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In the past two years, industries around the globe have seen differing trends in the way they operate. Businesses have sustained through the toughest of times and even though COVID took the world by storm, the presence of innovative technologies and breakthrough inventions have helped many companies stay abreast. Similarly, software development trends have emerged in such a way that industry experts are predicting a pattern in their development plans in 2022. [lwptoc skipHeadingLevel="h1,h4,h5,h6"]Given how companies stayed in their strategic mode to say afloat, experts have high hopes that things will keep looking up as long as managers stay persistent to move forward. As we have it through history, no one stays in a slump forever. Similar is the thought process of the information technology industry experts as they keep an open mind yet a cautious level of optimism as they keep moving forward. Coincidence? We think not! Companies that lie in the business of technology departments such as vendors, supply chain management, channel firms, and even distributors, vastly benefit from emerging software development trends to keep moving ahead in their fields.

Introduction - The Software Development Industry

Firms that are integrated with such functions internally – or even externally – are expected to increase their technology budgets in 2022, especially given how things have become highly subjective to technology innovation in the post COVID era. Firms are even training their existing staff, welcoming new members to take on existing positions to accommodate future business opportunities, without missing out on the opportunity to expand on their current portfolio of services offered. Software development companies have always had to keep up the pace even before the global pandemic hit, but now, as the remaining industries seem to have tracked down the need to catch up, tech companies seem to be running faster and faster to meet their needs. Because the race keeps getting tougher, many experts suspect that some practices even today may not be applicable till we reach the incoming year 2022. It really makes you think how pressed you are for time and how obsolescence really is the big monster right behind your back. Founders of small companies are increasingly training their employees to keep abreast with changes in the software industry and to think outside the box to stay one step ahead of their technological counterparts. Managers responsible for creative thinking and strategizing are increasingly accommodating app and software developments in their plans, forced to think of other ways to push their brand forward, integrating it with software developer stratagems. As business functions slowly seem to be getting fully integrated – backward and forward – IT experts advise businesses to keep an eye on the latest development trends especially in the year 2022 because things are expected to get bumpy as we collide head-on into the new year. The pandemic has forced companies to adopt new ways of getting themselves out and about into the market, pushing down the idea of constant upgrading that is required to survive. Organizations are now looking further than their traditional modes of operations, adopting innovative digital strategies, and even suggesting their own strategies that will incorporate or even encourage never ways of integrating software development trends into their existing operations. At this point, it is safe to say that innovative software has become a crucial part of every day, running the lives of businesses and managerial efforts. Experts reveal that companies are once again opening up and accepting operating challenges by collaborating more and more with their counterpart software developers. With data insights and analysis on the rise, managers are increasingly seeking out the help of software developers to provide simple, yet collaborative software that will extract insightful information to help them make decisions that are less risky and more profitable. Not just in the short run, but sustainable long-run goals are imperative for a business to operate in a stable environment. And what better way to do that than but be sure of your strategies from the get-go? Needless to say, our global village keeps getting tighter as our corporates huddle together. Experts are trying to come up with sustainable ways of doing business while maintaining their competitive edge and embracing cutting-edge technology to get ahead of the competition.

Software Development Trends to Prepare for in 2022

As we shift from traditional working spaces to remote ones – as witnessed in the past eighteen months or so – IT departments find themselves branching out and providing solutions to their employees to fit into the new normal. As of now, companies have come up with the need to maintain a central headquarters with satellite branches, as opposed to residential homes arranged in a spider web fashion. Though such structures were the need of the hour when companies first started thinking of alternatives to the pandemic situation but have become more permanent work systems. The work-from-home migration encouraged channel firms to collaborate with manufacturers anew to sell hardware and security-based devices while service providers found new opportunities to supervise and guide employees regarding nodes on networks and basic connection techniques.

Business Travel Innovation

Additionally, businesses have gone remote and Zoom! Business travel has been an important part of meetings and conferences for a long time, but once again, 2022 seems to be the year where things will still be somewhat hybrid. Industry experts think that the pandemic has completely changed the corporate landscape in a way that will never go back to the way it used to be. Business travel will still be different for employees and executives alike for the upcoming year, however, this time, the youth involved in the workplace seems to be enjoying this mixed fashion of working as managers and leaders have seen many innovative solutions in light of the recent global paradigm shift.

Budget Expansion

In the past, CIOs have been known to push companies for growth in the year-to-year budget allotted to the IT side of their companies. Experts believe this was due to the traditional thought and perception of what IT was. However, all it took was a pandemic for people to realize the potential this industry has for companies all over the world. As companies continue to spend more on technological solutions, they have strategically experienced growth and innovation in other areas of the company. Sometimes, technology is the underlying component of a company’s business solutions hiding in plain sight. For instance, following the latest software development trends, you can build a cutting-edge website to run your newest, hottest marketing campaign to achieve those annual company goals. Sure, the strategy and the ideation are important but so is the digital manifestation of your creativity. None of it is possible if you keep your strategies rigid in an environment that is versatile and constantly changing.

Strategizing Cybersecurity Employment

Cybersecurity has gained an intense amount of attention ever since employees have started working remotely. With several assets co-located and re-situated, the need and level for security have escalated. Companies have expanded their budget to incorporate the safety of their corporate network, providing innovative firewall systems and antivirus programs. It is a common perception held by businesses and managers that cyber-attacks take place through external sources, however, this is not always true. Although firms invest all their time and energy building a strong defense, there is always a possibility that such security breaches stay on the system for long periods. These concerns can be difficult to detect, making the situation even more crucial. When companies do not invest in updated, contemporary systems, they run the risk of being exposed to hackers and other cybercrimes. To counter such problems, experts suggest penetration testing, and companies are looking toward cyber range exercises for their internal teams.

Regulation and the Tech Industry

The tech industry is heavily regulated given how companies need to protect their internal data as well as data collected from external sources such as their clientele and customers. Consumer privacy has been a buzzword in the industry ever since the rise of consumerism, as technology continues to transform society, slowly becoming leaders industry-wide. Creating proper, regulatory laws is a lot tougher than it looks and this often happens for two reasons. First, concepts and basic systems vary across geographical areas. With cross-border technology available, antitrust concepts and sometimes basic business practices in different cultures get highlighted in many different ways. The other problem with the application of existing regulatory guidelines is that they do not extend to the digital platform. For instance, price gouging has been controlled by many laws built to keep consumers safe. However, these rules do not apply to the digital wing of brands that also choose to operate remotely through social media. As difficult as it sounds, sometimes rules passed to consumers digitally give off a perception of mistrust in technology. Parties involved in making such regulations must ensure to build a framework for responsible behavior, accepting the inadvertent consequences of the use of technology. Tech firms can work collaboratively with lawmakers and legislators to come up with comprehensive, detailed regulatory laws so customers can be involved in a fully transparent environment.

Artificial Intelligence and Software Development Trends

Artificial intelligence is the buzzword when it comes to software development trends of 2022. AI can effectively be used to enhance customer experience, while other tools can be used to collect, segregate, and analyze data into useful forms for marketers and managers. For those companies that work largely on data, making strategic decisions based on calculated risks and results, the correct utilization of AI can be a remarkable experience. Companies can maintain their competitive edge while maintaining their standard of business by saving up on expensive costs of incorrect decisions, and bad judgments. This technology provides a platform for dynamic digital transformation, taking together the entire team to do the job innovatively and effectively. Information technology companies are employing systems such as RPA techniques that will result in higher efficiency of functions and decisions. When redundant tasks are involved in a certain business model, RPA makes sure to reduce redundancy, employing its smart techniques to outperform every time. Banks use this technology when a new customer walks in, looking to open a current account. The account operating officer can use a basic device to avoid exerting manual labor to record basic, monotonous details during account opening services.

Internet of Behavior

An ongoing innovative software development trend has resulted in the invention of the Internet of Behavior. Customization is the friend of every brand and having IoB on their side can really help managers and marketers understand their audience better. A major part of running a company is making a good amount of profit that will earn wealth for its stakeholders. IoB helps these top-level executives understand the general public behavior of consumers with a brand-new developed app and other such devices. Another great example of real-time interactive, innovative techniques in the market is YouTube with its feature of recommending personalized videos depending on consumer activity and interaction with other websites and apps online.

Distributed Cloud Technology

Cloud technologies have also grown through evolution since their inception. These services have now been recognized as one of the biggest shaping applications depending on developmental trends. Although cloud computing is not a concept foreign to us, the evolution of the use of cloud technologies has revolutionized the way customers employ this service to enhance their personal experiences. Distributed cloud is a great new way company is connect remotely when they are placed away from any resources they are used to. Industry experts advise startups and small businesses to adopt cloud sharing, where applications will remain in operation no matter what happens to a remote server. The frequent availability of cloud services makes it much more difficult for breaches to take place, keeping customer data safe in one place.

Continuous Delivery Systems

Where web and app developers work, they need continuous delivery – that keeps an emergency copy of the code – that can be deployed at all times. Considering how fast changes are taking place globally and locally, these developers require constant updating of the code to keep moving forward. Although continuous delivery is expensive to adopt, its constant feedback and ability to identify issues make it the ideal partner for the way IT business models work. For companies, especially startups, to fully utilize the skill of the development team, it is important that they have their hands-on rapid systems that work in short, sprint cycles.

