A few years ago, only big corporations with a focused objective of rapid growth invested in augmenting themselves from the inside out to thrive in a potentially highly competitive future. But today, even SMBs are coming forward to invest in business transformation approaches that involve capitalizing on powerful new digital technologies and trends.

Modern businesses need to operate more efficiently and establish themselves in new markets faster than before. Additionally, many businesses are also focusing on improving customer experiences in order to improve business outcomes. It’s safe to say that business transformations will be gaining even more momentum this year.

However, achieving such a transformation is a big challenge in itself. As a first step, IT teams should devise a digital strategy that addresses all the limitations of the business including legacy technology limitations, and make the most of all the strengths of the business.

Here are a few important trends that could help a business succeed in transforming itself efficiently.

Focusing on one digital approach

A recent DXC Technology report found that companies tend to invest in a single digital approach to accelerate business transformation. There certainly will be a lot of big strategic commitments involved. Another surprise is the fact that many organizations are actually avoiding hybrid traditional digital strategies and going for an approach that can essentially unify their business without compromising its integrity and security.

Leveraging next-generation IoT platforms

As new IoT platforms pop up, businesses are exploring new opportunities to merge their traditional data sources to new ones, obtain more accurate data inputs, enhance existing real-time data analytics capabilities etc.

Cloud & AI for a smarter IT infrastructure

Businesses are closing in on realizing the idea of a smarter IT infrastructure. To make it happen, many businesses rely primarily on the cloud and Artificial Intelligence. An effective combination of the cloud and AI can help build an ecosystem driven by AI-powered applications with nigh unlimited resources. Such applications grant more power to users while also offering lower reaction time and better, more localized analytics.

Improving services with better decision-making capabilities

Companies have realized that data is the key to a faster growth. They now mine tremendous amount of data to uncover valuable information that will help them make informed decisions. This trend has led to an increasing demand for AI & ML-based tools and big data analytics capabilities. Making more educated decisions subsequently improve the services an organization offers.

Shutting down enterprise data centers

Data centers are slowly going obsolete, and it is evident this year. The proliferation of the cloud led companies to shift their workloads onto the cloud. Many companies move their mainframe workloads to specialized data centers while most others are shutting down their data centers. Traditional data centers are running out of steam. But the bright side is that companies can serve distributed customers better and access higher bandwidths to improve functionalities.


Business transformation is the initiative enterprises are most keen on taking this year. They adopt different approaches for the purpose but often fail to realize that sometimes it’s the little things that could have the biggest impact. The trends mentioned in this blog should help an enterprise in its venture to trigger a transformation to thrive in a competitive world. If your enterprise is on the lookout for next-generation digital solutions that can facilitate digital transformation, AOT can help you build one instead. Talk to our experts to learn more.

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The evolution of mobile phones into a must-have gadget in people’s day-to-day lives also established the dominance of mobile apps and the significance of data. With millions of mobile devices and mobile apps in circulation, massive amounts of data will be generated.

Determining useful data sets from data silos, and mining those data can help businesses uncover invaluable insights that would help them understand and serve customers better, and improve their decision-making. The same strategy is being used today to build cutting-edge mobile apps that are better than their predecessors.

Seems all too fine, doesn’t it? Here’s the catch.

The booming app market gives customers practically uncountable number of choices for apps that can perform a specific single task. This results in a great many apps running out of steam in the market and disappearing simply because they were outpaced by their competition.

To thrive in such harsh conditions, mobile app developers will need to leverage every advantage they can when building apps. It’s data that can give them such advantages. But where to start when you have an immense data pool?

This is where big data comes into the picture.

Investing in big data

With the right kind of tools and analytics strategy, big data can help developers build robust apps that serve customers better while offering a great user experience. Such apps most certainly score high in the market.

Still however the question persists whether big data is actually a big deal that it claims to be. This question is one of the reasons why some organizations are hesitant when it comes to investing in big data.

But the answer is quite obvious when you observe the matter with mobile apps.

