Beginner’s Guide to Task Automation: Automate Repetitive Tasks and Scale Your Business

Over time, tools that act as fundamental advancements became more complex, especially for people who have no experience with writing code. This article will give you an in-depth introduction to task automation.

• How to automate repetitive tasks? 
• How can task automation benefit your business? 
• What are the task automation examples and best practices?

Read on to get more insights and scale your business with robotic desktop automation. 

What is automation?

Desktop automation is a process that requires minimum human effort. Automating tasks allows you to reduce the amount of human effort wasted on the task, cut time, and make the process error-free. Considering the International Federation of Robotics report, 70% of workers believe automation will offer more opportunities to engage in strategic work.

COVID-19 and its influence on the adoption of automation

The COVID-19 pandemic provides us with a sudden glimpse into a future world, where digital has become central to every interaction, forcing both organizations and employees to adopt automation almost overnight. The world has changed.

Digital channels became the primary customer engagement model, and automated processes became a primer driver of productivity and the basis of flexible, transparent, and stable supply chains. In this new world, agile working methods are a prerequisite to meeting daily changes to customer behavior. 

For example, the digitalization of support functions is another pivotal lever for improving efficiency. By automating repetitive tasks such as indirect purchasing, finance, legal, and HR, you can quickly minimize costs and free up time to invest in more practical activities. Companies that have automated their claims collection and financial reconciliation realized that they also increased the agility and accuracy of these processes.  

According to the McKinsey & Company report, The Next Normal: The Recovery will be Digital, “The majority of the benefits may come not from reducing labor costs but from raising productivity through fewer errors, higher output, and improved quality, safety, and speed.” Understanding which activities can be automated could provide an opportunity to rethink how employees engage with their jobs. Also, it’s a chance for top managers to automate their routine tasks and free up time to focus on the things that no algorithm can replace.

Benefits of task automation

Usually, organizations have some routine, repetitive tasks that take a lot of time and human effort. Automating such tasks allows you to eliminate errors and speed up the whole process. You may wonder if ‘That’s it? That’s all how automating tasks can benefit me?’ Of course, no, that’s just the tip of the iceberg. Let’s have a closer look at the benefits of task automation, as identified by WinTask.

1. Increased productivity

Automating particular processes gives you a chance to optimize the service provided and the costs of the organization. With employees having more time focusing on complex tasks that only humans can perform, you increase your income and improve the effectiveness of your workforce. 

2. Reduced errors

When your processes are automated, the chances of making errors decrease dramatically. When the procedures are familiar for the employees, it is easier for them to perform their job correctly. Eventually, you get processes that give you the best end-result. 

3. Reduced time in task execution

One of the main advantages of automating tasks for companies is that software tasks take less time than the ones performed manually. All you have to do is to schedule a task that should be repeated, and from then on, it will be repeated accurately, taking less time and eliminating errors.

4. Improvements in internal communication

Sometimes it’s really tough for a person to organize between meetings, emails, notes, and reminders. With workflow automation, communication within the company becomes simpler. When everyone knows the work that colleagues are developing, the communication between departments becomes easier.

5. Integrated systems

Systems can become more effective if integrated through automation. For instance, if you integrate payment software with some financial management application using API, it will be much easier to analyze the company’s state.

6. Reduced operating costs

Automating repetitive tasks generates a lot of savings. For example, it eliminates errors, reduces time to perform specific tasks, and requires fewer employees than the task usually would need. Correct automation allows you to speed up the process from the day being.

Common features of desktop automation

Here are some examples of what can be automated with the help of software robots.

1. Excel and database automation

Manipulate and transform data for excel or database automation. Automate data entry, transfer data between spreadsheets or different databases. To make setting up more accessible, you can record your actions and let the automation software replay them for you. Also, you can automatically run queries and extract data from the web to Excel spreadsheets.

2. Task scheduling

That’s a great feature to keep everything under control: you can schedule logins and automate other web tasks. Create a list of actions to automate and stick to it. This feature can save you hours by running scripts automatically. Set it up (Windows logins, for example) and comfortably go about your business. RDA tools will do everything for you.

3. Web data extraction

With this feature, you can easily extract unstructured data from web pages and convert it into an Excel file or database. Thanks to RDA, you can do it error-free and much faster than a person could do it manually.

4. Implementation of task automation  

It’s never too early to prepare for the future. At first, identify the problem you need to solve with automation. Think about the tasks your employees perform daily and consider automating them. Especially if your employees are struggling with repetitive tasks spending too much time completing them, desktop automation is right for you.

Businesses that use automation software report high customer and employee satisfaction. With no complex integrations or modifications to your current systems, any company can deploy RDA in a matter of days. RDA tools give immediate benefits realization, fast payback, and high ROI.

To get more in detail, you can use different task automation tools in various industries. According to Harvard Business Review, ‘60% of existing US jobs, 30% or more of current work activities can be automated with available or announced technologies.’ For instance, maintaining data consistency is quite a tedious task that sales representatives tackle daily.

First, the salespeople should enter data into CRMs; finance analysts must replicate the data and enter it into another system. Eventually, this results in duplication, errors and impacts productivity. Desktop automation helps avoid repetitive tasks and eliminates errors, performing end-to-end sales activities like data entry and invoicing. 

Conclusion

If one considers desktop automation versus RPA, there is no doubt that desktop automation is the perfect choice for companies that are just beginning to automate desktop applications. That’s totally fine to start small, automating one process at a time. This way, you can determine how desktop automation can help you scale your business.

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Benefits of Hiring IT Consulting Services

IT consulting services have become a prominent part of the business culture across the world. A consultant is synonymous with niche expertise, outsourced in the hour of need for perspective. This has allowed companies to compartmentalize their core business activities by saving time and improving business sustainability. The technology transformation wave growing over the recent years has made IT consulting services a business necessity. Technology has transformed the way businesses now operate. 

