BMW, rely on Testifi as a supplier of test automation solutions. Web & API testing capabilities are provided through their CAST solution, which includes tracking and a real-time performance dashboard.

See the demo below to understand how CAST works.

3- Define your test automation framework

A test automation framework is a set of guidelines, rules, and coding standards you follow when developing automated tests. Defining your test automation framework will help ensure your tests are reliable, maintainable, and scalable. Some of the popular automation testing frameworks include:

Page Object Model (POM)

Behavior Driven Development (BDD)

and Data Driven Testing (DDT)

Implementing these test automation frameworks will improve the quality and efficiency of the testing process. See Figure 1 for an illustration of test automation frameworks.

4- Start with a small set of tests

It’s best to start with a small set of test cases and gradually increase the scope of automation. Starting with a small set of tests allows for better planning and implementation and helps identify potential issues before scaling up.

5- Maintain test data separately

Test data should be kept separate from the automation code. This makes maintaining and updating test data easier without affecting the automation scripts. It also helps to ensure the consistency of test data across different test runs.

6- Use test data management techniques

Test data management is creating, storing, and managing test data for your automated tests. Using test data management techniques is important to ensure your tests are accurate and repeatable; it will also promote data-driven tests. Some popular test data management techniques include

Data-driven testing

Random test data generation, 

Test data masking

See Figure 2 to understand why test data management is crucial.

Source: Novature Tech

Figure 2: Why is test data management crucial?

7- Implement version control

Version control systems like Git allow for tracking changes to automation scripts and help manage collaboration between team members. It also enables the restoration of earlier versions of automation scripts in case of issues.

8- Use descriptive names

Using descriptive naming conventions for test cases and automation scripts can make understanding and maintaining the code easier. This helps improve team members’ collaboration and reduces maintenance time.

9- Keep test cases simple

Test cases should be simple and easy to understand, with a clear purpose and expected outcome. This helps to avoid confusion and errors in test execution and results analysis.

Keeping test cases simple is essential for test automation because it improves your test suites:






Simple test cases are easier to maintain, modify, and reuse in different contexts. They are also less prone to errors, more reliable, and easier to debug when things go wrong. 

Furthermore, simple test cases are more scalable and can help you maintain a manageable and maintainable test suite over time. By keeping your test cases simple, you can improve the effectiveness and efficiency of your test automation efforts and ensure better quality software.

10- Schedule regular maintenance

Regular maintenance of automation scripts is essential to ensure they work correctly as the application being tested evolves. Maintenance should include updating the automation scripts to reflect changes in the application and fixing any issues that arise.

11- Implement continuous integration and continuous deployment

Continuous integration (CI) and continuous deployment (CD) are best practices that help ensure the automation scripts are always up to date and integrated with the latest changes in the application. CI helps to automate the build process, while CD automates the deployment process. 

Automated testing is a critical component of CI/CD because it enables developers to catch issues early in the development cycle and prevents errors from being introduced into the production environment. 

According to McKinsey’s study on AI adoption, businesses that benefit the most from AI implement cutting-edge techniques like MLOps in their AI/ML initiatives.

Read our article to know the differences between continuous deployment and continuous delivery: “Continuous Deployment and Continuous Delivery”

12- Run tests on multiple platforms and browsers

Running tests on multiple platforms and browsers helps to identify any platform or browser-specific issues. One of the main aims of automated software testing is to improve the quality of software and applications; running tests on multiple browsers can improve the overall quality of the application.

13- Analyze the test results

Analyzing the test results regularly and taking corrective action to address any issues identified is essential. This helps to ensure the application’s quality and the automation scripts’ effectiveness.

Get our test automation white paper:

If you have further questions, reach us:

He received his bachelor’s degree in Political Science and Public Administration from Bilkent University and he received his master’s degree in International Politics from KU Leuven .





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8 Digitization Best Practices In 2023

~90% of business leaders report that digital transformation will be essential for business success. Digitization is the first step in the digital transformation process (Figure 1). Digitization entails converting paper-based text or other documents to digital format and organizing them. However,

More than half of digital transformation attempts fail.

