In order to ascend in today’s world where technologies are capable of making and breaking businesses, organizations are required to migrate their enterprise applications to the cloud. This amplifies the usefulness of enterprise applications manyfold. For example, an enterprise app that supports 100 internal users may support 100x more users on the cloud. Such features are a standard nowadays for enterprise-grade apps that are accessible to a wider value chain-facing user base.

Because traditional enterprise applications and their data are open to a larger audience now, they are expected to deliver the best user experience which is where technologies like AI and Machine Learning come in. Modern enterprise applications are expected to deliver smart, contextual experiences to customers and stakeholders. The cloud serves as the best platform for cutting-edge technologies like AI to augment a traditional enterprise application without hassle.

As a matter of fact, once on the cloud, the apps get even more benefits in the form of an improved cost structure, great scalability and flexibility, and the ability to quickly adapt to changing business needs. But all of these achievements that organizations expect can only be obtained after successful migration to the cloud. It’s seen by many organizations as a particularly complicated procedure.

But cloud migration doesn’t have to be that complicated and difficult. With the right kind of planning and good execution, cloud migration can be successful.

That said, here are the steps every enterprise should take before proceeding with executing their cloud migration strategy.

Application Inventory Assessment

Before migration begins, it’s a good approach to have an application portfolio inventory. The inventory will have the enterprise’s applications categorized. It’s important to assess this inventory and the applications’ dependencies including their physical and virtual server configurations, network topology, compliance requirements, security mechanisms, data dependencies etc.

Such an assessment would enable enterprises to determine an approach to get the best results from the migration. For instance, the ‘Modern Apps’ category in the portfolio inventory might already be on the cloud platform or can be easily migrated there. The ‘Legacy Apps’ category in the portfolio could present a big challenge when it comes to cloud migration. The risks may be too big. Then there are other enterprise-grade applications like web applications and Java applications. The enterprise would know which category they should begin with for the best results.

Complementing this approach, modern IT services also grant enterprises with the option of choosing the degree of cloud services for each of their applications while assessing the benefits of migration and estimating the cost of it. This doesn’t apply to legacy apps however. But cloud service providers can still provision servers and storage for such applications to run like they used to, without compromising the reliability users expect from them.

The most valuable opportunity, on the other hand, is associated with the migration of the ‘other’ kind of apps in the enterprise – the third category which includes enterprise-grade Java apps, web applications, and the likes. Migrating such apps to the right kind of public or private cloud results in a lot of cost savings.

Creating a Plan

Migrating and modernizing that third category of apps mentioned above requires careful planning so as not to add complexity, challenges or costs. The plan should take into account a number of factors including but not limited to:

  • The architecture of the application to be migrated and its dependency on its infrastructure.
  • The application’s security policies and tools involved that enforce these policies.
  • The tools that manage the accessibility to the systems on which the application runs.
  • Tools used by the team to deploy, manage, and troubleshoot the application
  • Application’s unique performance characteristics
  • Application’s awareness when it comes to the underlying network & hardware topology

Provided that the cloud service provider is experienced, the aforementioned factors can help your enterprise execute a migration strategy that appropriately prioritizes app migrations.

Applications that are already on the cloud with no dependencies apart from the immediate application stack can be managed with a managed public cloud service. Other enterprise apps that have multiple dependencies and relationships in the data center ecosystem should be quickly migrated to a managed private cloud.

Quick Migration for Cost Savings

Many enterprises prefer migrating with the help of their in-house team and choose a conservative approach to migration to cut costs. However, this approach too often ends up increasing costs in the long run; one major reason for this being the fact that the enterprise is essentially running two infrastructures during the migration incurring costs on both.

The best approach to mitigate risks while realizing cost savings is a quick migration. It’s possible to do this without assistance provided the enterprise invests a lot in rigorous planning. However, a more feasible and risk-free way is to enlist a cloud partner with expertise in public, private, and hybrid cloud.

The right cloud service expert can, within the budget, recommend the necessary services needed for successful migration based on the application portfolio inventory and the enterprise’s specific preferences. With their help, enterprises can get applications shifted to the cloud quickly and economically without being concerned about risks.

Conclusion

Moving applications to a managed public and private cloud properly ensures significant operational cost savings (of up to 60%) owing to the cloud’s optimized hardware utilization and the availability of efficient administration tools. But the pivotal component that influences the success of a cloud migration procedure is the expertise and experience of the party performing the migration.

If you are looking for a partner with the required expertise to make the migration happen without issues, you are at the right place. AOT Technologies, over the years, have been building our reputation as a reliable IT service provider specializing in software solutions and cloud computing technologies.

We have a team of qualified cloud experts who know their way around the most widely used cloud services and widely adopted cloud migration strategies. AOT is also fully capable of devising a migration strategy to deliver the outcome and benefits you desire cost effectively. Get in touch with us today.