With the versatile and ever-changing trends in IT, maintaining a full-time, in-house IT department happens to involve great costs. An IT solutions consulting services company provides efficient solutions with low costs and higher Return On Investment (ROI). Therefore, many small or medium scale businesses take up IT consulting services for small business companies for their IT-based work. The world may see a big shift within the consulting industry in the next 5 years, taking into account its massive contribution to the global economy. 

Types of IT Consulting Services

There are plenty of categories of IT consulting services examples ranging from helpdesk services to data backups. No matter small or large, businesses are always on the lookout for finding smarter technology solutions to incorporate. Both IT products and services help companies to grow their ventures in existing and newer domains.  

Some of the most popular types of IT consulting services are:  

• Enterprise IT Consulting Services

Enterprise IT consulting services include Enterprise Resource Planning (ERP), a business process management software that allows an organization to use integrated applications. It helps businesses automate the various functions- technology, services, and human resources, thus reducing the cost of manpower.  

• Cloud Services

Cloud Services lead the IT consulting services list due to its increasing demand across all businesses. They are not only budget-friendly but also ensure high performance and efficiency. With the help of cloud services, businesses have access to everything they need on the go without having the burdens of physical data storage. This is one of the best IT consulting services for small businesses which run on lower operational costs.  They also provide better security and control strategies to the companies. 

• Help Desk IT Services

All offices have IT support equipment such as printers, servers, scanners, etc. These often happen to be the road bump in day-to-day operations, thereby responsible for the low productivity of office spaces. With the Help Desk IT services helping with installation, set-up, maintenance, and repair of the office equipment.  

• Network Security

 Data is the heart of any business. It is one of the most valuable resources and forms the foundation for decision making, strategy planning, and business growth plans. The data is shared among company stakeholders, and hence, network security and cyber data protection are of utmost importance. 

5 Benefits of Hiring IT Consulting Services

The IT consulting services list is not limited to the ones mentioned above. It includes a range of other IT consulting services examples like data storage and management, data backup services, social media consulting, repair services, web designing services, to name the popular few. 

According to the U.S. Small Business Administration, IT consulting services in the USA have proven to scale up small businesses without creating additional overheads. IT solutions consulting services have a huge financial and logistical impact on businesses, especially small businesses. An outsourced IT consulting services company reaps many benefits such as:  

• Cost and Time Saving

In comparison to using an in-house IT department, an outsourced IT consulting team reduces costs on the employee training, taxes, employee benefits, and overheads incurred. A substantial amount of money is also saved on lost downtime, prevented by quick detection and problem-solving. According to a Forrester Research study by BCM Software, “the routine IT problems across all employees cost Fortune 100 companies more than $100 billion dollars annually.”

On the other hand, using an IT consultancy service comes with the option of pay-py-project or pay-by-hour. Such payment flexibility also benefits small businesses to control and optimize their costs.  

• Expertise and Experience

The decision to hire an IT consultancy is promising because of its company’s collective knowledge and domain expertise. IT consultancies employ experienced IT experts who hold expertise in various IT fields across industries.

This attribute of cross-functional and industry-wide knowledge provided by IT consultancies ensures top-notch technology development to companies. They ensure that their clients have the most effective and latest technology as per market trends. As long as businesses choose the right IT consulting services, they are always one step ahead of their competitors. 

• Higher Productivity, Higher Sales

Technology helps in improving business productivity by allowing communication, collaborations, and knowledge sharing. When the best technology practices are properly planned and implemented, true productivity is achieved by companies. This improves the quality of work for its employees, and in turn, makes them more productive.

On the parallel road, to improve customer relationships, companies turn up to IT consultancies to effectively manage big data and reach their customers. Turning to big data is a daunting and time-intensive task which the service providers do very efficiently. Businesses use this service to come up with effective marketing campaigns and better audience targeting methods. This, in turn, leads to a substantial boost in company revenues.  

• Network Security

In the growing era of technology, even technology breaches are growing at a rapid speed. Cyber attacks and security threats are happening across all business sizes, industries, and countries. By outsourcing IT consulting services that hold expertise in internet security, companies can rely on them and better focus on the core business activities. IT consulting companies also provide best-in-class training to their employees for the technology they are working on and the prospective threats to it.  

• Round-the-Clock Availability 

In today’s world, there is no buffer for downtime. Security threats and cybercrimes occur at all hours of the day and night. Businesses must constantly be vigilant and available at emergency notice.

An IT consulting service takes on the responsibility for this and is a call away to its clients. They also provide continuous monitoring that helps detection of threats much before they cause harm to the business. IT consultancy provides conduct fixes and upgrades during odd hours (night and weekends) so as to keep the business productivity intact.

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What COVID-19 Has Taught Us About the Importance of Digital Transformation in Business

The Corona Virus (COVID-19) has taught us many hard lessons, not the least of which is the importance of being prepared, the importance of anticipating what may be just around the corner, and the importance of being able to theorize, hypothesize and predict. All of these skills are amply supplied by data scientists and analysts. But, in today’s world, it is crucial to prepare and educate the average business team member to improve data literacy and engage in digital transformation (Dx). As the idea of data popularity becomes more mainstream, and team members and industries embrace data sharing and fact-based decision making, our understanding of looming issues, collaborative possibilities and possible solutions to address these issues will improve and so will our response time and efficacy.