For businesses, mobile apps serve as a powerful tool to connect with their customers via their personal mobile devices from anywhere. For the app to become successful, it should first satisfy its userbase in terms of features, functionality and experience. An app can achieve this in the best way possible with the insights hidden in the app’s user data. This user data will have intricate details on users’ behavior in the app.

Studying user behavior would in turn make it possible for businesses to understand user expectations, the problems they encounter when using the app etc. Rectifying the pain points will make an app seem a great option to the users, leading to them recommending the app to other users. This subsequently increases app downloads and the likelihood of success for the app.

Long story short, it is big data that can give businesses such game-changing insights. Here are a few other facts that make big data a big deal when it comes to mobile apps.

Big data helps with marketing apps

The app needs to turn some heads when it launches. So marketing is fundamental. Big data analytics can help app makers categorize market segments based on the market potential, identify audience expectations, and devise app marketing strategies accordingly.

Big data helps enhance user experience

User experience is a critical aspect that businesses should prioritize when developing mobile apps. Even if the app’s fully functional and efficiently serves its purpose, sporting an annoying UI could simply frustrate its users to a point where they will actively seek a replacement with a better UI and UX.

Big data analytics in real-time combined with powerful machine learning algorithms can give app makers a comprehensive overview of user behavior highlighting common usage patterns. This allows developers to determine what their next course of action should be in order to improve the app’s user experience and add more value to it.

Big data helps scale up app revenue

Even if the app scores high on all fronts including UX, interface, features, and functionality, it may not generate expected revenue if it lacks a proper engagement strategy. The app makers should know how to engage their target audience and keep them engaged while enticing them to make purchases.

With a pool of big data about user behavior which includes their likes, dislikes, location, expectations, purchase history etc. app makers can determine what type of push notifications should be sent to a specific set of users for conversion. This also helps them devise a good engagement strategy which can subsequently scale up the revenue generated by the app.


Everything points to the fact that big data will have a crucial role in the future of digital technology; particularly mobile app development. Big data, at present, is being widely used by app makers to increase app visibility, traffic, and revenue. Organizations across the globe preparing to adopt big data emphasizes the fact that big data really is a worthy investment when it comes to mobile app development.

The experts here at AOT can help your business use an app that wields big data technology effectively to dominate your target market. Contact us today to for a clearer overview of big data and its benefits.

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Many factors influence business transformation and evolution today. But among all those factors, data stands out unrivaled. The data that a business generates hide the key to the business’ evolution with respect to its operations. However, it’s not easy to mine those valuable information from such tremendous amounts of data unless big data analytics is involved. Modern day technologies make it possible for businesses to uncover valuable insights from the data and figure out the key to dominate in their respective industries.

So basically, a business needs advanced data management and analytics capabilities, data scientists, and an efficient infrastructure to leverage their own data to trigger an evolution that keeps up under dynamic market conditions and enables them to stay ahead of the competition.

Realizing this, many businesses today try to utilize the power of big data analytics to improve themselves. But not all of them succeed. Successful big data integration is a challenge in itself, and that’s what we are going to discuss about in this blog.

Bringing big data into the picture

Data integration impacts the processing, collection, and transfer of data within an organization regardless of whether the data is old or new. Big data integration is for large, complex data sets. The integration itself is complex, and should be done carefully while keeping in mind the importance of the resulting accuracy for better decision-making.

Big data analytics is a whole another process with which businesses can reap great benefits. We’ve discussed that bit before. But integrating big data is the first challenge to be addressed.

Here are a few things to consider for successful big data integration.

The right talent

Big data combined with disruptive technologies like AI and Machine Learning can deliver astonishing results. In addition, many new tools evolved in the big data sector recently – from modern relational database tools to data layouts designed to reduce storage footprints and improve accessibility, which can make things much simpler for enterprises.

The new Hadoop ecosystem, the advancements in analytics, efficient data management frameworks etc. present enterprises with different types of approaches for integrating big data. However, it’s easier said than done because to do all this, the enterprise will need the right talents. It’s best to rely on people or organizations with great experience when it comes to leveraging big data technologies.

