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IT Outsourcing is the way to go for many projects and many companies worldwide. As the information technology industry progresses and thrives, the fostering of digital transformation is on the rise, even for companies that have no strategic core of technology development. 

Whether it is an enterprise or a startup, IT Outsourcing can be the tactical approach for prospects and enhancement, not to mention the cost-barrier to be lowered when implementing this.

However, as if managing complex technology projects wasn’t difficult enough, IT outsourcing can be a loose screw in your overall IT operations if the management methods go off the rails.

In fact, it can create numerous stresses that you have never experienced, causing malfunctions and underperformance when using an external team.

Interesting enough, it is normally the lack of communication, strategic analysis and management skills that derail the project, not the technical competence of the IT Outsourcing providers. Indeed, the most important and primary aspect in managing outsourced projects lies within how to meld diverse organizations into a cohesive unit. To do this, you might want to follow these 9 strategic guides for a cost-effective and cohesive operation when working with IT Outsourcing companies.

 

Identify what to outsource

The picture of outsourcing is not as simple as you might think. In fact, the adoption and implementation of hiring someone outside of your organization require a thorough examination and assessment of what to outsource to ensure minimal cost and strict security.

What should always be kept in your mind is the ultimate goal of outsourcing, which is the cost to be reduced. The question here is, which operations and systems can be outsourced without damaging the business’s operations and core strategic services.

 

To do this, you should divide your operation into 2, one of which is a commodity system and the other is a strategic system. By stating the “commodity”, we meant the activities that keep you running but do not necessarily differentiate you from other competitors in the market.

By dividing your business activities accordingly, you can have a clearer look at which activities can be outsourced to a supplier with a reasonable price and which cannot be entrusted to outsiders. 

 

Choose your supplier

One special feature of IT outsourcing is the length of the contract. Normally, the outsourcing contract in other sectors could last for years, while in the IT industry, the average contract duration is 2 years. 

The reason for such a short contract time is the unprecedented and unpredictable changes in the industry, not to mention the ever-growing amount of new approaches and technologies. Many companies might want to change the terms and requirements in the contract to adapt to new features of the industry, and this is inevitable.

When choosing your supplier, another thing to take into serious consideration is to minimize the power of suppliers by soliciting separate bids for each service. By doing this, you have already minimized the risks of complete dependence on the suppliers.

On the other hand, one should not automatically assume that a supplier would outperform their own IT department. Instead, the in-house department should be allowed to compete with the outsourcing team to institute the best practices.

To constructively choose the right IT Outsourcing supplier, your business should form a team consisting of mostly IT professionals to review the proposed bidding for each contract. Their deep technical expertise, together with a clear understanding of the company’s goal, can keep an objective eye on the service, benefiting the business operations.

 

Clarify your business goals

The clarification of your core value proposition and business goals does not necessarily stay at the top of your priority list, but it is a must for smoother and more effective collaboration between you and your outsourcer.

This does not entail endless and lengthy training sessions and documentation exchange. In fact, the essence of this is to share your most important features and elements of your business such as business process, business model and your people. 

 

Once your business goals have been clarified to the outsourcer, they will have more information to adapt and align their approach in accordance with your business goals. To achieve this, a mutual understanding between two parties can help a great deal to create more value, both for your business and the outsourcing company.

For the case of IT Outsourcing, you need paramount consideration of how to define your business and software concept. The questions of what it can do, what target audience and how efficient you want it to be have to be answered right at the beginning of the project. Being on the same page about mutual interests and values can create a strong partnership, which can be of great use in the time coming. 

 

Make sure everyone’s working from the same playbook

Being cognitive is very much advantageous to your business when you want to adopt IT Outsourcing, but it does not simply mean the alignment in business values, business goals or anything so large-scale like that.

In fact, it is about the resonation in how to work, process, test and deliver. One way to ensure the well-designed and robust workflow of the two parties is to create a playbook that contains step-by-step instructions.

This method has long been in use in some giants of the IT industry when outsourcing. With a detailed hand-book, all information compiled can facilitate consistent application of your requirements. 

With this hand-book, the majority of information is the background and the technical material that could help them in further streamlining and managing the project.

During your work of outsourcing your projects, you might encounter IT Outsourcing companies from different parts of the world, and they might not be speaking English. 