There is nothing like a global crisis to incite panic but, when you are facing large, multi-faceted issues, it is important to work in an environment that is prepared to provide the data and the views you need to address the issues you face. In businesses and government agencies where Digital Transformation (Dx) is ongoing, the business has a much better foundation for understanding financial and geo-political issues, security problems, weather-related emergencies and delays and the effects of these problems on stakeholders and supply chain. Data, analytics and an agile Dx workflow and environment weighs heavily on success in these instances.

As long as we are using COVID-19 as an example of a complex issue that requires rapid response, the ability to plan in an uncertain future and the need to work with facts to ensure appropriate action, let’s dive into this issue in a bit more detail. As we work through this example, consider your most crucial business issues, the changing market in which you compete and your need for timely, accurate data. You will find the comparisons to be thought provoking!

A recent survey revealed that:

• Nearly 50% of businesses are using data analytics more now than they did before the pandemic. They use the data to plan and address issues of revenue decline, resources issues etc.
• Small businesses (those with 200 or fewer employees) are using analytics and data even more than large enterprises. This is not really surprising, since small businesses have less fat to cut and are at more risk of losing customers and team members during this crisis.
• Over 60% are using analytics in operations, 50% are using it for sales, over 40% are using it in product support and over 55% are using it in financial support.
• Over 50% of all companies are using data to improve efficiency and address resources and workflow issues.
• Over 60% of all companies are increasing or maintaining their data analysis budget. That might surprise you, but it shouldn’t. There is nothing more motivating than a global crisis and, in the case of ongoing business management, there is nothing more motivating than dramatic changes in the market or in customer response to products and services.

Investing in Digital Transformation, technology infrastructure, systems, software and user data literacy ensures that the business is working with up-to-date, accessible systems and tools that are easy for team members to embrace so every team member is empowered and accountable for data-driven decisions and recommendations. When a crisis hits, e.g., cyber-attack, significant market shift or competitive initiative, OR loss of customers or revenue to an economic downturn or a global pandemic, a business must have the tools, processes and technology in place to respond with agility and speed. During such a crisis, there is no time to implement changes that will take months or years to execute.

So, what have we learned from COVID-19? A lot. But, perhaps the most critical lesson for businesses trying to survive or emerge from this pandemic is that digital transformation and improved data literacy are initiatives for NOW. If your business wants to be prepared to address the lingering after effects and permanent market changes that will result from this pandemic, the time to implement digital transformation (Dx) and data literacy initiatives is NOW!

If your business is trying to survive and thrive during these significant market and business shifts, it must build a cohesive, stable foundation to help the business succeed and thrive in uncertain times. For more information about Digital Transformation explore our white papers: ‘Understanding Digital Transformation: What it Is, What it is Not’, ‘Preparing Your Business for Digital Transformation and Data Literacy.’

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Deep Learning and Google Search: The Future of SEO

How Deep Learning is Changing the Future of SEO

Algorithms have become smarter. They’re learning what websites you frequent, why you typed a certain query and what other search suggestions you’re likely to click. Behind the online results you’re seeing is a highly advanced form of AI: deep learning.

It’s based on artificial neural networks that allow the machine to learn without human supervision. Deep learning requires more data because neural networks need to process more information to be more efficient, to gain better insights — to better answer queries.

So how does it change the way Google does its work and what does it mean for SEO?

The Impact of Deep Learning on SEO

With the help of deep neural networks, machines can now analyze vast amounts of data and learn more tasks. Features like identifying photos, accurately recognizing voice commands, and correctly interpreting search queries will be done faster, and at a much bigger scale.

Google’s RankBrain was one of the first algorithms that exhibited deep learning. It’s able to understand the user’s search intent, resulting in more accurate search results. But this is just the beginning.

In an interview with former Google search engineer Edmond Lau, he stated that with machine learning, it’s hard to explain why a particular search result ranks higher than the other. Because the algorithm and ranking signals were designed by humans, it’s difficult to tweak machine-learning based systems to boost certain ranking signals. Engineers are still required to tweak the algorithm whenever they discover a loophole.

With deep learning, they are expecting AI to take over the tweaking. Deep learning technology is expected to achieve more accurate results. But the engineers won’t always be able to explain what led the machine to come up with the result.

Ultimately, what Google aims to do is create successful searches that satisfies users. No matter what pattern deep learning technology discovers, a website that satisfies user queries is more likely to clinch marketing success.

What SEOs are Doing

Businesses see a bright future with deep learning. But SEO experts and SEO companies may be anxious at the probability that machines may figure out unique algorithms that deviate from the normal practice and SEO techniques.

As with any top trending technology, marketers will need to adapt to deep learning.

How are other SEOs adjusting to this search innovation?

Focusing on User Experience

The goal for any business is to make customers happy. This goal is no different from Google’s aim of continually improving the user experience. Although the rules of ranking will change, the goal and purpose will not. 

As search engines become smarter, an SEO company or professional will need to focus on providing the best user experience to make sure that they keep attracting the right visitors, persuading them to return to the website.

How do you get this result?

Make sure your web design is responsive, and that it loads fast. Over 5 billion people in the world are unique mobile users, which is about 66 percent of the global population. Site owners who optimize for mobile will be the ones who will come out on top following the upcoming updates.

Targeting Long-tail Keywords

Now that people have the ability to talk to their devices and issue voice commands, it will be smart to target conversational keywords or long-tail keywords. This approach creates a more targeted strategy. It will require you to dive deeper into your audience’s psychographics to know what they’re asking and what they expect.

Producing Relevant and Quality Content

Always create fresh and updated content for your website. No matter the update, both users and Google will prioritize websites that churn out quality and valuable content. One of the main goals of algorithm updates is to sift out irrelevant and low-quality materials.

Deep Learning in the Beginning

In 2014, Google acquired DeepMind, a British company specializing in deep learning technology. Fast forward two years later, AlphaGo, an algorithm using Google’s deep learning technology, was able to beat the champion of the world’s hardest game, Go. A historic moment in the world of AI.