Low-Code Development and Cost Optimization

Among the many software development trends, companies are expecting to see in the coming year, technologies to optimize low-code development by cost optimization are also gaining importance. This technology enables companies to construct applications with as minimal coding as possible. While the regular coding process involves flowcharts, visual interface testing, and drag and drop tools, low-code development tools allow businesses to assimilate business logic into the application.

Also Read: What is No-Code Application Development? – Complete Guide

Experts are exploring the idea of building applications with little to no coding at all, reducing dependency on skilled, high-paid experts. For startups, this mode of development is perfect as they can get so much more out of paying as sensibly as they can. New managers can instantly idealize business models, test them out, and officially launch them in the market.

Software Outsourcing

It is a fact that most giant companies prefer to outsource their software and app development functions. Be it an effort to reduce costs or collaborate with big names from time to time in the industry, time efficiency is also an important aspect to consider behind such outsourcing. This practice can also prove to be healthy and helpful for startups that make use of third-party skills and talent without having to bear the cost of expensive information technology costs and staff salaries that they cannot afford. Even giants like brands from the Facebook network such as Instagram and WhatsApp are known to outsource a good chunk of their developmental operations to third-party software houses and other such companies. This strategy is to save up on permanent capital costs and indulge in low-risk decisions, all the while keeping in check the cost of the in-house recruitment. A manager must ensure to adapt effective, intelligent strategies when outsourcing their software developmental needs, keeping private and secure the data and information of their customers.

Final Thoughts

The new and upcoming software development trends have everyone on their toes which also gives experts an insight into what the IT industry will be like. The software development industry is a dynamic one, rising the incoming waves of technology, consumer behavior, and many other underpinning factors that make it easier for marketers and managers to make decisions that will benefit the company. Organizations are beginning to think big and living on the verge of change, making sure to evolve as the industry does. After an earth-shattering experience with COVID, managers are bracing themselves while they jump on the bandwagon of technology. As discussed above, the predictions are expected to take the software development sector by storm and evolve once they hit the market. Startups need to buckle down, focus on staying abreast, and adopt newer ways of operating and strategizing while the industry changes from traditional and outdated operations to those that are the need of the hour.

What keeps eCommerce enthusiasts active at night? Someone invests years developing a business with a renowned eCommerce platform and ends up outgrowing its native capabilities. Since the maximum scalability has been achieved is it possible to switch to something more flexible while refraining from starting everything from scratch?

You’ve probably heard the discussions around headless eCommerce and the enormous benefits it offers that significantly boost site speed and improve UX on a priority basis.

But what about headless Shopify?

How are brands turning their existing Shopify stores headless?

Is it somehow possible?

You can’t manage to lose the powerful eCommerce functions that Shopify is famous for such as payment gateways, inventory management, PCI compliance, etc. But in parallel, you may want to deploy a technology that dramatically boosts the speed of your site, or maybe you have advanced content management requirements, a lengthy product catalog, or maybe just want to surpass the limitations of your theme and ensure a unique site experience.

It’s perfectly possible to merge headless commerce with an existing Shopify platform and there are different ways to make your shift drive more smooth and directed. This article will describe the reasons and approaches of going headless with Shopify, how can you achieve this, and the benefits you will experience as an outcome.

What is headless Shopify?

If you gear up to go headless with Shopify, you incorporate all the remarkable backend eCommerce functions Shopify provides, you’re just using a separate frontend or “head” to drive the customer-facing view of your web store.

Shopify is one the top one-stop-shops that sustain all your eCommerce requirements, with plug and play options like:

• Easy to install apps from the app store
• Mobile-friendly checkout supporting alternate payment gateways, like Apple Pay
• Great security and compliance protocols
• Advanced order management and inventory

But as you scale up your store and business, you start to feel some features or design functionalities a little inflexible. The one-size-fits-all strategy can work up to a certain limit, but when growing up to a certain limit you may find you need something more than the Shopify Storefront alone can provide. Often, brands opt for Shopify headless as a medium to ensure:

• A faster site load with quick page-to-page load time
• More control over how your products are visually appear
• More flexible content management
• A highly-customizable URL

If you’re going to expand your store, a storefront tailored to the varying needs of your brand starts becoming a necessity. The good news is there’s no need for an across-the-platform overhaul or creating a new eCommerce store from the scratch. Shopify was developed with freedom in mind and is able to sustain a frontend that better serves your business requirements with minimal hassle as you scale up. That’s where their storefront API comes into play. Is Shopify a headless CMS? It would be more appropriate to say that Shopify offers a CMS with a headless option. The primary advantage of Shopify, and what’s made it such a powerful platform for online business is its built-in tools and services. Shopify’s integrated CMS, the Theme Layer, and Editor, which you may already be known, supports the rest of its eCommerce functions. But these functions are developed in such a way that they can also function independently from one another if necessary.

So if you need a slightly more flexible environment than your organized content in your CMS (Shopify only allows four predefined content types) you can utilize a more diverse third-party CMS like Contentful or Prismic to outline your data in a way that matters to you, and, with headless, link it to Shopify via an API. Shopify and other providers have introduced an effective toolset that makes the process a lot easier than you ever imagine. Shopify CMSThird-party CMSExperience Platform Theme layer and simple editor allow you to make changes to content in Shopify. Four predefined content types: pages, blogs, products, and collections. More adaptable CMSs like Contentful and Prismic allow you to define content exactly how you want it.

A frontend solution that combines an Experience Manager and CMS to create an intuitive visual environment, where changes, updates, and experimentation can be done quickly and easily If you’re a small eCommerce business, Shopify’s CMS might well be enough to sustain your needs, but if you’re scaling, or have a mobile-first business structure primarily focused on speed, presentation, and the customer experience — the relative simplicity of Shopify headless commerce and an effective CMS option might well make following a PWA build valuable. You might opt for this option with an experience platform or a frontend-as-a-service provider who’s got a CMS built-in compatible with Shopify.

Benefits and Disadvantages of Headless Approach

As more and more customers start to see online shopping as a new norm the pandemic pushed up but certainly didn’t initiate, the need for performance at scale and across channels becomes ever more crucial. Going headless with your Shopify store using a PWA (progressive web app) can give you faster page load speeds even on mobile. This boost in speed allows you to future-proof your business. But there are certain factors you’ll need to consider for getting on the bandwagon of adopting a headless approach.

Benefits of Headless eCommerce

Rich control over the customer experience

Using a headless approach, you are allowed to have more customization and personalization than you’d get with plug-and-play Shopify. While the variety of themes that Shopify features are appealing enough, and up to a certain scale somehow customizable, they start to show lacks in performance in an attempt to provide an effective and responsive customer experience when you significantly scale up your operations. Opting for a headless approach allows you to keep your branding stand out from the masses. You can surpass the limitations of Shopify’s integrated features and themes, and make a significant difference by your digital presence in terms of visuals and CX.

If you want to launch your business internationally or want to produce multiple brands or product categories, the headless approach enables you to take your content management up to the next level. If you are up for breaking your comfort zone in working with standard Shopify, you must redesign workflows and manage content from a broad perspective. You’ll also gain more freedom in site management without expertise, and without compromising on customization.

Increased speed

It’s been well proven that more speed comes amidst more sales, especially on mobile, so anything you can do to automate your codebase will help you gain leads. Being able to add apps via Shopify at the click of a button is very appealing, but at a certain point, all that excess code starts slowing down your site. The headless approach allows you to independently and freely use a faster frontend delivery approach. Using a headless PWA architecture allows your browser to load quickly a statically generated site that doesn't cast any burden on a database by sending multiple requests.

Customizable URLs for better SEO

The structure of your URLs plays a vital role in assisting customers in finding their way to your site. To let Google know your product page is worthy to show in response to a customers’ search. So it is highly recommended to make your product page URLs scalable enough to entertain highly detailed product descriptions. Shopify is primarily very inflexible when it comes to modifying your URLs. There are only four predefined URL types to opt from, so you can’t add all the detail you require to make Google your key partner. Developing your web pages within a headless architecture gives you immense control over your URL structure, sparing you time and effort to maximize your SEO returns.

Reduced time to market

With a custom frontend linked with your Shopify store, your marketing team will be able to handle and create campaigns effectively and rapidly while playing around with site layout and product placement without disturbing any backend processes. You’ll also get enormous control over your look and vibe to match your brand exactly without any compromises.

Disadvantages of Headless eCommerce

Can mean loss of support for apps/services

When you abandon Shopify’s built-in themes, some of your apps and services are going to stop working. If you opt for a frontend-as-a-service provider, then they’ll have extensions and services you can pick and choose to have remained intact and working for you. But if your business is building your Shopify store’s frontend from an outsourcing firm or vendor, you’ll have to write some custom code so that Shopify’s APIs can support your third-party applications and services.

Can introduce complexity

Introducing another layer to your eCommerce stack means increasing the complexity of the operation. If you decide to opt for a dedicated frontend, you’re going to have to manage (at least) two platforms to keep your site keep going. The app-related compatibility issues pushed by headless Shopify also apply to a multitude of frontend integrations. Considering the extra complexity, if you’re immature in the eCommerce domain and running a small business then Shopify may well be already providing you enough flexibility. If you tend to scale up your store and operations then you’ll either need an in-house development team familiar with React, or you’ll have to go for outsourcing from a frontend-as-a-service provider and/or outsourcing firm.