Hence, besides the written documentation and requirements, preparing wireframes, annotated diagrams and other visual aids is also recommended. With your needs clearly conveyed through specific notes, the outsourcer has a higher chance of getting what you want.

 

Work effectively with the time-zone

Assuming that you’re working with an offshore IT Outsourcing service provider from Vietnam, while you’re in America. In this case, working in different time zones can cause much delay and inconvenience. To deal with this, you should be cognizant with the time difference, hence scheduling meetings that suit both parties.

 

By doing this, it not only helps you save time, it also shows your concern about your partner, through which you can build a level of trust and motivation for both parties to work harder. 

To make this work, you can take the following as an example. If you have partnered with an IT Outsourcing company in Vietnam, which is 11 hours ahead of the East Coast of the United States, you can send them something at the end of your business day, they can work on it while you are sleeping and have it back to you at the start of the next business day.

 

Build business communication

“Communication is the key.” With communication, a mutual understanding is formed with bells and whistles of the dos and don’ts in the projects.

Alongside the overall message to be delivered during the project, you also have to ensure the understanding over the project and the work required from every team member. 

You have to make sure that every team member has a clearly defined workload for the day, and understands their purpose of the project. Here’s how to do this:

• Daily calls with the team or at least 2-3 times per week. 
• Have a team of business analysts (BA) or a project manager (PM) to ensure that every stage of the software development goes smoothly. 
• Regular but short meetings which prevent exhaustion with long discussions and get regular updates.
• Involve a tracking mechanism (aka metrics) for measuring the team’s productivity and quality.

 

Use project management software

On a small scale, management software may not be the most necessary. In contrast, a large-scale operation would benefit your business the most if you use a collaborative platform to manage projects. 

In this platform, instead of staff keeping track of their own workflow, tasks and schedule in an unorganized manner, they can follow their work, time management, and task progress, in tune with their visualize colleagues

 

In other words, this platform is like a gathering place which keeps all the records of one’s work, allowing users to accomplish more without getting distracted. 

When choosing a platform for your project, you should pay attention to the built-in extension and other broadening functionalities to find the most suitable one. 

For projects with sensitive security terms, some might find a customized or in-house built tool is more suitable. 

 

Adopt software development methodologies

There are many software development methodologies that you can make use of, the most famous of which is the Agile and Scrum method. These allow the outsourcing team to work under unification and collaboration. The feature of continuous integration and continuous delivery helps boost the speed of projects’ time to market. 

These also ensure flexibility, constant reiterations and close supervision upon the outsourcing team, entailing full control over the operation and activities.   

 

Sync up regularly

To effectively speed up and ensure the work of outsourcing, agreement on the sync-up schedule and reports should be made.

It can be done on a regular daily basis, or even weekly to ensure the time frame and outcome, especially if your outsourced team is in another time zone.”

“Effective communication, especially with new or off-site employees, is key to creating a more efficient, productive and profitable project,” says Handy. “Whether you are using Google Chat or Slack, keeping all project communication in one central location will increase accountability and allow all team members to communicate in real time.”

Operating an IT Outsourcing project in tune with your business culture that can ensure efficiency and productivity is tricky. With strategic approaches and detailed checklists of the dos and don’ts, you can easily lower the cost-barrier with IT Outsourcing providers. 

 

Don’t want to bother yourself with the trivial problems of IT Outsourcing? Contact LQA now for standard and scrupulous IT Outsourcing services.

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IT Outsourcing for startups – Entrepreneurship can either be as sweet as ripe fruits or as bitter as a cud. Especially under the overwhelming effects of the Covid-19 Pandemic, startups are struggling to beat the odds stacked against them. 

Deprived of a regular and dull nine-to-five job, entrepreneurs have to work around-the-clock to keep the business running. However, this does not ensure success. The essence of one startup to stand out starts from how it is operated. And sometimes, sticking to the regular methods is not exactly the answer.

In other words, it requires entrepreneurs to always think outside of the box alternative solutions to critically solve the root problems, hence boosting the business.

One such solution that is being applied worldwide might be outsourcing, which ironically some startups hesitate to engage in due to the foreseeable additional costs. 

 

 

To comprehensively calculate and estimate the profit margin here, these costs should be seen in the big picture in which the operational costs and HR costs are spiking. For an in-house IT staff, necessary human resources costs or costs for business processes pose a huge overall cost for the company, making it impossible for startups to gain profit.