So what exactly happened there, and why was it so monumental? 

The ancient game of Go is seen as the most challenging game to master because the strategy and objectives are endless, and it’s a much more intuitive game compared to chess — which is more logical. A single game has about 250150 or 10360 probable moves, compared to chess, which has 3580 (or 10123). 

This makes it the ideal game to test deep learning since there’s no way to input all the moves into the system, which equates to the number of atoms in the universe. To beat a human, the machine has to learn and absorb new patterns and data to come up with the next move. Mimicking the process of the human brain.

Although machine learning functions to address a predefined purpose, deep learning technology like AlphaGo is not pre-programmed and it learns from experience. Developers and data scientists then use reinforcement learning, feeding it all the information it needs to learn and process new patterns.

The technology can be used in many industries, including healthcare, science, electronics and media.

The Future is in Deep Learning

Deep learning is still in its infancy, so more will be discovered about it. The technology may come up with new ways of helping search engines filter and rank websites. The major factors we’re seeing today, like anchor texts, page speed and meta descriptions may be factors that might not matter so much in the future. 

So what’s our takeaway?

• Adapt and learn as deep learning evolves; inaction is bad for business
• Never lose sight of user experience as you’re refining your strategies
• Consistently produce engaging and valuable content

New technologies will continue to be developed. And they will continue to disrupt and improve the way you market your business. The key is to make sure you’re always prepared for the future.

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Potential Risk AI can cause to the world!

Source: Google

Artificial intelligence is among the most talked-about trends in recent years, and It is a 2 side blade with positives and negatives. Talking about positives, technologies are starting to improve our lives in multiple ways, from enhancing the healthcare experience, shopping and also making the businesses undeniable. 

According to Statista, The market research firm IDC projected that the global AI market would reach a size of over half a trillion U.S. dollars by 2024. It is obvious to say that AI has a long way to go and benefit the industry with its brilliance. 

Yet AI can be beneficial for all industries, but it can also give rise to unwanted threats and serious consequences. In this article, I will be discussing some of the problems that AI can cause in the future, or someone might misuse it in different ways.

Let’s dive into that without killing much time: 

1. Data misuse

Ingesting, linking, sorting, and using data properly has become a lot difficult as disorganized data being ingested from sources such as the web, sensors, mobile devices, and the IoT has increased. 

All the information coming from the data can cause threats or problems. Victims can easily fall into the pitfalls created by threatening them about revealing their hidden data or sensitive information to the world. A lot of scams can happen by using this technology and develop nuisances in the world.

To solve this issue, AI has made several AI Tools that can help in reducing and eliminating data misuse: 

• AI in EndPoint Data Protection
• AI in protecting the privacy
• AI-powered data protection solution
• AI in Protection Data from phishing and social Engineering 
• AI in the protection of data from Malware/Ransomware/APTs

2. Safety Problem

Experts like Elon Musk, Bill Gates, and Stephen Hawking have already warned and are concerned about safety and asked to pay attention to its safety issues. 

There are various instances where AI has gone wrong; for example, Facebook AI bots started interacting with each other in a language no one else would understand, leading to the project shut down.

There are possibilities in which they can harm humankind, and in the case of autonomous weapons, they can be programmed to kill other humans.

There are few things that need to be taken care of: 

• We need to have strong regulations, especially when it comes to the creation or experimentation of Autonomous weapons
• Global Cooperation on such kinds of weapons is required to ensure no one gets involved in the rat race.
• Complete transparency in the system where such technologies have experimented is essential to ensure its safe usage.

3. Interaction issues

The interface between machines and people is another critical risk area. Among the most evident are challenges in automated transportation, infrastructure systems, and manufacturing.

Accidents and injuries are possible if operatives of heavy equipment, vehicles, or other machinery do not understand when systems should be overridden or if they are late to override them because the operator’s attention is elsewhere – a clear possibility in applications such as autonomous cars. 

In contrast, human intelligence can also be defective in prioritizing the results of the system. Behind the pictures, in the data analysis organization, script errors, data management failures, and errors in judgment in model training data can easily compromise fairness, privacy, security, and safety compliance. 

Accidents caused by AI Autonomous cars are mainly due to lack of data given to them, AI performs well when it is served with a lot of informative data, which helps them become more decision-making. 

4. AI Models Misbehaving

AI models themselves can cause many problems when they provide biased results ( which can occur, for example, if a population is underrepresented in the data used to train the AI model).

They become unstable, or they draw outcomes for which there is no Actionable recourse for those influenced by your decisions (such as someone who was refused a loan without knowing what they could do to change the decision).

Consider, for example, the potential for artificial intelligence models to accidentally discriminate against defended classes and other groups by combining zip code and income data to create targeted offers. 

Harder to spot are cases where AI models lurk in software-as-a-service (SaaS) offerings. When merchants introduce intelligent new features, often with little fanfare, they are also proposing models that could communicate with data on the user’s system to generate unexpected risks, even lead to hidden vulnerabilities that hackers could exploit. 

The assumption is that leaders who think they are protected if their organization has not purchased or built artificial intelligence systems or are only experimenting with their implementation could be wrong.

You can control the AI models by applying the following things: 

• Transparency and Interpretability
• Feature Engineering
• Quality Control
• Hyperparameters
• Model Bias
• Model governance

After deploying the data into your Machine Learning Application, it is beneficial to review and reinforce your model for sustaining the changes in environments, data, and actors. And this is very important in moderating the risk associated with ML. 

Conclusion

All the things listed above can be hazardous for humankind, as many AI models can misbehave or be programmed to harm humans. To be secure and safe, try to develop AI solutions better and always try to benefit the world and make the world better.