~38% of business leaders suggest that acquiring the resources required to carry out digital transformation is a challenge.

This article explains critical digitization best practices to provide executives with resources for digital transformation.

Figure 1: Digital transformation steps.

1. Follow a workflow and plan for the digitization project

Before starting the digitization process, it is important to have a clear plan for the digitization project. Establish a clear workflow for digitizing physical files to ensure that all steps are completed in an organized and consistent manner.

Alignment deficit is one of the common reasons for digital transformation failures. To solve this problem, top and middle management can agree on digital transformation plans, including plans for digitization.

For digitization projects, the following can be determined: 

The types of materials that will be scanned

The format of the digital surrogates (i.e., the digital version of the physical file)

The intended use of the scanned files

2. Scan documents for digitization

Prepare the documents: Sort them, remove any staples or other bindings, and make their single page. If necessary, repair any damage to the documents before scanning.

Scanning: This step involves scanning analog material into a digital format like an image.

Use good lighting: Good lighting can help ensure that the scans are clear and legible. To boost the contrast between the document and the background, shoot in natural light (ideally daylight).

Use a document feeder: If you are scanning many documents, consider using a document feeder to speed up the process and reduce the risk of damage to the documents. Some automatic document feeders can hold up to 500 pages. Some scanners can also allow you to simultaneously stack multiple documents and scan them to various spots on your device or local network.

Data capture: This step involves extracting information from digital materials using specialized software. For example, optical character recognition (OCR) software can extract data from images.

Quality control: This step involves checking the data for accuracy and completeness.

Data storage and management: This stage entails storing data in a digital format and maintaining it for long-term preservation and access.

3. Leverage OCR & IDC to extract data from unstructured documents

Unstructured data and documents account for ~90% of enterprise data, requiring the integration of multiple technologies to convert them to machine-readable formats.

These tools can convert images and other materials (e.g., PDFs, photos, and handwritten paperwork) into machine-readable data, enabling document automation. This data can be used to

Check documents for data quality issues.

Categorize documents.

Extract insights from documents.

Generate new textual documents like invoices and contracts based on the extracted data.

For example, Fleet Hire Services, a car rental company, used OCR technology to digitize ~11,500 monthly car rental agreements. Using OCR reduced the need for manually entering data about rented vehicles.

4. Test the digitized copies

Before discarding the original documents, it is a good idea to test the digital surrogates to ensure they are accurate and readable. Testing can be critical for compliance with laws and regulations. For example, some United Kingdom National Health Service (NHS) requirements for archiving include:

Authenticity: Archived documents must be created or delivered by the individual purported to have done so.

Integrity: Records need to be:

Complete and unchanged.

Secured from unpermitted modifications.

Changes made after creation are identifiable, as is the individual who made the modifications.

Usability: Records can be located, retrievable, and interpretable. 

To ensure compliance with the archiving regulations for electronic documents, the following testing considerations can be beneficial: 

Quality assurance: Testing the digitized copies allows you to ensure that they are accurate and readable. This is particularly important if the original documents are discarded after digitization.

Detection of errors: Testing the digitized copies can help identify any errors or issues during the digitization process, such as blurry or incomplete scans.

Data integrity: Testing the digitized copies helps ensure the integrity of the data they contain. Important information may be lost or altered if the digitized copies are inaccurate. 

User experience: Testing the digitized copies can help ensure a positive user experience by identifying any issues that can make the digital surrogates difficult to use or access.

5. Create backup copies

It is important to create backup copies of all digitized materials in case the original files are lost or damaged because digitized documents are vulnerable to data loss or corruption due to hardware failures, software errors, or other issues: 

21% of individuals have never created a backup.

29% of data loss cases are due to accidental causes

Approximately ~5% of data loss is caused by lost or misplaced devices, amounting to ~$4 million in penalties (see Figure 2).

There is a ~15% chance that cloud misconfigurations will result in data loss costing up to ~$4M (see Figure 2).

There is a ~12% chance that third-party software vulnerabilities cause data loss worth up to ~$4.5M (see Figure 2).