Implementation partner considerations

Having someone who can deal with the technical aspects of going headless, sparing you and your eCommerce team enough time and effort to concentrate on sales and marketing. But you need to allow this under your conscious supervision. Have a close look at your partner’s history of past services and clients. If they have experience of serving the brands like yours, that’s a strong determinator of their experience and skills-base required to turn your eCommerce presence into your success.

The Three Paths to Headless Shopify

If you’ve decided that your Shopify storefront needs a new and improved frontend, there are three main ways to do it, and pros and cons for each. Which route you choose will depend on your specific business needs:

Do it yourself - DIY

Pros

Complete control: Creating your own headless structure using Shopify Plus means that you’ll have full ownership and control of all your eCommerce functionalities, potentially allowing you more freedom and flexibility. It means you don’t have to adjust to someone else’s system and can take the necessary steps regarding development and coding.

Cons

Requires rich knowledge: going headless from scratch requires high development and coding. For instance, Shopify themes use Liquid as a template language but you can’t use Liquid in a headless approach, so if you want to build a custom storefront accessible via the Shopify Storefront API, you’ll require a team that’s used to dealing with frameworks like React.js and Ember. Or, if you want to build a static platform, they’ll need to be well-informed about frameworks like Next.js or Gatsby.

Build with an agency

Pros

Expertise: You’ll have an outsourcing vendor who’s well-informed in turning sites headless. If you choose your outsourcing firm wisely, you’ll be working with someone who has a rich experience of building headless platforms that are compatible with Shopify’s backend and will be well aware of all the potential hurdles and how to dodge them! Further, choosing an agency can allow you to make your store headless by teaming up with a frontend-as-a-service provider for the software to complete your headless transition drive.

‍Extra help: An agency will be able to provide you with suggestions and recommendations at the design phase and have the knowledge to turn your brilliant concept into a reality.

‍Experience: They’ll be able to make recommendations about the features, layout, and navigation that work best with a Shopify backend, and so help you optimize your web store.

Cons

Ongoing management: In the end, you have to manage your headless store on your own. Once the initial design and development are done, it’s up to you and your technical team to keep the whole operation going.

Use a Frontend-as-a-Service Provider

Pros

Bundled Software: If you want to absolutely free yourself from research, design, build, test, and deploy a complex platform and custom APIs, a frontend provider can take all your stress and load. What you get in the end is a complete package with everything you need to keep your storefront managed, still completely customizable and sustainable to your needs, and that too without any need to do all the heavy lifting yourself.

‍Lightens the load: Instead of your teams having to negotiate with multiple players at different phases of the development cycle, they’ll be able to manage all the eCommerce and marketing concerns while freeing themselves from a great number of technical headaches.

‍Expanded skills-base: Many frontend providers are linked with partner agencies that are specialist website builders, so not only will you get an expert opinion, and assistance for the sleek functioning of your store, you’ll have an attractive new site in your account.

Cons

Loss of control: You’ll lose active control over some core and internal aspects of your site’s functionality. You won’t be able to simply make a few coding changes whenever you want to. In fact, you’ll need to discuss some detailed, technical, and behind-the-scenes activities with your frontend provider.

Make and Crosscheck a List (before going Headless)

Now you have decided whether to go headless or not. Before going headless with Shopify, it is highly recommended to:

• Acknowledge that all the apps you’re depending on have APIs and can interact with Shopify Storefront API. Outline the apps and services you absolutely need to make it over to the headless site and consider how you’d like the experiences for each service to appear and perform with respect to their UX.
• If you’re taking a Do It Yourself - DIY approach to go headless, you’ll need a rich how-know of GraphQL, the language Storefront API uses.
• Benchmark your web store. Perform some tests and find out what your initial score is and what might be pulling it back. This way you can identify the benefits of your new headless commerce platform quantitively and measure its success ratio once launched.

In recent history, businesses have always had to face changes in customers' demands. But, amid the COVID-19 pandemic, the norms that prevail in modern society have changed, and the impact will likely be in effect even after the end of the pandemic. While some changes in routine due to strict lockdowns and other containment measures may end as soon as people start getting vaccinated, other aspects such as the way people depend upon digital technologies will endure.

Businesses can only survive in the post-pandemic environment is to reforming an organization's digital makeup. Companies will have to reform and revise their operations through the entire lifecycle, from customer acquisition and operations to post-sales service and customer care.

In the contemporary scenario, if an organization hesitates to transform itself to address the business landscape's demands digitally, its survival would be questionable and uncertain. Adapting rapidly to the evolving challenges and conditions is also mandatory for the successful and timely delivery of products and services to customers.There are many blockages for the conventional global supply chains due to containment measures in different countries, and people are getting used to adapting technologies in different ways. An agile response to transforming business processes hit by the pandemic is vital for the survival and growth of the organization. We must first accept that the new normal of the post-pandemic world will not be the same as the old one.

Digital Transformation in a Nutshell

Digital transformation denotes a reimagining of how an organization integrates technology, people, and operations to pursue modern business infrastructure and enhanced revenue streams, fueled by changes in customer expectations concerning the products and services. For many businesses that produce conventional goods, digital transformation means building digital products, such as mobile applications or an eCommerce platform. In order to do so, they must follow the operational model of software companies, most of which operate on product-based models. Ideally led by the CEO in partnership with CIOs, CHROs, and other senior leadership, digital transformation needs cross-departmental collaboration in integrating business-oriented philosophies with instant application development methodologies.

Digital Transformation - A Strategy or Solution?

In the recent decade, companies have embarked on digital transformation drives to eliminate the potential for disruption from incumbents and startups, but the sledding has been slow — until the pandemic. Top retail store chains like Walmart and Bed Bath & Beyond shook up store operations by switching to shipment-based delivery and other contactless options to assist consumers in safely having their hands on desired products or items. Meanwhile, IT leaders reprioritized strategic IT solutions, with many acquiring cloud infrastructure for video collaboration and deploying machine learning (ML) models that can help organizations to manage how products want through lengthy supply chain processes.

On a point-by-point basis, such methodologies and solutions don't impact in such a way as to be called transformations. In fact, how these methodologies and other solutions are integrated throughout an enterprise presents a transparent view of a company's digital supremacy and reflects its business priorities. In the contemporary scenario, When trade shows were stopped due to the COVID-19 pandemic, the most prominent companies took their interactive experiences onboard to ensure interactive customer engagement. They also trained their sales department to use these tools in one-on-one virtual meetings, and they are now able to showcase their competitive edge effectively. Digital transformation solutions are intended to overhaul the overall business operations, interactions, and service delivery mechanism. Therefore, Digital transformation is more of a strategy than just a solution. Understanding digital transformation bounds you to have deep insights into the reasons why the sudden upsurge in digital transformation and the need to adapt it is becoming crucial like never before. Let's discuss this in detail.

The Raising Concern for Handling Data

Businesses are becoming more data-driven than ever, and dealing with the overwhelming amounts of data has been the biggest challenge. In specific, the need to place intelligent mechanisms to manage data inflow and storage is growing exponentially to manage and store it in a secure and streamlined manner. In this race for digital supremacy that centered around data to keep businesses beyond their potential rivals, the only solution is to adopt digital transformation. It involves deploying effective tools and methodologies to conduct secure data transactions, place data strategies and develop data analytics.

In this context, the primary approaches in digital transformation are big data, agile analytics, and AI concepts (More specifically, Machine Learning and Deep Learning). These mythologies and approaches are helping businesses to extract more profound insights into the market dynamics and consumer response, which are vital in driving rational and informed decision-making processes. In this way, digital transformation can make businesses more effective in handling data and retrieving fruitful insights from it.

Service Delivery Efficiencies

Generally, companies value workplace services the least compared to client/consumer service delivery. Despite having numerous digital applications to streamline the processes, companies are using many manual methods to deliver services within the organization. One such case can be noted easily in HR-related functions, where both parties must write numerous back and forth emails, wasting their time for simple queries, like PTO balance, etc. HR departments waste around 70% of their time performing such routine and iterative tasks even if they could be streamlined.

Digital transformation in such service delivery processes can be attained by having intelligent HR automation software utilities. These solutions have an immense potential to boost operational efficiencies of the business while ensuring intuitive experiences in workplace service delivery. Further integrating AI-driven chatbots or voice assistants with service delivery platforms is a big plus for the overall process.

Getting Freed from Legacy Systems

Legacy systems are among the top hurdles for mighty organizations that abstain from meeting the modern market's dynamics. As legacy systems are based on conventional technologies, acquiring a resource person and updating it to digital technologies is complex. It was acknowledged by the majority of IT decision-makers linked with research work. Apart from this, most organizations are conscious of security concerns involved in performing legacy migration. For instance, in conventional legacy systems, such as air trafficking control ATC-systems, carrying out legacy migration can be risky as it possesses a variety of risk factors and intellectual data.