Author Bio: Ved Raj is the business analyst at ValueCoders (https://www.valuecoders.com/), Which provides consulting and AI-based solutions to high-tech companies in the tech and digital industries.

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Top Technology Aspects Shaping Online Delivery Business Across The World

Technology is transforming the world to be a better version of itself, and it has made a revolutionary impact. People have admired the technology as it has given them a better lifestyle by offering comfort and conveniences. The way the internet world has grown and taken charge by offering various services it offers has grabbed the attention of people. One thing is sure: the internet and technology are a brutal combination that this generation of people won’t be able to live without. Thus, technology is an important aspect these days for all aspects. 

The combination of the internet and technology has given birth to the modern-day business concept, like an online delivery business that is running successfully. The online delivery business concept is getting popular all over the world as it is a flexible and convenient mode of achieving business targets for business people. At the same time, people are enjoying the perks of online services and getting doorstep deliveries. 

Currently, food delivery service remains at the top of the chart in the online delivery business, which amounted to US$151,526 million in 2021. Food delivery service is something that has grabbed attention and popularity earlier, but with time, most businesses have started to go online and deliver doorstep services. 

Artificial Intelligence 

Artificial intelligence is a demonstrated intelligence performed by machines that try to clone the factors like how humans will think and adapt to certain situations. Consciousness and emotions are the crucial elements in artificial intelligence. Artificial is a technology trend that can be proved beneficial in the online delivery business. It can be beneficial in forecasting the demands and betterment of services. 

AI can be very helpful in recommending products and services to potential customers. Also, AI-powered services are helpful in offering better service to customers, which is a good advantage. According to Mckinsey, AI industries are currently valued at around USD $9-$15 Trillion, which is assumed to grow more as more businesses are going digital. Thus, Artificial intelligence is one of the technologies that can take online delivery service to ultimate heights and success. 

Internet of Things

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The Internet of Things is an impactful technology that has created a massive impact on the business sector. It is the technology that enhances the connectivity of all physical devices with sensors and advanced software. It is a development business infrastructure using intelligent and digital technologies to automate various tasks like tracking and safety and running a business online. The data sharing is manifestly done using this technology over the vast network connected with each other. Thus, it is a great and useful technology, especially when you want to run an online delivery business. 

App development is one of the important elements that is required when you are going online for your delivery business. The Internet of Things is nothing but taking your business online through this digital platform that connects things easily. The food delivery service, one of the popular amongst delivery services, has pioneered this concept. Like food ordering app development, any other delivery business can take advantage of it and have their app one of the mains in IoT infrastructure. 

Drone Delivery

Drone delivery is the latest concept that can be incorporated into the online delivery business. This delivery system will take a step ahead in the enhanced online delivery service. The concept of drone delivery will be more flexible and helpful and will greatly impact the online delivery business. In these fast-paced lives, people have become more demanding, and they need everything quick and exclusive. Thus, drone delivery is the concept that will fulfill that demand for people. 

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The business people will have the advantage of offering excellent service to customers and won’t require much manpower to perform delivery tasks. There are many benefits of introducing drone delivery concepts in the online business, and one of the advantages is completely automating the delivery services. According to reports, the drone delivery market, which is around 500 Million USD, is expected to grow at a rate of 42% in the upcoming years. Thus, drone delivery is the latest technology aspect that will shape the online delivery business. 

Big Data

Big data is one of the technical aspects that most businesses are heavily focusing on these days. It is undoubtedly one of the technologies that can do wonders for your online delivery business. Businesses are going digital because of the advantage of having databases. The online delivery concept in the business will require a digital platform like mobile app and websites. Digital technologies will allow business people to collect and store essential data related to business, including customer data and profile. 

These data are very helpful for businesses, and delivery businesses can use it to know customers’ interests by knowing past buying patterns. Business people can convince and recommend products using this technology. It can also be used for carrying analysis that is beneficial for the business. 

Robotics

Robotics and automation are emerging technologies that will rule in the coming years. Some of the players in the industries have already proposed the robots in their delivery service for a trial basis. With the help of this technology, the delivery system can be automatically managed and performed. The robots will deliver the products and by finding the route of the client’s address. These smart technologies can be very helpful in changing the dynamics of the online delivery business, and business people will be relieved using such technology. 

Robotics is a technology that is still in the initial phase, but it has a strong potential and capability to change the market and business sector scenario. The good thing about such technology is that it is accurate, and there is no possibility of human errors. With enough testing, there is no chance of technology error, and it is reliable. 

Ending Note

The businesses are being run online due to the concept of online delivery service. It is their backbone and gives flexibility. The online delivery service is getting popular day by day because people find it very convenient. Technology is advancing these days, making it more popular and one of the most effective elements for the growth of the business. These technologies are constantly backing the online delivery systems and making them more effective and efficient. The unimaginary and revolutionary impact these technologies have created in the business sector is ultimately going to be helpful to the entire global population, and this is how it goes on.