Figure 2: Average cost of data breaches and their frequency (measured in millions of dollars)

If the original files are lost or damaged, and you do not have backup copies, the information they contain can be permanently lost. Creating backup copies of digitized materials can help protect against data loss and ensure that the information is still available if the original files are lost or damaged. 

6. Use a consistent naming convention to store your digital files

Using the same naming convention for scanned files can make it easier to find and organize them. A document naming convention is a set of rules for naming files in a way that shows what they are and how they relate to other file formats.

File naming conventions can increase the probability of successfully searching for files and increase efficiency. Office professionals report that:

~93% of employees have difficulty locating the document they are seeking.

~83% of employees recreate a file because it cannot be located on the organization’s network.

7. Use content services platforms to store documents

For easier access, digitized documents can be stored on content service platforms (CSP). Content services let users store, operate, monitor, and retrieve documents from one place.

Cost-effective storage

Content services platforms can offer cloud repositories to store documents (see Figure 3). CPS cloud services can offer:

1 TB storage space on the cloud for $75.

A user interfaces for monitoring cloud usage with interactive dashboards (see Figure 4).

Increase in storage space on an as-needed basis.

Figure 3: CSP features.

Figure 4: CSP’s user interface for monitoring cloud usage.

Enterprise search

CPSs can be used for improved search capabilities for electronic file format (see Figure 3). They can offer:

Improved search features for digital collections: Platforms for CS can organize digital files using metadata information. Metadata can be added to digital objects like text documents or images to provide additional information about the document, such as the date it was created, the author, and keywords that describe the content. The search feature on content service platforms can be used to look up these digital files.

Predictive filing using AI: CS services can provide artificial intelligence (AI) services to forecast employees’ filing habits. AI can determine where to store files on the content services platform. Using AI in filing can reduce the effort involved in searching for documents.

Version control

CSP can provide version control to inform users about document versions. Version control is important during the digitization process because it makes sure that the most up-to-date version of a document is used. This is important to prevent files from being lost or overwritten.

Specifically, version controls can be important for digitization because

Collaboration: When multiple people are working on a document, version control can help ensure that everyone is working with the most current version and that changes are not lost or overwritten.

Auditability: Version control can help track the history of changes made to a document, making it easier to audit and identify any issues or errors.


CSPs can also offer editing layers to keep new versions of documents from being changed. With editing layers, changes made by different users can be saved in distinct layers. In digital image editing, layers are used to separate different components of an image (see Figure 5).

Figure 5: A document with editing layers.

8. Ensure security for the digital copies

To prevent unauthorized access or data breaches, it is important to make sure that digital information is stored and sent safely. The cost of data breaches in the U.S. was about $9.5 million in 2023.

To ensure the security of digitized documents, it is critical to follow best practices for data security, such as:

Assess and categorize data 

Classify digital information by sensitivity and business value. Discarding non-productive data is especially important if it contains personally identifiable information (PII).

Develop a data usage policy

Business data can be protected by limiting data usage and deactivating after a task. For example, the principle of least privilege—granting users the minimum permission to do their jobs—is a good approach.

Utilize security-improving technologies 

Privacy-enhancing technologies (PETs) allow businesses to leverage their data without jeopardizing privacy and security. PETs include:

Use cryptographic methods: Homomorphic encryption and zero-knowledge proofs can prevent unauthorized access to sensitive data. 

Use content encryption for cloud storage: Content encryption can encrypt files in the CSP’s cloud storage. When files or videos are uploaded to the content-encrypted cloud storage, only employees with access keys can only view the content in the storage with content encryption.

Use data masking techniques: Data masking is a group of techniques for modifying sensitive data without altering its structural properties. Automated redaction techniques can remove PII or sensitive personal information (SPI).

For more on best practices in digitization, please contact us at:

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





Bi Governance: 6 Implementation Best Practices In 2023

The global business intelligence market is projected to be $33.3B by 2025, with more business units adopting BI tools. Importance of business intelligence is increasing. Data-driven decision making, for instance, is five times faster via data access and data analytics.