In such a scenario, ensuring digital transformation clearly requires a precise statement of scope, and the objectives of digitalization must be clear. The best solution ever introduced is the Application Programmable Interface (API) to modernize such systems. This integration solution allows businesses to bridge their existing legacy systems with digital efficiencies. By doing so, the API provides a gateway for businesses with legacy systems to enjoy the perks of digitally transformed solutions.

Consumer Digital Trends are Driving Transformation

According to the research:

• Around 60% of the global population are internet users
• Around 2.5 billion smartphone users
• 75% of internet usage is driven by smartphones
• 2.5 billion are social media users
• Global eCommerce sales climbed to $4.921 trillion by the end of 2021.

These statistics clearly showcase the sudden upsurge in the use and dependence on consumers' digital platforms, devices, and applications. The need for businesses to revise their efforts to meet the contemporary market challenges to win the digital audience by promoting their services and products digitally is not a choice anymore. Meanwhile, companies are putting efforts into mobile apps, responsive websites, social media channels, etc.

End Words

Another aspect of the context is that most businesses are failing to achieve total digital transformation. This is because businesses are neglecting that digital transformation is more of a strategy than just a solution or a tool. Businesses need to have clearly outlined and defined objectives and scope before gearing up for digital transformation.

Process management is an enormous field that is divided into various sections. It is all about dealing with the crucial aspects of creating, managing, and implementing multiple architectures by minimizing all the obstacles in the process. Among the essential constituents of process management; comes process mining, which can be seen as a blend of various technologies that help complete a project successfully, saving time and energy.

The primary purpose of process mining is to inspect the way processes work, how they originate, the hurdles that appear, and the techniques to minimize the barriers and upsets for a process' improvisation. Keep reading this blog as we will shed light on process mining, how it works, its benefits, and will compare it with RPA:

What is Process Mining?

Process mining can be defined as a process to examine and to keep an eye on the processes’ progress. Earlier process mining was done by conducting various workshops and consulting individuals to draw a picture of the processes.Since everything has modernized with time, so have the process mining techniques as they have evolved from the traditional practices to more advanced and automated ways. These days, process mining is conducted by analyzing the already available data and displaying a process based on the information.Process mining can be implemented on any process if the required data is available or stored in a system. It has made the visualization of your processes more effortless than ever before. You can use process mining to conduct an in-depth analysis, compare different strategies, monitor tasks, set benchmarks, and work on the data for improving processes.

Process Mining Benefits

Process Mining brings a series of benefits with its implementation since it is a solid upgrade from the weary traditional methods for analyzing data and project management. Let's take a look at the salient advantages of process mining in this section:

1)   Process Improvements & Error Detection

All the activities that are conducted for the initiation, processing and finalization of processes are shown by the process flow. A process flow includes all the anomalies, divergences, and missed steps to help you conclude better results. A user can track the processes and check if anything goes against your target model, check for improvements, and make the needed amendments right on time. Not only that, but a process flow also informs you about the better methods, and you may implement them for improved results.

2)   Timely Improvements

Process mining makes it quick and a lot simpler to get the results, so it also has the nature to accept the real-time changes in the market.It also makes the process of setting goals easier, which helps in developing an all-encompassing, assertive, and long-term optimization strategy that's also flexible and welcomes new changes without any problems.

3)   Clarity

Since many processes are running in parallel, it is impossible to monitor each project following a traditional approach. Process mining provides more clarity in process management, as it shows the progress of all processes, whether running alone or in parallel to other processes.Earlier, the visibility was quite tricky since there was a lot of paperwork involved, and with bigger projects, it was nearly impossible to track every process. Gone are the days when you had to guess if a process was failing or successfully running; with process mining, you get a clear picture of the progress of all processes.

4)   Quick Results

Since process mining follows the latest approaches for optimization, it dramatically increases the pace of results. Rather than spending hours on paperwork and analysis, mining does your job in a matter of seconds.

5)   Easy Monitoring

Process mining displays all your processes in great detail so that you can bring about changes at any phase to improve your processes. It allows you to either enhance the whole process or just work on the snippets of a process. All this helps you in developing a better strategy. On top of that, process mining also allows you to check how your optimizations are affecting your processes and change the strategy at any point for better results.

Process Mining and Robotic Process Automation (RPA)

Process mining has been used effectively to analyze the current state of business process performance, identify areas of improvement, and assess the results of process improvements. With process mining, you get a clear, data-driven picture of how well a process performs. The ability to see issues and solutions clearly will intrigue people working with process management. It will strengthen a company's commitment to making decisions based on data. Some businesses have already recognized process mining as a significant step in implementing RPA with better results. Many upcoming solutions will use a fusion of process mining, robotic process automation, and machine learning for best results.

How Do Process Mining and RPA Compare Against Each Other?

RPA handles all the tasks that are performed on a repeated basis, as it automates all those repetitive tasks to be done by robots in a faster and more efficient way. The RPA bots are handled via an application, and they imitate all the human actions that include regular tasks like adding, editing, removing, sorting the data, and much more. Unlike RPA, which is a solution or a tool, process mining is more like a methodology, intending to turn data into useful information and take appropriate actions. In order to digitize and automate business processes, businesses use process mining to analyze event log data for trends, correlations, and precise details about how a process develops. The new insights obtained from process mining can be used to eliminate corrupt data, efficiently allocate resources, and respond to any changes rapidly. RPA automates business processes while process mining solutions help in the CRMs and ERP systems. Despite the fact that RPA and process mining are polar opposites, they work brilliantly together.

Benefits of Using Process Mining and RPA Together

Process mining and RPA are both powerful technologies but are lethal when they come together. They help your business in the following ways:

• Process mining and RPA complement each other as the former ads system event logs to gain insight into business processes, and the latter automates these processes.
• When used together, process mining improves the efficacy of bot operations and their deployment, which results in better results.
• Process mining increases the success rate of RPA projects.

Process Mining + RPA = Hyper-automation

Hyper-automation refers to the practice of automating everything that can be automated in a business. Think of it as a combination of RPA and process mining. Using AI, ML, and other technologies, organizations adopting hyper-automation aspire to streamline operations across their business so that they can function without human involvement. Businesses implementing hyper-automation will find that process mining does much more than just identify areas for automation. The system also establishes links between different IT systems and reveals previously hidden workloads. People mostly get confused figuring out the difference between automation and hyper-automation, so let’s clear how they differ once and for all. Automation refers to the accomplishment of a routine task without the involvement of a human being. It's more common on a micro level, with solutions tailored to specific problems. Hyper-automation pertains to using various automation tools for large-scale automation projects. The tools used in process mining also produce data ready for machine consumption, allowing for the automated process's robotic automation. Hyper-automation can benefit an organization in myriad ways, including:

•      Helping your workforce with teaching the right skillset.
•      Improving your business via intelligence using Artificial Language and Machine Learning.
•      Providing information on automating your ROI so that your business can continue to grow.
•      Optimizing any business process using the latest technologies.

Process Mining and RPA Costs

Sure, process mining and RPA are not cheap. You might get scared a bit when looking at the costs of RPA and process mining. But here's the thing. You need to calculate the value they are providing against their price. Calculate how much labor costs you will be saving with their implementation. If we take into account the amounts that these tools help us save, then their amounts will look like nothing. Keep in mind that these tools aren't built for struggling small businesses or individuals; but rather for enterprises. Using RPA bots as a quick fix instead of tighter data integrations and improved ETL processes is quite common these days. RPA bots often hide technical debt by sitting on top of fragmented software landscapes. Businesses can benefit from more intelligent automation. However, many organizations are better off unraveling their technical debt to enable simple data integrations and automation within their existing software rather than embarking on RPA expeditions.

Final Thoughts

In this technological era of development, anyone abstaining from the latest technological advancements will find themselves getting stuck in the web of problems.All successful businesses are embracing process mining and robotic process automation to help them grow faster than ever. The combination of both RPA and process mining is lethal, so if you can afford it, then go for it.

Today, more businesses are turning to an emerging technology trend known as Robotic Process Automation (RPA) to enable business process automation and cut costs. With RPA, companies can streamline iterative rules-based business operations allow employees to spend their crucial time serving customers or other core business processes. Others see RPA as an alternate of intelligent automation (IA) involving deep learning (DL) and artificial intelligence (AI) methodologies, which are intended to predict plausible outcomes of the future.

What is Robotic Process Automation?

RPA is a methodology driven by business logic and structured inputs that streamline business processes automation. Using RPA methodologies, a business can configure a software tool, or a "robot," to capture and translate inputs commands to process a transaction, manipulate data, generate responses, and interact with other digital applications.

RPA domain of influence can range from something as simple as producing an automated response to an email to deploying a multitude of bots, each designed to automate a variety of roles in an enterprise environment. For instance, professionals working for financial services facilities are eager to adopt RPA solutions, determining ways to effectively utilize the software to make business processes efficient without increasing monetary expenditure.

RPA - Use Case for Business Process Automation

Many enterprise-level companies adopt RPA, such as Walmart, Deutsche Bank, AT&T, Western Union, and American Express Global Business Travel. Walmart's Chief Information Officer (CIO) Clay Johnson reveals the retail giant has deployed about 500 active bots to streamline anything from responding to employee queries to extracting useful information from audit data.