Data quality is a priority

Many businesses tend to give data quality a thought after implementing big data analytics. This is certainly not a wise approach. Data quality should be prioritized before big data integration. There will be semi-structured and unstructured data involved which can complicate data set integration.

The analysis itself requires relevant, good quality data to deliver good results. The business needs to assess their data quality just to check whether they can have relevant data sets to feed to the algorithm once the integration is done. Just a lot of data is not the only condition that should be satisfied for implementing big data.

Data is your asset

The seemingly trivial data that a business generates, if mined the right way, hides a lot of real and assessable values. Not all data are valuable however. Data science helps differentiate relevant data. The point is that a business’ data is an important resource that helps make better decisions in the future leading to better productivity and enhanced growth rate. While integrating big data, make sure all the relevant data are safely secured, managed, and accessible. Data is an asset and should be treated that way rather than as a means to better times.

Be aware of the risks involved

Big data and analytics are sophisticated. The complexities obviously emphasizes the presence of risk factors. With big data analytics, the business gets to visualize and predict several outcomes. But the accuracy of these predictions depend on how well the integration was done. Doing it the wrong way may lead to unfair results, erroneous analyses, and misleading numbers.

This is why it’s crucial, during integration, to understand how things can go wrong, where things can go wrong, and how wrong things can get. Risk assessment helps in integrating big data the way it should be integrated.

Possible synchronization issues

Once the big data platform is online, data will have to be imported from multiple sources for analysis at different rates and on different schedules. This means there is a good chance for the data to go out of sync with the originating system. For instance, the data from one source might be outdated while data from a different source isn’t.

In addition traditional data management, data transformation sequences etc. also raises the risk for the data to go out of synchronization. This should be taken into account before big data integration, so as to devise a data migration management strategy that minimizes risks.

Various challenges

It’s important to be ready for any challenges that arise while integrating big data, including big data solution cost and compatibility, data volume, data transformation rates, data validation mechanisms, associated expenses etc. Another challenge is to implement a system where the data are processed at high speeds so the results are delivered and made accessible on time. Achieving that level of performance will require bespoke solutions and qualified technical support.

As of now, considering where technologies stand, an enterprise is better off seeking assistance from an experienced big data specialist and data scientists rather than investing in an in-house team. Laying a solid foundation to big data is the first step to using big data the right way. That’s why the initial steps should be taken under the supervision of big data experts.

AoT’s expertise in big data comes from our experience and commitment when it comes to mastering disruptive technologies so we can leverage them the right way. We can help integrate big data into your business while reducing associated risks. If you like to learn more about big data integration and how we can help with it, get in touch with us.

All that huge clusters of structured, non-structured, or semi-structured information that a business generates hides valuable insights that could essentially boost the business’ growth momentum. These data sets are referred to as big data, one of the hottest trends at present. We now have access to several big data analysis tools to make sense of this data and reveal patterns for businesses that’d give them great benefits when leveraged efficiently.

Many companies have already started taking the trend seriously, and are coming up with innovative ways to implement and use big data. Here are a few of their methods that we can learn from.

Customer behavior and preferences prediction

Organizations tend to give a lot of importance to customer requirements. So they employ many approaches to understanding what a customer needs from them, and how they can deliver it with the right functionalities and feature-sets. A modern business also considers one other invaluable factor that has a key role in this scenario – customer behavior.

Today’s big data analytics enables businesses to mine big data from several sources including social media channels, company websites, and other platforms to understand customer behavior itself. This gives them good insights on customers’ buying decisions, effective conversion triggers and other patterns as well customer preferences.

Though predictive analytics has been around for a while, the advent of big data vastly transformed the landscape. With the right big data tools and expert help, businesses can now get an edge by understanding how their services impact and influence customers’ purchase behavior better.


Big data obviously wouldn’t have been this popular if it hadn’t proved itself useful in effective decision-making. As mentioned in the section above, the insights from big data analysis reveals patterns and trends, centered on which an organization can devise appropriate strategies and make lucrative decisions.

They can also use big data from their operational processes for analyses to identify painpoints. This decision-making capability granted by big data can hence account for better profitability while granting them an edge that can help them potentially outpace competitors faster. An added benefit to this is customer satisfaction.