However, data quality in BI strategy is paramount if companies want to use it to reach their strategic goals. BI governance, or business intelligence governance, is the set of ground rules that ensure the data used across different business units is of high quality, fresh, applicable, and secured.

In this article, we will explain BI governance in more detail, and introduce you to 5 best practices when rolling our your BI governance framework.

What is BI governance?

BI governance is a business-wide strategy of how business intelligence data is gained, used, analyzed, accessed, and stored. For example, if your business units store their sales data differently from one another, or collect it from different data sources, it’d be difficult to analyze all data sets in the same way.

Or imagine you are sharing financial performance data with the shareholders. An “ungoverned” approach would be to send it sporadically, using a different set of metrics each quarter. A “governed” way would be send the report on specific day of each quarter, in a standardized format, using a dedicated financial or reporting solution.

What are the steps in BI governance?

BI governance is roughly made up of 5 subprocesses:

Data usage monitoring: To run an effective BI governance, business users’ data usage should be measured and monitored. This helps the BI development team to identify and categorize data based on usage.

Build new content: Based on your initial findings and the new data that comes your way, create content similar in theme and applicability to the other data that’s already popular with your user community.

Measure data performance: To optimize your BI implementation, you should constantly measure how well your BI strategy is working in action with KPIs. Is it increasing user productivity? Is it receiving engagement? Is it making employees’ lives easier? How are the various business units reacting to it? etc.

Declutter: Remember the vice words of Marie Kondo. An effective BI governance strategy is one that is lean and streamlined. Get rid of unusable data, low quality data, data that no longer corresponds to the current change management/business changes, and overall business intelligence. Only keep the data that sparks joy into the organizational structure.

Manage governance committee: Monitor the governance committee to ensure that your project management teams are constantly carrying out the previous four steps.

What are the three pillars of BI governance?

BI governance is built on three pillars: people, process, technology.

People: People are all the business users, managers, and developers who must work together to implement BI governance on existing processes.

Process: Each to-be-rolled-out process should be mapped out and agreed upon by the people. Tools such as process mining are useful in BI development.

Technology: Once people have agreed on implementing a BI governance framework, and have chosen the suitable processes, they can use a BI solution to create a well governed BI environment.

What is a BI team?

A BI team is composed of “people” who develop and implement the BI governance initiatives. Those people are:

Business owner: The business owner envisions how things should be done, what metrics should be monitored, and what business rules should be enforced.

BI analyst: BI analyst translates those demands and requirements into binary logic and defines from where the relevant data should be sourced from.

Data steward: Data steward is tasked with sourcing the right data that the BI analyst needs to do their job, which is monitoring the metrics the business owner has laid out.

6 best practices for BI governance 1. Assess process diameter

Process diameter refers to how many users a business process involves, and how many business units it impacts. For example, the procurement process is important for various business units. That’s because supply chain issues dictate that manufacturing sustenance and cash flow maintenance is dependent on a consistent procurement process.

To certify and mandate such a business process that has a high audience and high business impact, the BI team should work in top-down fashion. But for less critical processes, whose short term deviations don’t affect the long term vision of the company, shortcuts can be made where only the BI analyst and the data steward collaborate.

2. Recertify processes continuously

To ensure that your BI governance strategy is moving in the right direction, you should be monitoring and re-certifying your BI governance framework constantly. For example, you should ask the project management teams if they are still using the correct metrics when assessing business performance.

For instance, businesses follow a boom-bust cycle. In expansionary times, such as 2024-2024, businesses should focus more on expanding their operations and expediting their growth. So when the analysts create reports, the customer list is a metric for growth. But in down times, very few companies expand. So instead of serving more customers, they focus on serving their existing customers the best. So the metrics to focus on is customer satisfaction and customer churn.

Once responsibilities are laid out, processed, identified, and the metrics established, you should pull in the data and compare the as-is scenario vs. the ideal scenario. An effective bi governance strategy is dependent on fast and easy consumption of data.