Instead of in-house staff, more and more startups are leaning towards the idea of outsourcing. This implementation has proven its efficiency, but it can break loose at any time. The point for choosing which to follow lies upon the questions of “Do I want this done in-house or offshore?”, “Which of these options allows for greater growth?”

 

From in-house to IT Outsourcing for Startups 

Shifting from in-house to offshore/outsourcing requires multifaceted consideration. First, startups need to get rid of the knee-jerk reactions toward IT Outsourcing for startups. Lack of experience working through business process outsourcing sometimes leads to the belief that outsourcing, and this should be avoided. 

Typically, Startup owners often think of BPO as something that only large and well-established companies can implement. Facts have proven the other way round. Indeed, companies of all sizes and backgrounds can benefit from this emerging phenomenon. 

A survey from Clutch in 2018 shows that 37% of small businesses outsourced a minimum of one important business process, whereas 52% reported planning to do so the next year. 

While some major players in the field such as GitHub, Google, Microsoft, etc. employ a huge number of outsourced workers, some other companies which started as Startups such as Slack, Skype, Opera, etc. also achieved a lot through BPO.

 

Why outsourcing?

Outsourcing is often deemed as the solution for only large enterprises, in which it can help reduce costs and save time on trivial tasks. Nevertheless, the case of startups can apply the same approach as many are now expanding their businesses, heightening the need for outsourcing many tasks. What exactly can outsourcing do to urge entrepreneurs to go for it?

 

Cut back costs

The most dramatic change BPO companies can pull off is the reduction of operating costs. With costs of insurance, healthcare, travel expenses, rent of facility, etc. unburdened, business leaders now can leave financial resources available to expand and focus on the core business activities. 

 

 

With this being done, the cost reduction can be a part of the strategic growth plan of the business. Besides the common services to be outsourced such as customer support, accounting, HR, IT outsourcing for startups stands out with the highest growth rate due to the ever-expanding scale of the IT industry in general. The services to be outsourced include software testing services, software development services, etc.

 

Halt employee turnover

The puzzle of employee turnover is a chronic problem that has long been in the industry of information technology. A high turnover rate can result in high training costs and low efficiency. To have an outsourcing team taking care of all the training and operating costs for you can really ease out this headache, especially with the competitive prices from different regions. 

 

Experience and expertise from BPO

Startups often step in the game with high confidence, but it should be put within their expertise only. Instead of blindly following something you are not sure of, going for IT outsourcing for startups with specialized individuals and experts in the field is a better option. Hence, the chance of failing by wrongly estimating how well we can do all of the work ourselves is lowered.

Moreover, BPO is experienced in whatever field they are offering you. This also includes certifications, quality assurance, information security, stringent processes, all of which are the things that startups lack.

 

Low rate of burnout

Startups often require high volatility and a fast working pace with huge workloads, which can subsequently lead to burned-out employees. Outsourcing can solve this problem.

IT Outsourcing for startups might be attractive as it is, but please be noted that this is not a one-size-fits-all solution. Before rushing into anything, one must carefully evaluate the pros and cons of IT outsourcing for startups to avoid any kind of miscommunication or misoperation. 

Time zone differences, trustworthiness and credentials are the things you would want to consider when working with BPO. Start small, evaluate everything, and increase IT Outsourcing scale gradually.

 

Are you a startup and want to unburden some costs? Contact us now for full support from experts.

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The world has witnessed unprecedented growth in the information technology market and IT outsourcing trend, which can be seen in almost every aspect of our daily lives. With its share in the “market pie” remaining with a steady rate prior to the Covid-19 pandemic, the year of 2022 will mark a new milestone in the IT field in general and in IT Outsourcing in particular.

Why IT Outsourcing Trend?

IT Outsourcing is a service that has long been on the market with a relatively steady growth rate. As in the IT Services Outsourcing Market Size, Industry Report, 2020-2027 of Grand View Research, the global IT services outsourcing market is projected to grow at USD 520.74 billion in 2019. The annual growth rate (CAGR) from the phase from 2020 to 2027 is expected to be 7.7%.

Taking advantage of the shifting market

For a minor field in Information Technology, IT Outsourcing Service shows potential, but this growth rate was not that dramatic for us to call IT Outsourcing a flagship point.