David Thompson, CIO of American Express Global Business Travel, informs about using RPA to streamline and automate the process for canceling an airline booking and handling refunds (based on different refund criteria and time of booking cancellations). David is also interested in using RPA to facilitate automatic rebook recommendations in case of an airport shutdown or abandonment of services and streamline specific expense management workloads.

Role of RPA in Business Process Automation

Below is the complete list of perks and leverages amidst deploying RPA in the business process.

Optimize resource usage

To assure high efficiency in business operations, you need to curb the probability of error occurrence. The risk is notably hiked due to iterative routine tasks. Humans start feeling bored and demotivated while performing their iterative duties and make comical mistakes when carrying out routine activities. Such routine tasks are often served with less or no interest, resulting in compromised performance. RPA can be used in such scenarios to replace human counterparts and take care of tasks worthy of human effort and time.

By optimizing the use of human resources and assigning them more productive tasks and core businesses duties, businesses can get a significant boost in productivity with minor mistakes and mishaps from employees who start feeling exhausted by performing the same thing again and again.

Introduce adaptability and flexibility to processes

Robotic Process Automation tends to ensure flexibility in business operations. The tendency of RPA systems to adapt to conditions and scenarios makes them a good choice for most businesses, thus freeing human resources for more valuable and complex workloads. Studies have proven that when humans are bound to make small changes in the daily tasks they are subconsciously got used to, they will often forget to make the required modifications. RPA utilities are deployed on servers to eliminate this issue, making processes flexible and scalable in case the demand shocks or the scope of a circle increases.

Aim for cost-effectiveness

RPA tools, when used in parallel with workflow tools, can prove to be a game-changer for businesses. Instead of competing with each other (as is the widespread belief), both tools complement each other. The RPA program awaits a command from the workflow tool to carry out a particular job or task, and upon receiving the command or signal, the program performs the task and feeds the process back to the workflow tool. As a result, RPA and workflow tools complement each other and complete the loop mandatory for a reduced expenditure.

Improve communication

Replace document-producing processes like salary slips or HR emails with RPA, as it is better equipped to carry out your vital communication processes using its intelligence. RPA can make alterations in a single document and duplicate these changes throughout other documents, thus sparing the employees from manually updating and customizing each file and performing minor edits. These processes can ensure the end-users, representatives, and on-field employees get the latest information every time.

Discover automated responses and triggers

In general, every RPA system can perform task scheduling. Even though it serves way more than the task of a mainstream scheduler, it assists managers with completely streamlined and semi-streamlined scheduling utilities. The former scenario only triggers and responds when a particular activity happens, primarily a human activity such as a click or input command. In the case of unsupervised and fully-capable automation, the trigger does not need to be a human input or activity. Still, it can be anything such as an email or a document. Businesses can acknowledge certain critical areas in their operations domain that can be entirely or partially streamlined using triggers and responses.

Implement RPA hassle-free

RPA implementation does not demand any API configuration, making businesses refrain from investing massive capital in terms of costs and time. Robotic process automation comes with its own set of Graphical User Interface (GUI) that is very user-friendly and requires little or no technical background. RPA systems can handle the operations humans perform, such as clicks, keystrokes, pressing buttons, and so on, through the same UI.

Empower the workforce

When robots take control of tedious tasks, employees rejoice. Recent surveys and studies have proven that employee satisfaction boosts as they perform insightful, meaningful, and worthy jobs. As employees take charge of significant job roles, they look forward to appreciation, which becomes fuel for their extraordinary efforts, ensuring a model business infrastructure with highly motivated individuals. Another passive advantage of employee satisfaction is when employees have high motivation and active spirits and refrain from switching jobs.

Insights and Analytics

With robotic assistance in data and analytics operations, there are fewer chances of data loss, information leaks, redundant records, and poor outcomes. RPA assists businesses in penetrating right through their data and extracting actionable and fruitful insights with fewer chances of failure. Robots also help gather data where it wasn't possible or more accessible for humans. Therefore, a broad scope of data and intelligence gathering and analytics ensures more profound and more detailed insights. At the same time, employees can focus on more complex and dynamic analytics, leading to informed and intelligent decision-making.

Error-free operations

With RPA, the leverage of having error-free operations can be easily made possible. Process automation eliminates the chance of mistakes, those that lead to poor analytics and irrational decision-making. RPA assists businesses to assure accuracy in their operations and makes the overall process error-free. A notable perk of attaining such operational efficiency is when there is no missing or mistaken customer information, customer service can serve the esteemed customers without any hassles in a more personalized way possible. However, even minor turbulence in the customer care operations can ruin the experience of customers with your business. Such hidden but overwhelming risks can be well avoided by having an effective RPA methodology in place.

Secure enterprise data

RPA can be merged with a variety of applications to boost the security of enterprise data and information. These integrations and compatibility ensure that the client's apps must be modified or enhanced by a robot. Without any need for modification, the system reduces the risk of fraudulent activities as business functions use and inherit the existing security infrastructure where authorization protocols are already activated.

RPA - Expectations vs Reality

RPA isn't for every enterprise. As with any automation technology, RPA has the potential to eliminate jobs, which presents CIOs with challenges managing talent. While enterprises embracing RPA are attempting to transition many workers to new jobs, Forrester Research estimates that RPA software will threaten the livelihood of 230 million or more knowledge workers or approximately 9 percent of the global workforce.

Even if businesses navigate the human capital conundrum, RPA implementations fail more often than not. "Several robotics programs have been put on hold, or CIOs have flatly refused to install new bots," Alex Edlich and Vik Sohoni, senior partners at McKinsey & Company, said in a May 2017 report.

Installing thousands of bots has taken a lot longer and is more complex and costly than most organizations have hoped it would be. The platforms on which bots interact often change, and the necessary flexibility isn't always configured into the bot. Moreover, a new regulation requiring minor changes to an application form could throw off months of work in the back office on a bot that's nearing completion.

A recent Deloitte UK study provided a similar outcome. "Only three percent of organizations have managed to scale RPA to a level of 50 or more robots," stated Deloitte UK authors Justin Watson, David Wright, and Marina Gordeeva.

Moreover, the economic outcomes of RPA implementations are far from the expectations. While it may be possible to streamline 30 percent of tasks for the majority of job roles, it doesn't actually translate into a 30 percent cost reduction.

Did you know that most of the data warehouse projects fail due to wrong planning and platform selection? That said, many businesses skip the step of selecting the right cloud data warehouse and proceed directly with the other tasks. Speaking of cloud data warehouse platform providers, both Snowflake and Google BigQuery are among the most sought-after options and offer top-notch features to facilitate organizations.

Our blog compares both warehouse solution providers in detail as we dig into the details of these data warehouse giants to help you make the right selection.

Understanding Snowflake and BigQuery

The thought of setting up a data warehouse earlier implied emptying your pockets on overly expensive hardware solutions to run in your data centers. However, the advent of cloud data warehouse solutions has halted these scary means and has provided inexpensive and finer solutions like Snowflake and BigQuery. Before we jump into the comparison, let us first give a brief overview of Snowflake and BigQuery for people new to these names.

If you are already acquainted with these data warehousing solution providers, you may skip this part and directly move towards the comparison part.

What is Snowflake?

Snowflake is a fully managed cloud data warehouse that is offered as a SaaS and DaaS to users worldwide.What separates Snowflake from its competitors is its architecture, which lets the users scale and pay for the computations and storage separately.You can deploy Snowflake to any of the following cloud providers:

•      Microsoft Azure
•      Amazon Web Services (AWS)
•      Google Cloud Storage (GCS)

Businesses and organizations that don't want to get into the nitty-gritty of handling their in-house servers and hiring multiple people for the system's installation, configuration, and management can get a solution like Snowflake.With Snowflake, you don't have to deal with any back-end work, as you can deploy Snowflake instances on any of their preferred cloud providers.

What is BigQuery?

Google BigQuery, like Snowflake, is also a fully managed cloud data warehouse solution that is popular for its speed and responsiveness. As the name suggests, BigQuery is presented by Google and uses its Dremel technology, and is presented as a read-only data solution. BigQuery's tree-like architecture is the secret behind its ultra-fast scanning and querying. BigQuery is highly scalable due to the fast deployment cycle, and to put the cherry on top, it is serverless and offers on-demand pricing. Its architecture works on analyzing the used resources. It assures the usage of all available allocated resources so that the organizations can deploy them without needing to scale out. BigQuery is also a big-data solution thanks to its ability to collect high volumes of data and analyze and organize it fastly. Businesses and organizations seeking robust analytical and intelligent solutions can opt for BigQuery, as its algorithm, architecture, and flexible pricing makes it quite handy.

Snowflake vs. BigQuery: Comparison

Now that we have learned about Snowflake and BigQuery, we can jump into their comparison. We will compare both data warehouse solutions in three different departments, i.e., features, performance, and pricing, and lastly will conclude a winner that excels better in these departments.

Snowflake vs. BigQuery: Features

We all fancy solutions that are not just reliable and affordable but are also packed with the best and latest features. We will compare BigQuery and Snowflake in terms of their features' offering in this section and declare a winner in the features department at the end of this section.