BI tools automatically extract the sensitive data from different business units’ processes, standardize them, and present them in a digestible manner on visible dashboards. For example, you’d want to certify that each time a customer complaint was registered on the system, a representative reached out to them in the first 24 hours. The bots in the solution architecture would check the event logs data and extract the useful information. They then would put it in a report. And send it to the authorized personnel.

4. Maintain data governance strategy

A well governed BI environment is dependent on basic data governance to ensure each employee has access to the kind of data they have clearance for. So not only does data governance ensure data security, but it also increases transparency as the employees will have access to all the data relevant to them (Figure 1).

Figure 1: Dashboard of a vendor’s BI tool restricting employees’ access to sensitive data. Image source: Metric Insights

Most BI tools today have an optimized search function where business users can quickly search for something relevant to their business unit – this is also known as self service BI. Moreover, if data is restricted to them, they can request access and wait for a response.

5. Certify data sets

An effective BI strategy dictates that data sets are continuously certified so the user community knows they can trust the data at their disposal. Data quality and data integrity should constantly be monitored and maintained so when the employees create reports, it’d be an accurate piece of business intelligence.

6. Optimize BI resource optimization

Closely related to the concept of data sets certification, is BI resource optimization. Business intelligence strives for streamlined access to data, and so all data that is outdated, duplicate, irrelevant, or not useful should be taken out of circulation.

BI governance solutions can automatically scan the data dictionary, monitor data usage, user engagement, and assess its quality to put irrelevant data down the pecking order as a search engine would. Better content management results in less frustrated bi users and expedites ROI realization. That’s because the company will reach its BI maturity goal faster.

For more on business intelligence

If you are interested in learning more about business intelligence, read:

And if you want to invest in a business intelligence software, we have a data-driven list of BI vendors prepared.

We can help you through your vendor selection process:


He primarily writes about RPA and process automation, MSPs, Ordinal Inscriptions, IoT, and to jazz it up a bit, sometimes FinTech.





Whatsapp Marketing For Business: Best Practices In 2023

WhatApp is the most popular messaging app in the world (see Figure 1). Businesses use WhatsApp for commercial purposes, such as building their connections through Whatsapp marketing campaigns because it provides several benefits to businesses in reaching out to their existing and new consumers.

Figure 1. Most popular global mobile messenger apps as of January 2023

Source: Statista

In this article, we will explain WhatsApp marketing for business, its benefits and the best WhatsApp marketing strategies to successfully implement it.

What is WhatsApp marketing?

WhatsApp marketing is a multi-dimensional marketing approach that leverages the features and capabilities of the WhatsApp messaging app to achieve marketing and communication objectives. Given that WhatsApp is a platform with over two billion users worldwide and the number one messaging app across the world, it presents a significant opportunity for businesses to reach a large and diverse audience in a direct and personalized manner.

Businesses use WhatsApp Business app as one of their marketing channels to:

Advertise products, services, or brands

Directly engage with customers

Send promotional messages

Some successful and famous WhatsApp marketing examples include:

Netflix used WhatsApp notifications not only retained its existing customers but also brought back users who had unsubscribed to resubscribe

Absolut Vodka used WhatsApp marketing campaign to promote the launch of a limited-edition line of vodka

What are the benefits of WhatsApp marketing for business? 1- Large user base

WhatsApp has a huge global user base. It’s one of the world’s most popular messaging apps with over two billion users. This presents a vast pool of potential customers for businesses to reach out to.

2- Direct communication

WhatsApp allows businesses to communicate directly and instantaneously with their customers. This creates an opportunity for real-time engagement, personalized customer service, and immediate resolution of queries or issues.

3- Multimedia support

WhatsApp supports a wide variety of content types, including text, images, videos, voice messages, and documents. This enables businesses to deliver rich and engaging content that can effectively showcase their products or services.

4- High engagement rates

People tend to check their WhatsApp messages regularly, leading to high engagement rates. This means that businesses can potentially get their message seen and read quickly.

5- Cost-effectiveness

Compared to traditional marketing channels, WhatsApp can be a more cost-effective solution, especially for small businesses. It’s free to use, and even the business version incurs minimal costs.

