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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Mobile apps for a business is a strategic decision to stay ahead of competition in today’s world. There are millions of mobile apps in the app stores of major mobile platforms at present. Mobile users are given many options to choose from when it comes to apps serving a specific purpose, which makes it challenging for app owners to have their apps stand out from the rest.

There is no second chance when it comes to impressing users enough to have them keep using the app. But the first challenge for any business intending to release an app is to get people to download it. Considering the key factors of a mobile app’s success right from the beginning of app development is crucial. Ultimately, the success of the app comes down to the business’ goals and the nature of the app itself.

This blog explores a few important reasons why mobile apps for businesses fail to get downloaded by the target audience.

Insufficient Market Research

The app market is very competitive, which means a well-designed app with an appealing UI alone won’t be enough to succeed. The development of an app should start with extensive research that covers every aspect of the app and its purpose.

This includes identifying and researching the right target market, end-user expectations, current market needs and app development trends, rival apps and the features they offer etc. The research, in the end, would make it easier for the development team to define the prime objective of the app and how it’d meet the business’ goals.

Randomly choosing a mobile OS

While conducting market research, it’s also wise to identify the most influential mobile OS in the target market. Once the concept of the app is made clear, the next step is to choose the app’s mobile platform. The wrong platform means the app simply won’t reach the target audience. A mobile platform based on the target market exponentially increases the likelihood of the app succeeding, provided it serves its purpose well.

Ignoring the hottest tech trends

There’s the idea of getting a lot of hype for the app to give it a great head start. Today’s businesses leverage modern technologies to achieve this. Out of all of the hottest technologies leveraged in app development today, AR and VR are the best. AR and VR in an app grants a whole different, unique experience to app users, which would make them want more. There are other great technologies that can be used in apps too. Conclusively, the technology used is also a key factor in an app’s download ratio.

Incompetent marketing

Regardless of how well the app is designed and the technology it leverages, it would still not succeed without proper marketing. The idea is to create a pre-launch buzz and develop an effective launch strategy. The target market needs to be aware of the app and what it can do for them that other similar apps can’t.

Ineffective app marketing strategies and poor app store optimization leads to failure to raise app awareness that will then lead to lower app rankings.

Conclusion

Apart from the ones above, there are several other factors like adequate UX, great app security, extensive app testing etc. that directly tie into an app’s chance of success. The app developers at AoT are well-versed in every aspect of app development – from design to marketing and promotions. Contact us to take a better look at our success formula for mobile apps.

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We could say AI has been lying dormant for many years without major breakthroughs. But in the past few years, AI evolved into a form that propagates its vast potential across many industries around the globe. One of the most intriguing applications of AI is in the digital marketing industry – more specifically, mobile marketing. Big companies like Amazon leverage AI to predict users’ buying behavior, make recommendations on their previous purchases, notify them of deals on products they have shown in interest in previously etc.

DemandBase recently conducted a survey and revealed that 80% of the surveyed B2B marketing executives had predicted that AI will revolutionize the industry by 2020.

Here are a few ways how mobile app marketers can leverage AI.

Automated Reasoning

AI can augment a mobile application to have automated reasoning capabilities – something which no other existing technology today can do. Automated reasoning is all about understanding various aspects of reasoning automatically. With AI and machine learning combined, the app will have its own ‘will’ to reason for itself without human intervention, and subsequently make quick decisions based on various factors. This way, the app will be able to give a more personalized experience to users, based on their usage patterns.

Learn Purchase Behaviors

Once the app gets a lot of downloads, there should be a marketing strategy in place to maximize revenue. One of the many ways to increase revenue is upselling. Using emails, push notifications, in-app prompts etc. to encourage people to purchase everything the business has put up for sale is a better strategy than focusing fully on acquiring more customers.

But doing all this without caution can simply force the user to uninstall the app. This is where learning purchase behaviors is important. With AI, the app can learn a user’s purchase behavior, and drop targeted notifications and suggestions to that user with deals they would be interested in.

For instance, if they have recently purchased cosmetic items, they probably won’t be purchasing the same anytime soon. The app can understand this with AI-powered analytics, and instead encourage the user to buy items that complement the cosmetics he/she recently bought.

Recommendations

One major reason for app abandonment is when the app fails to deliver relevant, engaging content to users. Even push notifications and in-app prompts would be useless if the end user isn’t interested in the presented deal or offer. As a matter of fact, it’d only bother them too much enough to get the app uninstalled.

Because today’s mobile users use powerful mobile hardware, super-fast internet connections, and get great UX from every digital solution they rely on, they prefer apps that can provide a tailored experience. If the experience from the app isn’t satisfactory, they won’t be requesting the devs to fix it with an update. They’d just find a competitor with a better app instead.

AI-powered apps can monitor user preferences and the choices they make during their use to figure out what interests the user and what keeps them more engaged. The app can then use this information to serve users better, saving their time and effort, with relevant recommendations.

Conclusion

The first few use sessions of the app is crucial to whether they will keep using the app or not. AI can make these sessions much more engaging and convenient to users. It can help execute targeted mobile marketing strategies effectively. However, AI needs to be leveraged in apps the right way.

AoT can help with that if you are looking to harness AI in your app. Get in touch with us today to get to know what AI can do for mobile apps.

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