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DSC Weekly Digest 10 May 2021

Return to Work vs. Return to the Office Framing is a technique that communicators (and marketing people) use to push unstated, but heavily implied, messages into the discourse as part of an implied context. The framing in the above title caught my eye last week, and I think it’s worth discussing. Vaccinations are ongoing worldwide. Here in Washington State, due largely to politics at the federal level the last couple of years, the rollout has been slower than most would like because the supply of available vaccines has been limited. However, it is now proceeding apace (I finally got my own family vaccinated a week ago now). One consequence of this has been that the pundits are now talking about how we are all preparing to “Return to Work”. Here’s that framing I was talking about. One thing that has been so remarkable about the last year is that, despite having business patterns totally disrupted, people leaving the office had a surprisingly small overall impact upon productivity, except perhaps to improve it slightly. I’ve been working pretty steadily throughout most of the pandemic, sometimes clocking in more than sixty hours a week. I never stopped working. What did change was that as a society we proved, conclusively, that the need to go into an office, to spend two hours a day commuting, eight hours dealing with overloud conversations, interminable meetings, and the smell of burnt popcorn, was simply not there. Indeed, the more data-driven an organization, the less real need exists to cram people into the same building, watercooler conversations notwithstanding. Corporate executives need to be preparing now for the post-Covid era, where many people are going to be very reluctant to go into the office because the need to do so is simply no longer there. Yes, it is a good idea to pull together people periodically to both establish esprit d’ cor and brainstorm, but the era where everyone is crammed together in “open office” arrangements is probably now past. Navigating this new reality is likely to be a major challenge, one where managing the data involved in working will play a huge part. This is why we run Data Science Central, and why we are expanding its focus to consider the width and breadth of digital transformation in our society. Data Science Central is your community. It is a chance to learn from other practitioners, and a chance to communicate what you know to the data science community overall. I encourage you to submit original articles and to make your name known to the people that are going to be hiring in the coming year. As always let us know what you think. 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How to Train a Joint Entities and Relation Extraction Classifier using BERT Transformer with spaCy 3

Introduction

One of the most useful applications of NLP technology is information extraction from unstructured texts — contracts, financial documents, healthcare records, etc. — that enables automatic data query to derive new insights. Traditionally, named entity recognition has been widely used to identify entities inside a text and store the data for advanced querying and filtering. However, if we want to semantically understand the unstructured text, NER alone is not enough since we don’t know how the entities are related to each other. Performing joint NER and relation extraction will open up a whole new way of information retrieval through knowledge graphs where you can navigate across different nodes to discover hidden relationships. Therefore, performing these tasks jointly will be beneficial.

Building on my previous article where we fine-tuned a BERT model for NER using spaCy3, we will now add relation extraction to the pipeline using the new Thinc library from spaCy. We train the relation extraction model following the steps outlined in spaCy’s documentation. We will compare the performance of the relation classifier using transformers and tok2vec algorithms. Finally, we will test the model on a job description found online.

Relation Classification:

At its core, the relation extraction model is a classifier that predicts a relation r for a given pair of entity {e1, e2}. In case of transformers, this classifier is added on top of the output hidden states. For more information about relation extraction, please read this excellent article outlining the theory of fine tuning transformer model for relation classification.

The pre-trained model that we are going to fine-tune is the roberta-base model but you can use any pre-trained model available in huggingface library by simply inputting the name in the config file (see below).

In this tutorial we are going to extract the relationship between the two entities {Experience, Skills} as Experience_in and between {Diploma, Diploma_major} as Degree_in. The goal is to extract the years of experience required in a specific skills and the diploma major associated to the required diploma. You can of course, train your own relation classifier for your own use case such as finding the cause/effect of symptoms in health records or company acquisitions in financial documents. The possibilities are limitless…

In this tutorial, we will only cover the entity relation extraction part. For fine-tuning BERT NER using spaCy 3, please refer to my previous article.

Data Annotation:

As in my previous article, we use UBIAI text annotation tool to perform the joint entity and relation annotation because of its versatile interface that allows us to switch between entity and relation annotation easily (see below):

                                            UBIAI’s joint entities and relation classification

For this tutorial, I have only annotated around 100 documents containing entities and relations. For production, we will certainly need more annotated data.

For training, we will provide the entities from our golden corpus and train the classifier on these entities.

• Open a new Google Colab project and make sure to select GPU as hardware accelerator in the notebook settings. Make sure GPU is enabled by running: !nvidia-smi
• Install spacy-nightly: !pip install -U spacy-nightly –pre

• Install the wheel package and clone spacy’s relation extraction repo: 

           !pip install -U pip setuptools wheel

            python -m spacy project clone tutorials/rel_component

• Install transformer pipeline and spacy transformers library:

 !python -m spacy download en_core_web_trf
!pip install -U spacy transformers

• Change directory to rel_component folder: cd rel_component
• Create a folder with the name “data” inside rel_component and upload the training, dev and test binary files into it:

                                                                  Training folder

• Open project.yml file and update the training, dev and test path:

           train_file: “data/relations_training.spacy”dev_file: “data/relations_dev.spacy”test_file: “data/relations_test.spacy”

• You can change the pre-trained transformer model (if you want to use a different language, for example), by going to the configs/rel_trf.cfg and entering the name of the model:

 [components.transformer.model]@architectures = "spacy-transformers.TransformerModel.v1"name = "roberta-base" # Transformer model from huggingfacetokenizer_config = {"use_fast": true}

• Before we start the training, we will decrease the max_length in configs/rel_trf.cfg from the default 100 token to 20 to increase the efficiency of our model. The max_length corresponds to the maximum distance between two entities above which they will not be considered for relation classification. As a result, two entities from the same document will be classified, as long as they are within a maximum distance (in number of tokens) of each other.