6- Trust and credibility

WhatsApp is a trusted platform that people use to communicate with friends and family. By being present on this platform, businesses can leverage this trust and build credibility with their audience.

7- Customer preference

More and more, customers prefer quick and convenient communication channels to reach companies. By adopting a WhatsApp marketing strategy, businesses can cater to this preference and improve customer satisfaction.

What are the best practices for a successful WhatsApp marketing strategy? 1- Define your goals, target audience, and KPIs

Next, understand who your target audience is. Learn about their needs, preferences, and how they use WhatsApp. 

Then, establish KPIs (key performance indicators) to measure the success of your strategy. These could include metrics like: 

Message delivery and read rates

Response rates

Customer feedback, etc.

2- Create a good WhatsApp Business profile and a brand persona

Your WhatsApp Business profile is the first point of interaction that customers have with your brand on the platform. Therefore, it’s crucial that you take the time to create a detailed and engaging profile. You should have a WhatsApp Business account that leverages WhatsApp Business API for many reasons (see Figure 2). For more, check out our articles on WhatsApp Business API and the best WhatsApp Business partners.

Figure 2: WhatsApp Business API vs Business App

Your business profile should include all the necessary information about your business such as: 

Your business name

A profile (ideally your company logo or something that is instantly recognizable and connected with your brand)

Contact details


Business hours

Website URL

A brief description of your business 

Ensure that all the information provided is accurate and up-to-date to prevent any confusion.

Remember, your WhatsApp Business profile and brand persona reflect your brand identity. A well-crafted profile and a consistent brand persona can significantly enhance your brand image and credibility on WhatsApp.

Start building a contact list of customers who have opted in to receive your messages. It begins with gaining consent from your customers to send them WhatsApp messages. This can be done through various touchpoints, such as your: 


Social media platforms

In-store communications

Email campaigns

When collecting consent, be clear about what type of content you will be sending. This transparency can help in reducing opt-outs and maintaining a healthy contact list.

Segmenting your customer base allows you to tailor your messages to specific groups, making your communications more personalized and relevant. You can segment your customers based on various parameters like: 

Demographics (age, gender, location, etc.)

Buying behavior (purchase history, browsing behavior, product preferences, etc.)

Their interactions with your previous WhatsApp messages

4- Send personalized messages to each customer profile

Personalized marketing messages are powerful to improve engagement and build stronger relationships with your customers. When you send personalized marketing messages, you’re showing your customers that you know and understand them, and that you value their unique preferences and needs.

This involves tailoring your messages based on the customer profile or the segment they belong to. For example, if you have a segment of customers who have shown an interest in a specific product category, you could send them personalized messages about new arrivals, special offers, or content related to that category.

Personalized messaging could be as simple as using the customer’s name, or more complex like sending product recommendations based on their past purchases.

5- Automate WhatsApp reminders and notifications

Automation can save time and improve efficiency. Use the automated messaging feature of WhatsApp Business to send reminders, order updates, appointment confirmations, etc. But remember, while automation is useful, it shouldn’t compromise the personal touch in customer interactions.

6- Deliver automated WhatsApp customer support powered by chatbots

An important part of a marketing strategy is to also offer excellent customer support when your customers need it. However, handling a large volume of customer queries and issues in real-time can be challenging. That’s where chatbots come in.

Chatbots are AI-powered tools that can simulate conversation with users in natural language. When integrated with WhatsApp, they can handle a multitude of tasks such as: 

Answering FAQs

Providing information about products or services

Taking orders

These are also the reasons why customer support and marketing is intertwined.

Implement a chatbot to handle common queries and provide instant support. However, ensure there’s an option for customers to connect with a human agent when needed. 

7- Get feedback from your customers

Customer feedback is a valuable source of insight for improving your services. Regularly ask your existing customers for feedback on your products, services, or their experience with your WhatsApp service. This will not only help you make necessary improvements but also make customers feel valued and listened to.