However, with the world’s economy brought into a sudden and screeching halt due to the pandemic, many giants in business have shifted their focus to virtual/digital engagement with their clients. 

Surging from the uncharted waters, these businesses have proven the viability and possibility of how digital transformation can save a fortune, or perhaps even bring their names to the top of the chain.

Learning from the big names on the market, many other businesses, from big fish to local store owners, all want to apply the technology advances in their operations. To these businesses, digital transformation is the crossing bridge to bring customers and their services closer, especially under the influence of the pandemic in which people prefer virtual interactions.

Take Amazon and Shopify as examples, we can see that the application of e-commerce platforms was spiking in the first half of 2020. These platforms, of course, aim at selling, while their approach is through applications and software. Amazon, or Shopify, has its own in-house development and QA team. But for mid-sized or small-sized companies, they just can’t afford the HR and operation costs. Under this circumstance, the industry is anticipated to witness substantial demand for IT operations so as to allow companies to focus on their core tasks and reduce operating costs.

Parallel to the core tasks, the marginalized tasks also play an increasingly important role in businesses that are planning to foster digital transformation. 

Since the businesses wanting to employ digital transformation have no foundation or background knowledge over information technology, the IT Outsourcing market is progressing owing to the ever-increasing demand for consultancy.

The talking numbers

As the pandemic continues to put a strain on the global economy, many businesses plan to transition to remote work and online customer engagement and order fulfilment. In order to cope with this new approach, they increase spending on clouds, especially software as a service.

Financial cuts in the circumstance of the pandemic are a must, but the reduction in IT Outsourcing has eased from $83 billion in the spring to $31 billion at the end of 2020, signaling the growth in the global IT spending.

Worldwide IT spending is projected to total $4.5 trillion in 2022, according to Gartner’s forecast, growing by 3% compared to 2021, despite that people tend to cutback spending on PCs, tablets and printers by consumers 

As in the first phases of the pandemic in 2020, every aspect of the IT service was declining, but it began to take the initial steps for a huge growth in the years coming. For example, after contracting 4.6% in 2020 to $490 billion, worldwide IT spending on consulting and implementation services are predicted to experience a 4.5% CAGR through 2024. While worldwide spending on IT-centric managed services, infrastructure, and application support, which decreased 1.1% in 2020 to $475 billion, will see a CAGR of 5.3% through 2024. 

What companies want from their IT outsourcing providers

Pre-pandemic, the main focus for IT outsourcing providers was narrowly on specific services such as helpdesk, infrastructure, storage, network monitoring and network management. 

Post-pandemic, with the preferred solutions for digital transformation stay on top in almost every business, the IT outsourcing services are demanded to leverage and innovate to cope with the urgent needs for a wider range of requirements.

Subsequently, the expected outcome for this whole IT outsourcing service is cost avoidance. To achieve this, IT outsourcing providers are to fulfil the needs for:

1. AI and Automation

The employment of 4.0 Technology is developing with the pace that we’ve never seen before, leading to an upsurge in the need for human resources and infrastructure. Thousands of applications pilot every day, each with many features that require timid, tedious work of coding, testing and maintenance.

To this point, businesses who want to be ahead of the curve have to take advantage of being the pioneer, meaning they have to be the fastest and the most productive. Instead of the traditional way of expanding the team with experts in the field (which can be quite costly), many of the business owners decide to go for a cost-effective approach, AI and automation.

The fascinating idea of AI – a non-human machine that can interact with people is on the rise. But the real benefit of this is to reduce HR and operational costs. For example, before assigning a customer to a human customer service officer, the system has a chatbot to answer and interact with them. Only when the bot cannot figure out the requirement and how to fulfil it do they transfer the customer to a CS. With the bot work regardless of time, the business can save a fortune on the cost for a CS team.

2. Growth of the Cloud Services

On-premise storage for data management has shown weakness and limitations, hence the IT outsourcing trend in shifting to cloud services. 

Alongside the current worth of cloud computing reaching the hallmark of $180 billion worldwide is the market growth by 24% of PaaS, SaaS, and IaaS sections. In two years’ time, cloud computing service is predicted to soar to over $623.3 billion. 

One of the reasons why cloud computing is on the rise is the better protection of data. Moreover, it also ensures faster data operations and the ability to modernize business processes.