 [components.relation_extractor.model.create_instance_tensor.get_instances]@misc = "rel_instance_generator.v1"max_length = 20

• We are finally ready to train and evaluate the relation extraction model; just run the commands below:

 !spacy project run train_gpu # command to train train transformers
!spacy project run evaluate # command to evaluate on test dataset

            You should start seeing the P, R and F score start getting updated:

                                                                              Model training in progress

After the model is done training, the evaluation on the test data set will immediately start and display the predicted versus golden labels. The model will be saved in a folder named “training” along with the scores of our model.

 import spacynlp = spacy.load("NER Model Repo/model-best")Text=['''2+ years of non-internship professional software development experience Programming experience with at least one modern language such as Java, C++, or C# including object-oriented design.1+ years of experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems.Bachelor / MS Degree in Computer Science. Preferably a PhD in data science.8+ years of professional experience in software development. 2+ years of experience in project management.Experience in mentoring junior software engineers to improve their skills, and make them more effective, product software engineers.Experience in data structures, algorithm design, complexity analysis, object-oriented design.3+ years experience in at least one modern programming language such as Java, Scala, Python, C++, C#Experience in professional software engineering practices & best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operationsExperience in communicating with users, other technical teams, and management to collect requirements, describe software product features, and technical designs.Experience with building complex software systems that have been successfully delivered to customersProven ability to take a project from scoping requirements through actual launch of the project, with experience in the subsequent operation of the system in production''']for doc in nlp.pipe(text, disable=["tagger"]): print(f"spans: {[(e.start, e.text, e.label_) for e in doc.ents]}")

 spans: [(0, '2+ years', 'EXPERIENCE'), (7, 'professional software development', 'SKILLS'), (12, 'Programming', 'SKILLS'), (22, 'Java', 'SKILLS'), (24, 'C++', 'SKILLS'), (27, 'C#', 'SKILLS'), (30, 'object-oriented design', 'SKILLS'), (36, '1+ years', 'EXPERIENCE'), (41, 'contributing to the', 'SKILLS'), (46, 'design', 'SKILLS'), (48, 'architecture', 'SKILLS'), (50, 'design patterns', 'SKILLS'), (55, 'scaling', 'SKILLS'), (60, 'current systems', 'SKILLS'), (64, 'Bachelor', 'DIPLOMA'), (68, 'Computer Science', 'DIPLOMA_MAJOR'), (75, '8+ years', 'EXPERIENCE'), (82, 'software development', 'SKILLS'), (88, 'mentoring junior software engineers', 'SKILLS'), (103, 'product software engineers', 'SKILLS'), (110, 'data structures', 'SKILLS'), (113, 'algorithm design', 'SKILLS'), (116, 'complexity analysis', 'SKILLS'), (119, 'object-oriented design', 'SKILLS'), (135, 'Java', 'SKILLS'), (137, 'Scala', 'SKILLS'), (139, 'Python', 'SKILLS'), (141, 'C++', 'SKILLS'), (143, 'C#', 'SKILLS'), (148, 'professional software engineering', 'SKILLS'), (151, 'practices', 'SKILLS'), (153, 'best practices', 'SKILLS'), (158, 'software development', 'SKILLS'), (164, 'coding', 'SKILLS'), (167, 'code reviews', 'SKILLS'), (170, 'source control management', 'SKILLS'), (174, 'build processes', 'SKILLS'), (177, 'testing', 'SKILLS'), (180, 'operations', 'SKILLS'), (184, 'communicating', 'SKILLS'), (193, 'management', 'SKILLS'), (199, 'software product', 'SKILLS'), (204, 'technical designs', 'SKILLS'), (210, 'building complex software systems', 'SKILLS'), (229, 'scoping requirements', 'SKILLS')]
We have successfully extracted all the skills, number of years of experience, diploma and diploma major from the text! Next we load the relation extraction model and classify the relationship between the entities.

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The Rise of the Analytical CMO

In the post-Covid era, organizations are recalibrating their marketing strategies to make better use of data and analytics to stay ahead of the competition. Increasingly, it is the Chief Marketing Officer (CMO) who has to take the lead to drive digital adoption and become the organization’s digital evangelist. Data is everywhere and the CMO is increasingly expected to use multiple sources of data analysis and marketing intelligence for growth and revenue.

Customer buyer journeys changed significantly during the pandemic: Buyers prefer to undertake a self-education journey, learning about the product rather than engaging with a salesperson right from the start. This puts the onus on marketers to provide prospective customers with the right content at the right time via the right channel.

Also, as the Wall Street Journal reports, ad spend has shifted to digital with Google, Facebook and Amazon getting half of all US ad spend. Additional research by the Interactive Advertising Bureau and PwC indicates digital advertising grew by 12% year-over-year. With a surge of customer data available, CMOs had to respond to changes in the market in hours and minutes, not in days or weeks. To succeed in this dynamic environment, CMOs have made the shift to becoming more analytical. They now have a multidisciplinary toolbox of skills — including experiential, creative, and analytical, to gain insights to shape data-driven marketing, and business strategy. Data is a vital part of driving growth marketing.

Data-driven marketing: More than a buzzword

Every organization wants data-driven marketing and marketing leaders are faced with a flood of new customer data and insights that they are expected to use to shape their strategy. To see how this plays out in real life, take a look at wheelchair accessibility company Braunability: They were previously dealing with siloed data that was not easy to share and did not generate meaningful intelligence. With the help of the right BI solution, they were able to integrate sales, marketing, and logistics information to plan their promotions and new campaigns and evolve their marketing program. As marketing (in every industry) continues to change, becoming increasingly data-driven and with tighter and tighter margins, every CMO will be looking to up level their organization with the right actionable intelligence, delivered to the right users at the right place and time. Choosing a powerful analytics platform that can connect to disparate data sources and infuse insights into user workflows will be an important way companies separate themselves from their competitors and ultimately improve their marketing performance.

Infuse data into the creative process

Traditionally, the CMO had a creative mandate to think about how best to connect with customers. Decisions were mostly gut-driven and based on historical data. Today’s CMO must combine their creative thinking with marketing intelligence that reveals not only which ads convert best, but also customer triggers, unmet needs, and affinities which can unlock new opportunities.