Machine Learning

Machine learning sheds light on the algorithms and the data usage to copy the methods by which a process is learned and improvised with time, thanks to its complex technology. While the technological world is welcoming artificial intelligence with open arms, it is impossible to forget the importance of machine learning in growing data science solutions. BigQuery pays its homage to machine learning as it lets the users train and deploy the machine learning models using the existing models and improvising them. You can make most of this feature as you no longer are required to export your data or use a tool to carry data exportation tasks. Contrarily Snowflake solely depends on the external tools for machine learning. Even though using these external tools, you can carry out the tasks in a proficient manner; this solution is certainly not as coherent and handy as the one that BigQuery provides. Furthermore, if you combine BigQuery with Looker, you can get the best machine learning results.

‍Winner: BigQuery

Security

Security is one factor that, if compromised, can annihilate any business or organization regardless of its size. Any business or firm dealing with confidential data should only opt for the cloud data warehouse solution that provides the most robust security. Thankfully, both our competitors BigQuery, and Snowflake are strong contenders in the security domain. Snowflake and BigQuery both use Advanced Encryption Standard on the data and support customer-managed keys. That said, both are dependent on the roles to offer access to their resources. Snowflake provides the SOC 1 Type II, SOC 2 Type II, PCI DSS, and HIPAA compliance, and offers strong security features to safeguard your precious data from intruders. Other security features include access control, multi-factor authentication, etc.

Don't want specific IP addresses to access your data? Snowflake lets you choose a list of IP addresses that you can whitelist, and any user with a different IP address from the list won't be able to enter the system. You can also blacklist IP addresses and use its automatic data encryption feature to guard your data further. On the other hand, BigQuery also focuses on security and follows modern methods to ensure the best security protocols. As BigQuery is a cloud solution offered by Google, it encrypts all your data automatically regardless of it being at rest or in transit. What more would one want?Like Snowflake, BigQuery also meets the PCI DSS and HIPAA compliance standards. Moreover, BigQuery allows the admins to manage the user's access to the cloud resources.

‍Winner: Snowflake

Ease of Use

Usability is another factor that everyone must take into consideration while selecting a data warehouse solution. Luckily, Snowflake and BigQuery are pretty user-friendly and built to provide a handy experience. The best thing about BigQuery in terms of user-friendliness is its serverless architecture which does not require the user to get into the technical complexities, as there is no setup required. The user just has to move their data into Google cloud storage, and that's pretty much all that is needed from the user's end. Even though Snowflake isn't serverless, it does not require you to set up the storage and compute, as it separates them both and uses the Snowflake Data Cloud to handle them. That said, you will need to have a cloud provider to back you up, unlike BigQuery that Google Cloud manages. The comparison of BigQuery and Snowflake is quite challenging in this domain, as both go head-to-head on user-friendliness, with BigQuery having a slight edge over Snowflake.

‍Winner: BigQuery

Maintenance

Most organizations are reluctant to pay high prices while spending on cloud warehouse solution providers and to save a few bucks, opt for inexpensive solutions. Even though they save themselves in the beginning by paying low costs, that strikes back as the cheap solutions often fail or require hefty amounts for their maintenance. The cheap solutions' maintenance is hard on the pockets, but they are also unreliable and insecure. Always go for a well-reputed warehouse solution provider and that does not require heavy maintenance over time. Unlike other solutions, Snowflake and BigQuery do not require massive administration costs and are pretty easily maintained. BigQuery facilitates its users by transferring the unused data to long-term storage automatically, saving high costs. If any element within BigQuery has not been used for over three months, it will automatically move it to long-term storage. Since both Snowflake and BigQuery are automated systems, they don't require much supervision. Both don't need human intervention in query optimization and instance adjustment. They also allow the admins to manage the user roles and permissions to ensure secure access. As data scales up with the passing time and the queries get more complex, both Snowflex and BigQuery automatically scale them to meet the requirements.

‍Winner: Tie

Scalability

Since Snowflake separates the compute and storage resources, users can independently scale them as per their requirements. It also considers automated performance tuning and workload monitoring to enhance the query times when the platform is running. On the other hand, BigQuery tackles scalability differently. As it is serverless, it automatically facilitates extra compute resources or as per the on-time requirements to deal with big data. This ability makes it easier for BigQuery to process millions of gigabytes of data in a couple of minutes. Winner: BigQueryCombining our results in the domain of the features, we see BigQuery as the clear winner. Let’s see what we get in the performance and pricing domains.

Snowflake vs. BigQuery: Performance

The auto-scaling ability of both Snowflake and BigQuery allows them to sustain incredible amounts of load and deliver excellent performance. Both deliver almost similar performances for many tasks and require very little maintenance.If your business or organization deals with massive volumes of data and has high idle times, then BigQuery is a better option.On the flip slide, if your usage is relatively steady dealing with the data and queries, then Snowflake would be a more economical option, as it will let you resolve more queries into your compute times.Last year, Fivetran worked on a benchmark report that compared both our contenders, Snowflake and BigQuery. They ran 99 TPC-DS queries of different complexities and ran each query only once to abstain from caching the previous results.Fivetran generated a 1TB TPC data set having 24 tables in a snowflake schema, and they also decided to avoid fine-tuning the data warehouses and delivered the following results.

•      Snowflake gave an average query time of 8.21 seconds.
•      BigQuery gave an average query time of 11.18 seconds.

The results concluded that Snowflake is faster than BigQuery in terms of performance.Winner: Snowflake

Snowflake vs. BigQuery: Pricing

The last and probably the most important factor of our Snowflake and BigQuery comparison is their pricing plans and affordability. As mentioned in the upper sections, they both provide separate storage and compute, but we didn't discuss the computing costs.Interestingly, both Snowflake and BigQuery have different ways to calculate computing costs. While Snowflake calculates the prices based on time usage, BigQuery focuses on the data amount spent in scanning the queries.Let's discover more about their pricing plans:

Snowflake Pricing

Snowflake offers you a monthly amount of $23 per terabyte if you opt for upfront payment; else, you can also choose their $40 per terabyte (monthly average) if you choose their on-demand plan.Snowflake has separate pricing plans for the compute. It has divided its service into seven different tiers for data warehouses. You can avail of it for as low as an amount of $0.00056 per second.Visit Snowflake's official website to check out its pricing plans in detail.

BigQuery Pricing

With BigQuery, you have the following two payment options with storage:

•      A flat rate of $20 per terabyte (monthly) for uncompressed and active storage.
•      Pay $10 per terabyte (monthly) for long-term storage.

Note: Google offers the first 10 GBs of monthly storage for free. If we look at BigQuery's compute pricing plans, it charges you the on-demand queries for $5 per terabyte. It also gives you the option to buy 500 slots at $10,000 (monthly flat rate) or $8500 (annual flat rate). Note: Google offers the first 1TB of monthly storage for free. Visit BigQuery’s official website to check out its pricing plans in detail. Users seeking on-demand and pre-purchasing pricing plans as per their data needs and spending on a per-second basis should opt for Snowflake. While users looking for a charge per usage basis should go for BigQuery. BigQuery's web console also provides an estimated number of scanned data before the run to help you get an idea of the total cost. Winner: BigQuery

Final Decision: Snowflake vs BigQuery?

We compared both Snowflake vs BigQuery on various factors. While we have concluded a winner from our findings and personal opinions, we leave the final decision to you to pick up the better option.As per our comparison, BigQuery won in the features and pricing department, while Snowflake won in the performance department. While both are neck-to-neck competitors in all domains, our results conclude BigQuery as the better data warehouse solution.

To ensure business process automation, Robotic Process Automation (RPA) platforms and utilities feature development environments to create workflows that automation agents then follow. These development environments are usually No-code platforms. Instead, they offer a drag-and-drop interface, so as to be easily accessible for non-developers who want to build necessary processes. As an alternative to designing workflows from the scratch, many platforms use machine learning models to evaluate actions performed by a human and translate these actions into workflows. The virtual agents perform actions following these workflows both with and without human supervision or intervention.[lwptoc skipHeadingLevel="h1,h4,h5,h6"]It is normal for RPA platforms to possess some sort of cognitive behavior or artificial intelligence as it usually uses computer vision for training the bot agents in virtual scenarios or machine learning algorithms to enhance bot decision-making. Apart from that, products that belong to the RPA category often possess analytics capability and a central medium for maintaining and controlling all the bots active across the digital infrastructure of the business. RPA approaches can be implemented in any domain of an organization where manual processes can be automated but are mostly used in finance and operations, sales, and supply chain departments.

Platforms/Tools enabling RPA

To qualify as a Robotic Process Automation utility, a platform must be capable of:

• Deploying bots into third-party apps and platforms
• Allowing users to design workflows for dedicated bots to follow, via less or no-code interface
• Enabling drag and drop options for better interaction
• Streamline workflows and routine tasks for employees, both with and without human supervision

10 Popular Robotic Process Automation RPA Platforms in 2021

• UiPath RPA | Robotic Process Automation

UiPath is renowned for automation. It makes the whole RPA process easy for every person and allows robots to acquire new skills and knowledge base through AI and machine learning methodologies. Despite being a free and open-source tool, UiPath is committed to bringing the digital revolution to the lives of millions of people around the world, thereby boosting business productivity and efficiency, employee engagement, and customer experience -CX. This hyper-automation platform possesses the top-of-the-line Robotic Process Automation (RPA) solution with a full suite of capabilities that allow every organization to scale their digital business operations at an overwhelming pace. UiPath RPA has already streamlined millions of iterative, astonishing tasks for businesses and government organizations around the globe including approximately 50% of the Fortune 500. UiPath was recently recognized as the top utility on Deloitte’s Technology Fast 500.