3. 5G

5G wireless technology is meant to deliver higher multi-Gbps peak data speeds, ultra low latency, more reliability, massive network capacity, increased availability, and a more uniform user experience to more users. Higher performance and improved efficiency empower new user experiences and connect new industries. – Qualcomm

With the employment of 5G in almost every aspect of the IT world, it speeds up the adoption of reliability, low latency and larger network capacity. Alongside its emerging deployment in major aspects such as medtech or Internet of Things, 5G also plays an important role in the development of AI implementation.

For example, as the Covid-19 pandemic took its toll on the world, some 5G-based applications have already made their way into medtech, especially in the adoption of telehealth and remote monitoring. All of the wireless technology, powered by 5G, have benefited the healthcare staff with utmost convenience.

For the part of AI implementation, 5G is pervasive in domains such as autonomous driving, virtual reality and augmented reality. With higher connection density and the ability to handle an immense number of connected devices at the same time, 5G comes to the forefront as the pioneering factor for both cost avoidance and service enhancement.

4. Cybersecurity

There’s no denying that information technology advances are developing with upsoar rate, resulting in the ever-growing number of service end-users. Larger number of users equals larger threat of cybersecurity. 

To have a screw loose in the cybersecurity is to bring threats to the system, but to recruit a full-stack IT security engineer is no easy task. Instead of having an in-house staff who works full-time, businesses are leaning towards IT outsourcing. They often need:

• Monitor your environment 24/7
• Thorough security staff training
• Security strategy
• Security architecture

One report from Allied Market Research estimates the market to reach nearly $41 billion by 2022, based on a 16.6% compound annual growth rate between 2016 and 2022.

5. Remote Work Statistics

According to Weforum, “The number of days US employees spend working from home increased from 1.58 per week in January 2021 to 2.37 in June 2022”, as the result of Covid-19. The IT sector, among many other sectors, has witnessed the dramatic shift to remote work, marking a new IT Outsourcing trend in the IT outsourcing market.

Working remotely is not new, especially under the specific traits of how IT staff can work. However, the rate is increasing with soaring popularity.

A report by Avasant shows that middle-sized tech companies have been the largest contributors to the growth of the IT outsourcing industry in 2020.

It’s also declared that the average outsourcing for midsize companies went from 9.1% to 11.8%. So while some tech businesses increased their IT budgets on the brink of the pandemic, the rest continued to work with their nearshore and offshore IT outsourcing partners to reduce development costs.

Delve deeper into other technology trends and industry movement.

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Automated Data Labeling is a new feature that is currently being constantly mentioned among Data annotation trends, and some even deem it the solution for the time-consuming and resource-consuming casual manual annotation.

As the Manual Data Labeling – aka Manual Data Annotation takes hours to annotate one dataset, the Automated data labeling technology now proposes a simpler, faster and more advanced way of processing data, through the use of AI itself.

 

How we normally handle dataset

The most common and simplest approach to data labeling is, of course, a fully manual one. A human user is presented with a series of raw, unlabeled data (such as images or videos), and is tasked with labeling it according to a set of rules.

For example, when processing image data for machine learning, the most common types of annotations are classification tags, bounding boxes, polygon segmentation, and key points.

 

 

Classification tags, which are the easiest and cheapest annotation, may take as little as a few seconds whereas fine-grained polygon segmentation could take a few minutes per each instance of objects.

In order to calculate the impact of AI automation on data labeling times, let’s assume that it takes a user 10 seconds to draw a bounding box around an object, and select the object class from a given list.

In this case, provided with a typical dataset with 100,000 images and 5 objects per image, annotators would have to spend 1,500 man-hours to complete the annotation process. This eventually would cost approximately $10,000 just for data labeling. 

The price of $10.000 is only for data labeling. For annotation project managers, AI data processing takes more than that. To ensure the high quality of the training data, they are compelled to add other layers of quality control and quality assurance. This helps manually verify and review each piece of labeled data, but it would be very costly. Moreover, the quality control and quality assurance staff must be trained of the sample output so that they understand what is required in the outcome of the annotation projects, thereby increasing the labeling costs by about 10%.

 

 

Some annotation project managers might choose consensus-based quality control. By implementing this method, the whole annotation project goes through multiple annotations. The same piece of data is annotated multiple times, and the results are consolidated and compared for quality control purposes. With this method, the amount of time and money is proportional to the number of annotators working on the same task. Simply put, if you had three users label the same image three times, you would have to pay for all 3 annotations. 