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Digital disruption is what enterprises and technology companies are looking forward to in this age. It’s a welcome change by many organizations – a major shift in something that’s already working bringing with it a multitude of new opportunities and increased growth potential for businesses. Though the term is overused, its impact is real.

Take Uber for instance.

Uber is a digital service that disrupted the taxi industry and changed the way people operate when it comes to commute. The concept was fresh and the impact was huge.

But like every other technology or trend that garners a lot of attention, the idea of digital disruption is also surrounded by myths. These myths impede innovation by giving enterprises false hopes or deterring them completely from ever attempting to achieve disruption. That’s why it’s important to investigate these myths and understand the reality behind them instead of bluntly dismissing them and leaving digital disruption out of a brand’s growth strategy.

Here are a few of the most serious myths surrounding digital disruption.

Disruption is actually a bad scenario

The word disruption itself carries a negative vibe. When people hear the word, they tend to have negative thoughts. But disruption being a bad scenario is still a myth though. Anything that’s disruptive will have both positive and negative effects. It can be a threat to one enterprise and an opportunity to a different one. The bottom line is that disruption is good for a lot of enterprises. The trick is to figure out how to be one of those enterprises for whom disruption will appear as an opportunity.

If it’s a change, it’s disruption

Many organizations have a misconception that any change in an organization brought forth by technology or a change in culture can be termed as disruption. In reality, digital disruption is a significant, fundamental long-term shift in a system and not simply a change. Disruption may be enabled just by the existence of a technology or a trend. It’s a long-term effect which demands enterprises to devise a strategy to thrive in a post-disruption ecosystem.

The benefits of disruption are only for digital giants

This myth exists partly because of the huge publicity that corporate giants obtain for their contributions to technological advancements. There’s a public perception that companies like Google, Amazon etc. are the only disruptors in the game. These companies are often the first to achieve disruption which sets off a chain reaction of secondary effects that impact thousands of SMBs and large enterprises. They simply don’t get publicized much.

Hyped technologies are the most disruptive

This is probably the most widely believed myth surrounding disruption. Many organizations believe that hyped technologies are the most disruptive. For instance, blockchain and AI have been turning a lot of heads for the last couple of years. Companies have started seeing them as highly disruptive technologies.

Contrary to this, in reality, for a technology to be disruptive it has to be a mainstream favorite; widely adopted across the globe and with a number of secondary effects. AI and blockchain aren’t quite there yet.

Conclusion

Without some form of digital disruption, it won’t be easy for organizations to keep up their pace in the coming times. The digital landscape is undergoing dynamic transformations as more technologies keep popping up every year.

To seize the opportunities that disruptive technologies present, you will need powerful digital solutions tailored to complement your business goals. And when it comes to those kind of solutions, AOT is a proven expert. Drop us a message to see how we can help you leverage disruptive tech effectively.

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Over the years, businesses utilized a number of advancements – both technological and industrial, to accelerate their growth and increase their profits. Of all that which businesses have leveraged so far, one particular advancement stands out for its massive potential – Data Analytics.

With extensive data analytics, businesses can uncover valuable insights from the tremendous amounts of seemingly senseless and random data they generate. Armed with this information, organizations can tweak their business strategies for better results.

What makes this ‘big data’ awesome is the fact that it shows great promise for all types of enterprises irrespective of their sizes. Through big data analytics, enterprises can not only increase the revenue generated but also identify security vulnerabilities and gaps in their services and products. Big data is still an evolving technology however, and like every evolving technology, it is also shrouded by a lot of myths and misconceptions.

This blog debunks a few of the most popular myths that deter businesses from checking out big data.

Myth 1: All that matters is size

The first thing that anyone can think of when hearing the term ‘big data’ is the size. As such, there seems to be a myth that the main defining trait of big data is the sheer size or volume of data involved. This is not the case. Though size is certainly one of the main characteristics of big data, it isn’t the only important one.

The volume of data involved demands sufficient storage systems. But big data also requires high velocity data processing mechanisms to deliver relevant, more up-to-date outcomes. The data come from various sources which means the outcome may vary from what was expected. The data being handled by big data should be also be veracious and the insights from these data should provide value.

From all these facts, we can infer the most important traits of big data other than size or volume.

Velocity. Variety. Veracity. Value.

Neglecting these features can result in an improper implementation of big data that can complicate simple solutions and deliver irrelevant results while wasting resources.

Myth 2: Big data is a collection of high quality data waiting to be processed

Big data is a collection of a lot of data sets that can include both relevant and irrelevant data. All that data don’t necessarily have to be of high quality. As a matter of fact, without proper data filtration mechanisms, big data analysis can result in a lot of data quality errors.