• Automation Anywhere - RPA | Robotic Process Automation

Automation Anywhere is widely recognized for Robotic Process Automation (RPA), assisting customers to streamline end-to-end business functions with intelligent software bots. AI-based digital bots that perform iterative and routine tasks, result in dramatic productivity boost, optimized CX, and high employee engagement. The company features the world’s only cloud-based and web-based automation platform that incorporates RPA, artificial intelligence, machine learning, and business analytics, yielding remarkably lower TCO, immense security, and high scalability than conventional monolithic platforms. Its Bot Store is the world's first and largest venue that provides more than 1,200 pre-built, intelligent automation solutions. Automation Anywhere is responsible for deploying over 2.9 million bots to assist some of the world’s famous enterprises across all industries in all 6 continents as well as than 90 countries

• Blue Prism

Blue Prism is the market leader in intelligent automation tools for businesses, revolutionizing the way work is being done. Blue Prism has users in around 170 countries and more than 2,000 businesses, including Fortune 500 and public sector organizations, that are adding value with modern working methods, improving performance, and sparing a great number of hours annually. Their intelligent digital bots are smart, secure, scalable, and interactive to all.

• Laiye RPA

Created by Laiye, a leading RPA+AI solution, Laiye RPA is one of the few RPA platforms that offer advanced AI capabilities. Laiye RPA can streamline manual and iterative routine tasks, sparing time and focusing their attention and efforts on core business operations and high-value tasks. Laiye RPA is easy to understand and you can start using it efficiently within a few hours. It features more than 400 pre-write commands which can be utilized in all sorts of projects, including those with a variety of lengthy and difficult workloads. Enriched with AI and Machine Learning utilities, Laiye RPA is applicable in a wide array of domains ranging from data entry assistants to automated invoice processing. Many businesses are leveraging from Laiye RPA by streamlining tasks of different departments including Finance, Supply Chain, Customer care, Healthcare, etc.

• Rocketboy

Rocketboy is an RPA tool that helps in process automation, through Software Robot, centric around ROI. It performs optimization in three ways:

1. It provides flexibility and compatibility with all the environments and domains that users want, from small and mid-sized organizations to enterprise-level businesses as well as on-premise and cloud environments.
2. It is focused on providing an agile environment, by allowing the simultaneous execution of independent processes in parallel. For instance, It can streamline and perform up to 10 different processes, in the same environment, simultaneously.
3. As a scalable software utility, it allows you to build robots by yourself without having any prerequisite knowledge.

Rocketboy is widely used among the masses as more than 100 business analysts build their own bots or automation solutions every day.

• Aiwozo

Aiwozo is another Intelligent Process Automation software tool introduced by Quale Infotech. It combines the conventional RPA capabilities with Artificial Intelligence (AI) to acquire a higher level of automation. It offers a user-friendly interface that allows non-developers to adapt and learn the new technology much faster with minimal or no prerequisite knowledge. The integration of AI with conventional RPA fuels the automation with the judgment-based engine in combination with the Cognitive approach of AI like Natural language Processing (NLP), Machine Learning (ML), and Speech recognition.

• Pega Platform

Revolutionizing the way the world develops software, the Pega Platform accelerates digital transformation within the organization by combining leading artificial intelligence, robotic process automation, and customer engagement models. With its user-friendly interface, developing an application, whether it’s simple or complex, is not a struggle anymore. Pega’s model-based unified approach offers an enterprise-level, agile tool with a no-code development environment.

• Laserfiche

Laserfiche is another renowned software solution provider for intelligent content management and robotic process automation. Using intelligent workflows, digital forms, document management, and analytical tools, Laserfiche eliminates the need for manual processing and streamlines iterative tasks, boosting the overall efficiency and performance of businesses. Laserfiche is the trendsetter in introducing paperless offices with enterprise-level content management tools for more than 3 decades earlier. For now, Laserfiche is featuring cloud, machine learning, and AI technology to encourage organizations in more than 80 countries to accelerate their digital transformation drive.

• Appian

Appian assists businesses to develop apps and workflows within no time, by providing a low-code development environment. Integrating human resources, technologies, and data in a single workflow, Appian can help companies to unleash the true potential of their resources and enhance business outcomes. Most global enterprises use the Appian tool to enhance customer experience, acquire operational supremacy over their rivals, and follow global risk management guidelines and regulations.

• Automate Robotic Process Automation

Automate is an award-winning Robotic Process Automation software tool developed by HelpSystems. Serving 15,000 businesses in 100+ countries around the world, HelpSystems is a trusted technology partner of the corporate world since 1984. With a lengthy portfolio of solutions and services, HelpSystems assist organizations from all industries to develop better IT infrastructure and boost their digital transformation drive. Their automation software offers 5 times more value than other RPA platforms with a no-code development environment and unrestricted bots. Their form-based development allows users to quickly develop customized bots and enjoy the perks of having more than 600 pre-built automation solutions. The resultant bots can be deployed as supervised or unsupervised human counterparts for parallel execution of the iterative tasks. Whether you need one bot or unlimited bots, Automate offers high scalability, so you can unleash your full digital automation potential. Automate integrates and streamlines independent applications and technologies that fuel your business process without any need to write even a single line of code. Whether your requirement is to automate the compilation of month-end reports, transfer thousands of files, or fetch data from different APIs, Automate will help you automate your routine tasks so you can focus on the core business operations strategic activities.

Conclusion

RPA platforms incorporate bots to streamline iterative tasks within software platforms or web applications that are traditionally performed by human counterparts. This approach is used to spare time and eliminate the need for human assistance to perform time-consuming, repetitive, and routine tasks.

In 2021, dynamic pricing made headlines when the prices of everyday goods such as toilet paper and hand sanitizer changed dramatically. More common examples are happy hours at your local bar, airline pricing on travel websites, and rideshare surge pricing.[lwptoc skipHeadingLevel="h1,h4,h5,h6"]

Dynamic Pricing in a Nutshell

Dynamic pricing is a compelling strategy that can help brands run their business profitably while keeping up with the competition across channels. To put it in simple words, dynamic pricing is changing the discounts, or markdowns, on the price of products based on their KPIs. It is also called demand pricing, dynamic markdown, time-based pricing, or surge pricing based on its application in different markets.One of the most common examples of this pricing strategy is the end-of-season sales. While off-season merchandise is sold at discounted prices, goods that are not affected by changing styles or weather can be sold at a constant price or minimal discounts.

Related: How to Start an eCommerce Business with Low Budget in 2022

Dynamic pricing strategy automates price adjustments based on data-driven algorithms fuelled by personalized information provided by each company. Factors like demand changes, inventory, and competitive data are also considered. To be successful, dynamic pricing needs to combine the age-old wisdom of the brand with machine learning and adaptable software.

Different Dynamic Pricing Strategies to Capture New Markets

Dynamic pricing strategy can appear in a few forms. Each of them can be used for achieving different goals.

• Segmented pricing: This strategy offers different prices for different customers. That means that the customers are divided into segments. For example, high-value customers can be offered higher prices. Here we can assume that they might put service speed and quality over the price.
• Time-based pricing: Companies can use this product pricing method when they want to charge more for providing some faster services. This means that you’re going to pay more if you want to have same-day service, or if you reach the company close to the end of the working hours.
• Changing market conditions: As you know, the market situation can change due to various factors. However, businesses must act accordingly. If sales begin to fall for some reason, the company will go for the strategy of lower prices.
• Peak pricing: Many industries can use this strategy to charge more during peaking hours.
• Penetration pricing: The penetration pricing strategy is used when businesses want to reach a large portion of the market. By doing so, future customers get familiar with the offered product. In order to do so, companies set prices that are below the market prices and tend to increase them gradually.

The Effect of Dynamic Pricing on Profitability

A dynamic pricing strategy isn’t new. The basic idea of adjusting pricing to match demand is as old as pricing itself. In fact, pricing used to be based on haggling. A fixed price seemed more “fair,” It was certainly less time-consuming for brands. Customers caught on to the idea and now expect fixed prices, especially in the retail market. But that’s changing.Using a dynamic pricing algorithm enables brands to capture the most revenues from their products. It enables brands to optimize their pricing based on real-time inputs instead of setting a price over the long term and either pricing too low and giving up margin needlessly or charging too much and losing sales.It also ensures that the customers who value a product the most have the opportunity to purchase it.

Why Do Other Pricing Strategies Often Fall Short?

If you refuse to be dynamic, you’re left with two basic pricing strategies. To achieve your desired profit margin, you can set a static price based on your variable costs or price.These pricing strategies assume you will achieve the sales volume necessary for your desired profit margin to cover your fixed costs. You may find more brand manufacturers prefer a fixed-price strategy over a dynamic one. Most retailers, however, prefer fluctuating prices because they allow them to have more control over sales volume.