All this is to emphasize that, the two most expensive steps in data labeling are:

• The data labeling itself
• Reviewing and verifying it for quality control. 

 

Looking at all the huge costs that it would take in an annotation project, many business leaders have turned into a less time-consuming and tedious solution, which is the auto annotation tool technology.

Thankfully, with the latest technologies in artificial intelligence and machine learning, automated data labeling, or auto annotation, is usable now. However, to create an effective and well-rounded auto annotation tool now, it even requires more training data and human input for correcting errors induced by the AI. Therefore, anyone has the naive attempt to entirely apply auto annotation tools, they have to be cognizant of the truth that the tools are not the one-size-fits-all solution.

 

The advantages of Automated Data Labeling

Automated data labeling is quite a new term in the field, but the technology advancement implementing and making it happen is developing with high speed, shown in the large number of tools on the market now. So what are auto data labeling and its benefits?

 

What’s automated data labeling?

Automatic labeling is a feature found in data annotation tools that apply artificial intelligence (AI) to enrich, annotate, or label a dataset. Tools with this feature augment the work of humans in the loop to save time and money on data labeling for machine learning.

 

 

Most tools allow you to load pre-annotated data into the tool. More advanced tools, which are evolving into platforms (e.g., tool plus Software Development Kit or SDK), allow you to leverage AI or bring your own algorithm to the tool to improve the data enrichment process by auto labeling data.

Other tools offer prediction models that suggest annotations so workers can validate them. Some features leverage embedded neural networks that can learn from every annotation made. All of these features can save time and resources for machine learning teams and will have a profound effect on data annotation workflows.

 

Outstanding benefits of automated data labeling

When working with organizations using tools to annotate images for machine learning, we find two optimal ways to apply auto labeling in data annotation workflow:

• Pre-annotate some or all of your dataset. Workers come behind the automation to review, correct, and complete the annotations. Automation cannot annotate everything; there will be exceptions and edge cases. It’s also far from perfect, so you must plan for people to make reviews and corrections as necessary.
• Reduce the amount of work sent to people. An auto-labeling model can assign a confidence level based on the use case, task difficulty, and other factors. It enriches the dataset with annotations, and sends annotations with lower confidence scores to a person for review or correction.

We’ve run time experiments, with one team using tools that have an automation feature versus another team that is manually annotating the same data. In some cases, we’ve seen auto labeling provide low-quality results which increase the amount of time required per annotation task. Other times, it has provided a helpful starting point and reduced task time.

 

 

In one image annotation experiment, auto labeling combined with human-powered review and improvements was 10% faster than the 100% manual labeling process. That time savings increased from 40% to 50% faster as the automation learned over time.

It also had a more than the five-pixel margin of error for vehicles and missed the objects that were farthest from the camera. As you can see in the image, an auto-labeling feature tagged a garbage bin as a person. It’s important to keep in mind that pre-annotation predictions are based on existing models and any misses in the auto labeling reflect the accuracy of those models.

Data annotation tools can include automation, also called auto labeling, such as Labelbox and Tagtog, which uses artificial intelligence to label data, and workers can confirm or correct those labels, saving time in the process.

While auto labeling is not perfect, it can provide a helpful starting point and reduce task time for data labelers.

 

 

Some tasks are ripe for pre-annotation. For example, if you use the example from our experiment, you could use pre-annotation to label images, and a team of data labelers can determine whether to resize or delete the labels, or bounding boxes.

This reduction of labeling time can be helpful for a team that needs to annotate images at pixel-level segmentation.

Our takeaway from the experiments is that applying auto labeling requires creativity. We find that our clients who use it successfully are willing to experiment, fail, and pivot their process as necessary.

As auto data labeling is one of the breakthroughs for a better outlook of the AI technology, specifically machine learning, we still have a lot to discover with this new term.

 

 

If you want to hear from our experts concerning the matter of Automated data labeling, please contact us for further details.

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Parallel to the fast-paced development of the Artificial Intelligence and Machine Learning market, the field of data annotation is moving forward with the most accelerating trends, both in terms of tools and workflow.

From AI-Powered Virtual Assistant to Autonomous Cars, data annotation has played an important role.