Many organizations opt not to clean the data before processing to avoid the hassle of dealing with such large amounts of data. Data cleaning also requires the organization to invest more in big data. This practice most likely ends up with big data failing to do what it’s expected to do. For big data analysis to be effective, the data should be cleaned. This helps the system provide more accurate results.

Myth 3: Big data predictions about a business’ future are highly accurate

This myth is based on the assumption that the volume of big data is directly proportional to the accuracy of big data analysis results. The more data one feeds into the system, the more accurate the results will be. But that’s a myth however. Data certainly hold great value for modern businesses but there are other factors that are just as important if not more.

A big data system will never be able to accurately predict a business’ future by simply analyzing data. Factors like economy, technological advancements, human resources etc. directly influence the success of a business, and data simply won’t be factoring in everything that directly or indirectly influence the business’ success. What big data can do is extrapolate future possibilities by comparing and analyzing historical data.

If the business uses real-time data instead, the system will be delivering predictions based on a probability theory i.e. the results still won’t be certain. However, it’s true that the accuracy of forecasted results will be higher if the data fed are relevant and of good quality.

Myth 4: Big data is expensive

That’s the myth all right. This is the fact – big data ‘was’ expensive. Back when big data started turning heads for its potential, only big organizations and government bodies stepped forward to invest in it. The investments weren’t small either as organizations required large-scale data centers and talented big data professionals to wield big data properly.

But time has changed. As big data garnered praise from all directions, many vendors stepped forward reducing the licensing costs of big data tools and making them much more affordable to small medium-sized businesses. In addition, new tools and techniques are also being introduced to help smaller businesses perform big data analyses.

Myth 5: Big data is centered on machine learning technology

Machine learning certainly is associated with big data. But big data isn’t centered on machine learning. New generation big data analyses make use of complex machine learning algorithms to derive sensible and relevant insights from massive data sets. That’s the whole concept of Machine Learning – using data to uncover information that can help an organization or an individual make meaningful decisions. Both technologies are used in conjunction to deliver expected results. Big data can exist without machine learning.

Myth 6: Big data is all about analytics

What’s strange about big data is that it has multiple definitions, and most of them are accurate. A simple Google search would get you at least a dozen different yet valid definitions of big data from various sources. While some consider it as massive data sets, others see it as an analysis technology that can process massive data sets. The fact is that big data is something much bigger than data analysis with many capabilities to solve a variety of problems with data.

Conclusion

For big data to truly live up to its name in an enterprise, it should be seen as something more than a lot of data being processed. It needs to be managed and overseen by experts who can tell myths from facts.

If your enterprise is planning to leverage big data and is looking for expert guidance or automated solutions, you are at the right place. AOT’s deep expertise in a wide array of technologies including big data and AI/ML can ensure maximum big data ROI. Drop us a message to get started.

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Are businesses getting to grip with Artificial Intelligence and Machine Learning?

AI and ML have been creating a lot of buzz in the tech realm for the last couple of years. However, many businesses still aren’t clear about the long-term benefits of these technologies.

According to a new report by Microsoft named ‘Maximizing the AI Opportunity’, nearly two-thirds of business leaders lack clarity when it comes to potential returns from artificial intelligence.

The report surveyed 1000 business leaders in the UK. It may seem surprising but there are a lot of companies out there that still consider AI as an unjustifiable expense with more limitations than benefits. Many industry experts point out that AI shows promise but adopting it in now in its present state may lead to unpleasant surprises.

That said, businesses that have invested in AI in return for something did see tangible benefits. Many organizations don’t even realize that they are using AI in some form. As a matter of fact, getting started with AI is rather simple really.

This blog includes a few best practices enterprises can follow to get started with AI in the right manner.

Identify business problems

Before investing in AI, an enterprise should make sure that they have identified and individually assessed each of their business problems. This way it will be easier for them to determine if a specific business problem can be solved efficiently with AI. The problem can be anything from handling a lot of online customer chats to freeing up employees’ time so they can focus on core tasks; AI can solve pretty much all of it.

Assess enterprise readiness to adopt AI/ML

Once the enterprise identifies the problems that they can solve using AI, the next step is to assess the enterprise itself for its readiness to build and manage AI or ML-based systems.

Major cloud service providers like Microsoft Azure and AWS offer on-demand ML services for a number of purposes including image processing and speech recognition. In addition, tools and ML-centric software frameworks are also available for the enterprise itself to build custom ML models or in-house AI-based systems. For any of these systems to work, data play a crucial role.

The main question to ask here is whether the enterprise generates and captures the right kind of data to train predictive ML models to deliver the right results. Even if it collects the right kind of data, there should be copious amounts of them for the AI system to process. Both quantity and quality of data matter when AI-based analytics is involved.