Also Read: How to Launch a Product in 2022 [Complete Guide]

Fluctuating prices are basically what dynamic pricing used to be. You compile market data, process the data, and then adjust prices as needed. This process can take weeks, especially if done manually. Your customers are all charged the same price on the ground while you capture and analyze data. Prices are still dynamic, but they’re slow and rather unresponsive in a rapidly changing market.In other words, these pricing strategies can fall short because your customers are more dynamic than you are. They shop around. They look for better prices, better products, and better choices. If you’re not responding, then you’re losing them to competitors who will.

How does Dynamic Pricing help Brands?

Though the primary advantage that may come to mind is increasing profit margins, there are several other benefits of implementing dynamic pricing. Let’s discuss.

• Create demand to keep the stock health and age in check

At the right time, a rightly priced SKU can help boost sales. This helps in keeping in the freshness of stock and the availability of sizes/variants of a product. Products that are either discontinued, near expiry, or meant to last only for a specific period, need to revise selling price as per their planned lifecycle. If a brand is handling this manually across categories and points of sales, it can become near impossible to track and monitor. With the right dynamic markdown algorithm, prices can be tweaked to control the sales of a product.

• Understand your customers

Collecting data is a key part of implementing automated dynamic pricing. This lets brands gain insights into the consumer’s behavior, patterns, and preferences. It can translate to more actionable data which can feed your algorithms to perform better, leading to an efficient pricing strategy.

• Make informed decisions

Implementing dynamic pricing allows brands to access real-time pricing and demand trends. This can help make informed decisions on the price change of a particular product or category. Knowing the competition’s price trends ensures products are priced just right, which aids in increasing sales and profitability.

• Reflecting demand

The digitally savvy consumer is often aware of changes in price as the demand for a product changes. Many a time, they are also willing to pay a premium to get exclusive access to a product. Similarly, it is known to them the prices of seasonal goods will change as the weather changes. Dynamic pricing lets you reflect the demand of a product at its price without compromising on the brand image.

• Increase revenue and profits with a healthier working capital

One of the most significant outcomes of using dynamic pricing software is to take into consideration demand and supply, competitor strategies, and price fluctuation in the market. All of this data is crunched in real-time to deliver an optimum price for staying profitable. Dynamic pricing can be used to lower prices to increase sales, meet sales targets and avoid the accumulation of deadstock, thereby freeing up the choked revenue stream for the companies.

End Words

Although there may be a stigma around dynamic pricing, the reality is that it can maximize profits and track expenses. Using algorithms and machine learning, you’ll employ price optimization, ensuring you set your goods and services at the best price.If supply and demand impact your product significantly, dynamic pricing may be the best strategy to ensure a steady flow of sales. What’s more, the data you gather after implementing dynamic pricing can reveal your products’ perceived value during times of atypical demand.With insightful strategy and attention to detail, you’ll find that a dynamic pricing model can help take your business to the next level.

If you are a data science enthusiast, then your curiosity about the life cycle of data science projects is quite understandable. Knowing such important processes is essential in developing a better understanding of the overall subject. Data Science has come a long way since it was first introduced and is constantly evolving with time. Data Science works on data as the main subject, and all the studies and researches are conducted to derive more from the available data.

To feed all the inquisitive data scientists with the information they need, we have covered the life cycle of data science projects in great detail in this blog. Keep reading to find out about the steps involved in the life cycle.

What is a Data Science Life Cycle?

You may think of a project's data science life cycle as recurring stages that are required to be completed, and its deliverance to the client is dependent upon the successful completion of each step. Even though the life cycle contains similar steps, each company or organization follows a different approach. Data science projects require collaboration and are unsuccessful without a proper team effort. Different deployment and development teams come together on one platform to work on the given data and study it to derive various solutions and their analysis.

The data science life cycle encompasses all stages of data, from the moment it is obtained for research to when it is distributed and reused. The data lifecycle begins when a researcher or analyst comes forward with an idea or a concept. Once the concept for the study is accepted, then begins the process of collecting the relevant data. Data is stored after it is collected by the research team and is made available to other researchers to be used in the future. Once data has reached the distribution point, it is stored where other researchers can access it.

Why Do We Need Data Science?

Not too long ago, we didn't have enormous quantities of data, and it was readily available in a well-structured form to be easily stored in documents and sheets. However, as the data size increased with time, keeping big data and maintaining it became quite an obstacle and required extra effort. Companies dealing with gigantic data sizes can not rely on Excel sheets or a few folders for their storage; they want an improvised solution.

The need for maintaining and analyzing the vast data amounts gave birth to the idea of Data Science, which solves this problem using its complex algorithm, and robust technology. Data science is necessary to process, analyze, and interpret data safely. It helps the organizations better plan, set realistic goals, get a proper understanding of their current data, and focus on growth. The prominence of data science in the past few years has caused a spike in demand for data scientists throughout the world.

Five Stages of the Data Science Life Cycle

Data Science has come a long way since it emerged almost three decades back. Problems like these require a proper set of steps to tackle the issues correctly. Over the years, data scientists have developed a life cycle for data science projects and adhere to the process while working on data science problems. We all love shortcuts without realizing the damage they can provide. Some organizations prefer to jump towards the methods to solve the problem directly, without going through the proper steps. Sometimes these shortcuts solve your problem, but they almost always prove detrimental in the long run. Following the data science, life cycle steps ensure that the problem is being tackled to its core and provide a much better and more detailed analysis. The data science life cycle is divided into five steps, and we have listed the steps below along with their brief overview.

1. Business Understanding

Before you start working on your client's model, learn about the obstacles they're facing to apprehend their needs. Most people skip the pivotal step of understanding the actual problem and directly jump to the next phase and often end up in a failure or not fulfilling their client's demands. Understanding your client's issues is essential to building an efficient business model. Conduct thorough research to learn more about your client's business and ask them their expectations. Don't be reluctant to spend your time on the understanding phase, take help from the relevant people, conduct multiple meetings, and do whatever is required until you have understood the existing problems and issues. Business analysts are normally given the duty to collect customer information and send it to the data scientists team for analysis. Identifying and analyzing the objectives with the utmost accuracy is crucial, as even a tiny mistake can result in a project's failure.

2. Data Collection

Data science is non-existent without data, so collecting data is one of the most crucial life cycle stages for data science projects. When you have clearly understood your client's requirements and have analyzed the existing system and its problems, it's time to map down how to collect the required data. Consult your client, conduct team meetings, and do proper research to develop your data requirements and the methods to obtain them. Seasoned data scientists have their own ways to source, collect, and extract data to meet clients' expectations. Usually, the data analyst team is assigned to obtain the data, and they either source data via web scraping or with third-party APIs.

3. Data Preparation

Data is primarily obtained in a raw form, and the proper alignment of the scattered form is required to perceive it as information. It has to go through a cleaning process and be arranged in a proper format to be understood and used in an analytical step. The process of refining data is called data cleaning and is the core of data preparation. Once the data is presented in a structured form and is free from useless information, it helps you devise a strategy much better. Multiple sources are used for extraction during the data collection process, but they have to be compiled together in an understandable form for proper analysis. When data is typically acquired from various places, it sometimes is incomplete or has many gaps to make any sense for analysis. Data scientists have designed multiple methods to extract the missing piece and help structure the data. They also take the help of the exploratory data analysis (EDA), which identifies the important process of conducting initial research on data to find patterns, detect anomalies, and test hypotheses using statistical results and graphical representations.

4. Data Modelling

Data modeling is perhaps the core of the data science life cycle. In this step, the data scientist has to choose the appropriate model depending upon the problem. Using structured data as input, a model then outputs the desired result. Once the model family has been decided, the data scientist has to choose the right algorithm depending upon the model family that would give the best results and implements them effectively. Data scientists use the modeling stage to find data patterns and derive insights. The modeling stage marks the start of the entire data science system's analysis and allows you to measure the accuracy and relevance of your data.

5. Model Deployment

The final step of the life cycle of a data science project is the deployment phase. The step focuses on developing a delivery procedure to deliver the model to the users or a machine. The complexity of the deployment step depends upon the nature of the project. At times, it would require you to display your model output, and sometimes it would need you to scale your model to the cloud to thousands of users. Normally this step is taken care of by the application developers, SQA team, data engineers, machine engineers, and cloud engineers.

FAQs

Q. What is the life cycle of a data science project?

Ans: The life cycle of a data science project comprises the five stages that lead to the project's completion. The five stages are listed as follows:

1. Business Understanding
2. Data Collection
3. Data Preparation
4. Data Modelling
5. Model Deployment

Q. What is the first step in the data science life cycle?

Ans: The first step in the data science life cycle is business understanding. Data scientists should start with understanding their client's requirements first before jumping on to the next steps.

Q. What are the final stages of data science methodology?

Ans: The final stages of data science methodology include structuring the data, choosing the appropriate model, and then deploying the model.

Final Thoughts

Data science is the field that revolves over statistical methods, innovative technologies, and scientific thinking. We have tried to cover the data science life cycle in this blog and have tried to explain every step concisely and clearly. Still, if you are unclear about anything, don't hesitate to comment, and we will answer your queries ASAP!