A McKinsey survey of over 200 CMOs and senior marketing executives revealed that marketers who combine data and creative thinking drive more growth than those who don’t. The top-performing marketers consistently integrated four or more insights on average into the process of improving customer experience instead of the traditional approach of using analytics as a distinct and separate process.

Infuse intelligence from multiple sources into workflows to drive data adoption

Customer data resides everywhere, but it may not be in the most obvious of places. Chatbots, social media, voice search — these new sources of textual data contain valuable but untapped insights. The use of artificial intelligence in marketing, specifically natural language processing, can convert this rich textual data into valuable customer insights. When done right, it can give CMOs a significant competitive advantage: According to the Sisense-commissioned IDC Internal Analytics Survey 2020, 78% business leaders are already using AI in their BI tools, and the rest plan to start using it in the near future; 38% users are looking for a solution that offers natural language queries. Savvy CMOs who are already using artificial intelligence in marketing use it to track granular details of their customers and campaigns to optimize in real-time.

Analytics from this wide array of sources can be the CMO’s best friend when it comes to things like measuring ROI on marketing spend and other important KPIs. Building an analytics-driven team and culture is vital to that mission but going back to the IT team for insights repeatedly wastes time.

The solution: Infuse analytics into workflows. This bridges the gap for marketing teams by providing self-service, shareability, and real-time updated data right where and when users need it, without leaving their usual tasks to hunt for intelligence. According to the IDC survey, 61% of business leaders say incorporating analytics into their existing workflows is one of their biggest objectives while choosing third party solutions.

Bring customer insights to the C-suite to influence strategy

Challenges provide tremendous opportunities to grow. As organizations face fast-changing customer expectations due to Covid, it is the right time for CMOs to evolve their role. They can bring their analytics-driven customer insights to the boardroom to prove marketing’s measurable and strategic importance and help influence strategy.

To achieve this, a strong foundation of analytics is essential. With the right analytics solution, infused seamlessly into workflows, marketing leaders can develop an analytics-driven culture that leads to optimized campaigns and improved KPIs and ultimately impacts revenues. An agile analytics infrastructure is key to evolving your business.

AUTHOR BIO

Ashley Kramer is a senior executive with over 15 years of experience scaling hypergrowth companies including Tableau, Alteryx, Amazon, Oracle, and NASA. She has a strong track record of transforming product and marketing organizations and effectively defining and delivering the end-to-end product strategy and vision. Ashley is passionate about data, analytics, AI, and machine learning.

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Diagnoses Future of Global 5G Chipset through its Expert Lens

The smartphone and IoT connected devices sector has evolved over the years to a great extent and is creating ripples across the large consumer base with novel technological advancements seeping in. Internet connectivity forms a great part of generating good revenue for any player in the smartphone industry. Therefore, for extensive internet connectivity, various technologies are used that further have a positive impact on smartphone sales.

The 5G technology is seen as a boon for the growth of this massive smartphone and IoT connected devices industry. Faster speed, good bandwidth, and reduced latency are some of the features that 5G technology offers. For the smooth integration of 5G technologies with these devices, 5G chipset is of great importance. Therefore, the global 5G chipset market may witness overwhelming growth during the assessment period of 2019-2026.

According to Transparency Market Research (TMR), the global 5G chipset market is expected to expand at a CAGR of 44.01 percent through the forecast period of 2019-2026. The 5G chipset market is also expected to attain a valuation of US$ 20,195.8 mn by 2026.

Numerous vendors are in the fray for developing state-of-the-art 5G chipsets due to the growing influence of technology across the telecommunication industry. 5G chipsets are used in a plethora of end-uses such as media and entertainment, consumer electronics, energy and utilities, automotive and transportation, healthcare, and others.

Escalating Demand for Strong Network Infrastructure Boosting Growth Prospects

The advent and magnification of the Internet of Things (IoT) and other similar connected technologies have led to massive data consumption. Connected devices are also catering to an enormous consumer base, thereby inviting expansive growth opportunities for the 5G chipset market. The data consumption rate is prophesied to increase tenfold in the coming years according to various studies and surveys and this will give a Midas touch to the growth of the 5G chipset market.

Mid-Range Smartphones to Push Growth for 5G Chipsets

Mid-range smartphones may bring immense growth prospects for the 5G chipset market. Companies in the 5G chipset market are exploring opportunities in the cheap and mid-range smartphones. This segment is a hit in densely populated countries like China and India. Players are launching 5G chipsets for these smartphones. For instance, MediaTek launched Dimensity 800U that enables 5G capabilities for mid-range smartphones. Similar developments may help the 5G chipset market to garner growth.

Large-Scale 5G Deployment to Lay Red Carpet of Growth

5G-deployed cities may serve as a prominent growth contributor for the 5G chipset market. For instance, Shenzhen became the 1^st Chinese city that deployed full-scale 5G technology around the city. The city has installed over 46000 5G base stations and the mayor of the city also quoted that the number of 5G base stations in the city alone puts it in tandem with the number of 5G installation bases in entire Europe. Similar cities may bring exponential growth for the 5G chipset market.

Political Tensions between Certain Countries Hindering Growth of 5G Chipset Market

The changing political dynamics between some countries may dampen the growth of the 5G chipset market. The tussle between China and the U.S. is a recent instance. The U.S. government recently announced fresh sanctions that will ban any foreign semiconductor company from selling chips developed by using U.S. software to Huawei without obtaining a license from the government.

Qualcomm is in the process of convincing the US government for allowing selling 5G chipsets to Huawei. Qualcomm and Huawei had recently forged a long-term global patent license agreement. The U.S. government is trying to keep Huawei at bay out of next-generation networks citing national security concerns. Such aspects prove to be growth obstacles for the 5G chipset market.

Get More Research Insights about 5G Chipset Industry by TMR

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