Assess in-house skills to handle AI

Before getting started with AI, enterprises should make sure they have the right talents to manage their AI systems. It’d be a wise approach to assess in-house skills to handle the AI project. This way the enterprise would also be able to identify missing skills that they will need in their mid to long-term run with AI systems.

Microsoft’s report also found that many business leaders are not sure as to how they can provide employees with the skills needed to adapt to and manage the disruption caused by AI. There’s also the fact that employee roles might change once AI starts doing its job. This requires the organization to put considerable effort into training employees when needed and retain sufficient skills for stable functioning of the AI ecosystem.

Experiment with AI

AI’s only started being of use to businesses. As such employees may be hesitant to engage with it. It’s up to the organization to foster a work culture where employees can experiment with the implemented AI systems. The employees can start small, get familiar with the technology, learn how it works, and then tinker with it to get an idea of what it can and can’t do. The company can gather feedback from them and scale up to leverage AI better.

Ensure ethical handling of AI

At the end of the day, data-driven AI/ML systems are only as good as the data fed to them. Invalid, biased, or wrong data will drive an ML algorithm to deliver useless results. Many companies make this mistake of feeding flawed data into the system leading to poor results and ROI.

All factors considered, it’s a great practice for a business to use an AI/ML manifesto to ensure that the technology is used ethically and securely. The manifesto should lay out specific guidelines to overseers and everyone else in the company using the AI-system.

Conclusion

AI and ML tech are at their infancy even now and demands diligent human oversight. The technology should be nurtured to deliver unbiased, responsible outcomes that can give businesses a great edge in the market and trigger disruption. If the company is ill-equipped to leverage AI but is willing to invest nevertheless, it’s better to seek service from an established AI expert.

That said, it’d be a pleasure for AOT to provide you with assistance when it comes to new-gen technologies like AI, ML, and IoT. Feel free to dial us up to learn more about AI for businesses.

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Despite the various advancements in AI, AI investment and adoption have always had a gap between them. However, there is more than enough evidence to show that AI indeed promises various benefits including efficiency, automation, productivity, personalization and more. But the confusion surrounding how the technology should be leveraged is deterring widespread application of data-driven AI-powered business models.

Nevertheless, many prominent businesses pour huge investments in AI despite its lack of commercial support. As of now, mobile applications are one of the most prominent industries to embrace AI revolution. But the potential challenges along the way are still being questioned by many businesses.

Why is it that many businesses are still reluctant to adopt AI?

Let’s take a look at the central concerns.

Lack of understanding

AI lacks an exclusive categorization though it’s basically known as human-like intelligence. The technology isn’t new but is still at its infancy at this point. For the AI framework to be effective against specific problems, other technologies should be coupled with it. This subsequently makes the framework complicated.

In addition to all this, the AI’s dynamic nature to become better by self-learning makes it difficult for enterprises to fully comprehend a good approach to adopt an AI model. The funny thing is that many organizations use AI in the form of AI applications without realizing it. Understanding the technology well may require assistance from experts who know how to leverage its potential in the mobile space to meet business goals.

Uncertainty on value proposition

Businesses today realize how an effective mobile strategy can help them move forward to their goals. The prospect of channeling AI in mobile apps to improve the existing leverage they get from apps, however, is looked at with doubt. This is one reason why organizations aren’t keen on incorporating AI into their mobile strategies.

If the enterprise is digitally mature enough, they are likely to have human resources with more than enough technical expertise to comprehend business cases for AI investment. AI’s value proposition and the ROI are too vague for many businesses to see unfortunately.

Taking shortcuts all the way

Popular early adopters of AI include Google, Amazon, Apple etc. and they all share a few common traits concerning their attitude towards AI.

  • They invest in other trending technologies also including the cloud, IoT, and big data.
  • They are investing in AI models and frameworks despite the growing concern on the technology’s potential, ROI, and security.
  • They invest in more than one type of AI technology, including AI tools and apps for digital disruption.
  • Their innovation driven by AI is motivated by growth potential.

From these commonalities, we can infer that AI adopters that successfully implemented the technology are committed to expanding their AI infrastructure. Meanwhile, many other businesses are more interested in learning about shortcuts when it comes to leveraging AI so they can obtain immediate benefits.

There are no shortcuts when it comes to successfully deploying an AI model. It requires the organization to address various digital and analytical elements, define business cases, and establish an ecosystem for secure data storage, analysis, and transmission.

Conclusion

This may seem like roadblocks but in the end, AI will inevitably transform the way businesses function. It’s already becoming an invaluable resource that drives mobile innovation. Every app would eventually incorporate AI at least to a small extent.

If your business is set to jump on the mobile AI bandwagon, AoT technologies can help you take the first step. Talk to our AI experts to see if your business is ready to make the jump.

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