If you have questions, don’t hesitate to contact us:

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





Ai Scaling: 4 Best Practices For Organizations In 2023

Artificial intelligence (AI) is transforming industries and how businesses function with hundreds of use cases. In order to realize its full potential, companies should scale their AI initiatives and widely integrate AI applications into their existing business processes. According to Accenture, companies that deploy AI at scale achieve ~3x higher return on AI investments than companies that are in the AI proof-of-concept stage.

However, scaling AI initiatives from the proof-of-concept phase to the enterprise level is a major challenge for many businesses. This is because AI at scale involves much more than plug-and-play AI models. In this article, we explore 4 key steps for businesses looking to successfully adopt AI at scale.

1. Invest in your data strategy

Data is the lifeblood of AI and ML models and an organization-wide AI strategy should start with data. As AI is scaled, it becomes increasingly challenging to manage, cleanse, maintain, and utilize data. Therefore, without proper methods and tools for managing the various aspects of a data lifecycle, it is nearly impossible to deploy AI at scale across an organization.

Some challenges of dealing with big data are:

Data silos: This is data that is accessible by one or a group of departments but is isolated from the rest of the company. This reduces transparency and efficiency within the organization.

Incompatible data: Data collected from different sources has different formats and must be standardized before usage.

Inaccurate data: Large datasets necessarily include inaccurate, outdated, and other problematic data that must be cleaned for an accurate analysis.

Organizations must implement a robust data management strategy to successfully scale their AI initiatives that covers all components of a data lifecycle including collection, storage, integration, and cleaning. 

A key component of a scalable data management strategy is automation. Organizations should adopt DataOps practices to efficiently automate data orchestration.

There is also a wide range of tools that cover parts or the entire data lifecycle management. Feel free to check out our hub for data management tools ecosystem.

2. Streamline AI processes with MLOps

Similar to managing data-related processes, you should also standardize and streamline how you build, deploy, and manage your models. It’s relatively easy to build a few ML models that work well for specific business problems, but things can get complicated quickly if you want to adopt AI systems across the enterprise. This is because:

Building machine learning models require lots of trial and error for optimal models, datasets, hyperparameters, codes, etc. This is especially challenging for complex models such as deep learning, natural language processing (NLP), or computer vision, trained on large datasets.

Building a model is quite different than bringing it to real use: Only 36% of companies were able to deploy an ML model beyond the pilot stage.

All models should be monitored in real-time to ensure that they don’t suffer from model decay.

Managing all these processes manually with data scientists from different departments working in silos would seriously hinder an organization’s ability to scale its AI projects.

This is why companies have started to adopt practices known as MLOps to standardize and automate processes associated with building and managing ML algorithms. MLOps helps organizations to:

Streamline machine learning lifecycle with automated pipelines,

Create a unified framework to follow which facilitates improved communication and collaboration between stakeholders.

Feel free to check our in-depth article on MLOps for a comprehensive account.

3. Build multidisciplinary teams

A small team of data scientists may be enough to manage a couple of models. But AI scaling requires a wide range of skill sets, including data engineers, IT and cybersecurity specialists, project managers, and so on. More importantly, technical staff must be connected with business professionals who would determine specific use cases according to business needs.

For this purpose, companies have started to establish AI Centers of Excellence (AI CoE) to close the gap between executive decision making and AI implementation within an organization. These business units bring technical experts from different departments and coordinate and oversee organization-wide data science and AI initiatives.

For more on AI Centers of Excellence, check our article on the topic.

4. Build an enabler company culture

Successfully scaling AI requires new tools and technologies but adapting the company culture and ways of working is just as important. This is because:

Employees may fear being replaced by AI which can slow down the transformation.

A large-scale AI transformation requires new skills as it fundamentally changes the way employees interact with machines.

In order to address these challenges, companies should:

Create opportunities for reskilling and upskilling for employees,

Restructure business processes, workflows, and policies,

Improve top-down communication to ensure that everyone understands what is changing, why it is changing, and what the expectations are.

These can help companies prepare for changes resulting from the large-scale adoption of AI capabilities and increase employee confidence and engagement.

Feel free to check our article on the importance of company culture on digital transformation initiatives.