Some might think that data annotation is a boring, timid and time-consuming process, while others might deem it the crucial element of artificial intelligence’s success. 

In fact, data annotation, or AI data processing, was once the most-unwanted process of implementing AI in real life. However, with the ever-growing expansion AI in multiple fields of our daily lives, the needs for rich, versatile and high-quality datasets are higher than ever. 

In order for a machine to run, in this case, is the AI system, we have to pour training data in so that the “machine” could learn to adapt to whichever is coming at it.

With these trends in the data annotation and AI data processing market, it not only sets a new outlook for the whole market, it also proves the urgent needs for well-annotated datasets.

 

Predictive Annotation Tools – Auto Labeling Tool

It is pretty obvious that the more fields we can apply Artificial Intelligence and Machine Learning in, the more we need AI data processing. 

By saying AI data process, we also mean that we need both the data collection and data annotation.

The rapidly expanding needs of the AI and machine learning market have set a new goal for another focus of the data annotation process. As it is with the Testing market, the demands for auto labeling, or we can call it predictive annotation tools are coming to a peak.

 

Basically, the predictive annotation tools (auto labeling tools) are the tools that can automatically detect and label items with the foundation of the similar existing manual annotations.

With the implementation of the aforementioned tools, after some manually annotated data, the toolkit can subsequently annotate the similar datasets.
Throughout this process, the human intervention is limited to the minimum amount, hence saving a lot of time and effort to do such repetitive and boring tasks.

With just some scratches on the surface, auto labeling, or predictive annotation tools may be the pivotal change that will boost up the speed of the annotation process by 80%. But to put one auto labeling tool on the market, it takes years of developing sophisticated features, not to mention a large number of data types need to be put in the data annotation system of that tool. That is why you often see one tool for only one data type.

While the advantages of an auto labeling tool are undeniable, the cost for one commercial tool like that can be enormous.

 

Emphasis on Quality Control

It is sure that Quality Control plays a huge role in every process. However, the current situation only shows that QC is only circumstantial. 

In the future, data engagements at scale will be the main focus, requiring a higher emphasis on quality control.

With more data labeling solutions going into production, and later into the training model of AI systems, more edge cases will be considered.

 

Under this circumstance, it is a must that you build your own teams of QC to exclusively handle the quality of the annotated datasets. They will not work the way the old QC staff did. On the contrary, these specialized experts can function without detailed guidelines and focus on spotting and fixing issues with large datasets.

What about security? With the security, the QC team should follow a stringent process of maintaining security of the annotation process. This should be ensured throughout the whole project.

 

Involvement of metadata in data annotation process

From autonomous vehicles to medical imaging, in order for the AI system to run smoothly without glitches, a staggering amount of data is required for annotation.

Metadata is the data clarifying your data. With the same old annotations as the code snippets you put in at the Java class or method level that further define data about the given code without changing any of the actual coded logic, metadata is for data management.

 

All in all, metadata is created and collected for the better utility of that data.

If we can make good use of the metadata, any human errors including misplacing things, management malfunctions, etc. will be tackled. With metadata in hand, we will be able to find, use, preserve and reuse data in a more systematic manner.

• In finding data, metadata speeds up the process of finding the relevant information. Take a dataset in the form of audio for example. Without metadata and the management from it, it would be impossible to us to find the location of the data. This also applies to data types such as images and videos.
• In using the data, metadata gives us a better understanding of how the data is structured, definitions of the terms, its origin (where it was collected, etc.)
• In re-using data, metadata helps annotators navigate the data. In order to reuse data, annotators are to have careful reservation and documentation of the metadata.

The key to making all of this happen is data annotation. Adding metadata to datasets helps detect patterns and annotation helps models recognize objects.

With all the benefits of metadata in how we can manage and use the datasets, many firms now have grown interested in developing metadata for better management.

 

Workforce of SMEs

The rapidly growing number of the industries embracing AI, a subject-specific data annotation team is of urgent needs. 

For every domain such as healthcare, finance, automotive, etc. a team trained with custom curricular will be deployed on projects, hence expert annotators built over time. With this being done, more value and high-quality to the annotation process will be focused with a deeper approach, and this strategy will start with the validation of guidelines to time of data delivery.

 

Do you want to deploy these data annotation trends? Come and contact LQA for further details:

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Fanpage: https://www.facebook.com/LotusQualityAssurance