You can also check our data-driven lists of:

to explore AI solutions. If you have other questions about how to successfully scale AI in your company, feel free to ask:

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





Best Robotic Process Automation Books You Must Read In 2023

Robotic Process Automation (RPA) has become an essential toolkit critical to enterprise success. An enterprise must first ensure which

Trilogy of RPA books

By Dr. Leslie P. Willcocks and Dr. Mary C. Lacity In this trilogy, the authors who are well versed in their writings, present an informative and well-balanced view of the subject. These books are a must-read by both the novice and automation practitioners. This series brings together-

Service Automation: Robots and the Future of Work

Robotic Process Automation and Risk Mitigation: The Definitive Guide

The RPA risk mitigation framework reveals key RPA risks and identifies 30 key risk mitigation practices which the research has found to be successful. This book serves as a definite key source of knowledge to all levels of the organization whether it is at the beginning of its RPA journey or has reached maturity levels.

Robotic Process and Cognitive Automation: The Next Phase

The book presents insights about the future of Automation with client interviews, providers, and analysts. The last book of the trilogy features a detailed analysis of the automation v/s the future of work debate. Readers can read an incisive and compelling, evidence-based perspective on the direction of service automation, and future trends through 2025. This book fuels the debate that automation technologies like Intelligent Automation and the newest Blockchain technologies are found to elevate human work rather than causing an elimination.  

The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems

By Tom Taulli In this book, Tom Taulli explains how to leverage RPA effectively in enterprises to automate repetitive and rule-based processes, like data inputting and transferring, scheduling, cut and paste functions, filling out forms, and basic search function. Based on industry-specific case studies and industry best practices Tom Taulli explains how companies have been able to realize a substantial Return on Investment (ROI) with automation implementation, such as lessening the need for outsourcing or hiring.  

Robotic Process Automation: Guide to Building Software Robots, Automate Repetitive Tasks & Become an RPA Consultant

By Richard Murdoch This is the perfect book for those who are looking to become automation consultants. Automation career is a viable option for developers and non-developers due to the no-code developer environment and intuitive design interface.  

The Care and Feeding of Bots: An Owner’s Manual for Robotic Process Automation

By Christopher Surdak JD In this book, Christopher explains what it takes to make bots work in organizations. Author Chris Surdak summarizes his experiences gained by deploying hundreds of bots for dozens of organizations around the world over 5 years. Along the way, the number of failures, missteps, and errors are also shared with the readers. “The care and feeding of Bots” explains eighteen different ways in which bots can fail and how to avoid these failures. This book also discusses the next wave of cognitive automation which will be bought by artificial intelligence and Cognitive Computing.  

Robotic Process Automation RPA: A Complete Guide — 2023 Edition

By Gerardus Blokdyk This book lets an organization assess what is their backup plan taking labor costs and operational expenses into account. Robotic Process Automation RPA: A Complete Guide seeks answers to key questions like an organization’s outlook for top-line revenue growth over the next three years and the debate over bots taking up human work. Readers face 898 new and updated case-based questions, organized into seven core areas of automation implementation. This Self-Assessment will help enterprises identify areas in which Robotic Process Automation improvements can be made within their existing processes.  

Digital Workforce: Reduce Costs and Improve Efficiency Using Robotic Process Automation

By Rob King Rob king explains the different types of Robotic Process Automation and how to align enterprise needs to the solutions available to start an automation journey. This book gives key insights to scaling up as well. The book is a carefully considered approach that helps enterprises align their specific business needs to the need-based solution and the requisite business model.  

The Simple Implementation Guide to Robotic Process Automation: How to Best Implement RPA in an Organization

By Kelly Wibbenmeyer In this book, the author examines critical issues, like how to overcome common problems when implementing RPA and start an RPA implementation journey to further successfully carry it out; by obtaining funding and support from leaders to build an RPA team for future scaling up. The book includes the positives and negatives of various deployment strategies in addition to the key factors to be considered. The author explains in crisp timelines and uses cases giving a realistic view of how to manage business processes.  

Becoming Strategic with Robotic Process Automation

By Leslie P. Willcocks, John Hindle, and Mary C. Lacity

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