Customer segmentation

The increasing customer acquisition costs forced organizations to figure out other alternatives, one of which being customer segmentation. Most companies now consider it critical to target marketing promotions through customer segmentation.

Correlating profile information of the behavior of customers on various channels and their purchase history can give an organization insights on how they can target various customer segments. Personalizing the offer for specific customer segments would reduce acquisition costs. Business Review publications from Harvard and other popular sources reveal that there are organizations that had attained 70% improvement in their conversion rates by targeted marketing promotions. Big data now arms them better to go further.

Corporate giants like Time Warner, Amazon etc. uses big data for customer segmentation identifying trends among customers’ purchase behavior and then leveraging the findings in their marketing and sales strategies. Essentially, companies will be able to get the right product to the right users with personalized offers tempting them to make a purchase even if they were not planning to.

Fraud detection

Regardless of the industry, financial crimes and data breaches are the most common challenges faced by organizations. This used to be a global problem for organizations before big data came into the picture. Now big data analytics help organizations detect security vulnerabilities and take preemptive measures against internal and external security threats.

For instance, the analysis algorithms can detect unusual debit/credit card transactions and can alert a bank about the likelihood of a potential theft. This way, the bank would be able to suspend further transactions while they get in touch with the card owner.

Big data analytics helped VISA ascertain $2 billion in probable incremental fraud opportunities and rectify the vulnerabilities beforehand. Analytics on emails and phone calls is also possible now to detect vulnerabilities that are otherwise difficult to identify.


Big data’s potential is criticized by some. But with the cloud gaining great prominence in the present digital era, we can definitively say that effectively leveraging big data can be a great asset to competitive organizations. It can help them augment their security, increase their profitability, and get a higher ROI all while growing at a faster pace.

Organizations adopting big data analysis practices on a wide scale are catapulting the trend to mainstream, and the next phase is expected to impact new products and bring about more innovations. This is the right time to get your business initiated. When it comes to big data, AoT is the right place for you for the right beginning. Get in touch with us now to learn how we can help you begin with big data.

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Blockchain – One of the most significant developments in IT in recent years has the potential to turn insights into assets for businesses when combined with analytics, and promises data security and integrity on a different level. It still hasn’t gone mainstream but is on its way there subtly transforming businesses, financial corporations, and even governments.

According to Wikipedia, blockchain is basically a growing list of linked and secured records or ‘blocks’. The World Economic Forum (WEF) defines it in a different way.

A technology that allows parties to transfer assets to each other in a secure way without intermediaries. It enables transparency, immutable records, and autonomous execution of business rules.

“Immutable” is the keyword here. Thanks to blockchain, the information in a network remains in the same state, and impossible to alter, as long as the network exists. The ongoing evolution of blockchain is expected to improve many areas including but not limited to immutable entries, audit trails, timestamping etc.

When Big Data Meets Blockchain

You can’t find a good connection between blockchain and big data in the context of Bitcoin. But in a scenario where blockchain itself is a ledger for financial transactions, things would be different. The prospects are surprising, and this applies to even stock trades and business contracts.

This is also why the financial services industry is keeping watch on blockchain, which can potentially reduce processing time from days to minutes.

In the financial services sector, blockchains will take the form of a grand canyon of blocks that will have full history of every recorded financial transaction. All it lacks is analysis because what blockchain can essentially do is to provide integrity for the ledger.

This is where big data comes into play.

What Big Data Analytics Can Do

This year, a consortium of 47 Japanese banks partnered with Ripple, a blockchain startup, to test a blockchain project that facilitate money transfers between bank accounts. The goal was to reduce the cost of real-time transfers, as real-time transfers are riddled with risk factors like double-spending and other potential transaction failures. Blockchains managed to avoid most of the risks. In this scenario, big data analytics can make a significant difference one of which, for instance, is that it can identify patterns in the way consumers spent. It can also identify risky transactions far quicker and better than through any other current means. This can considerably reduce real-time transaction costs.

For sectors other than banking, blockchain adoption is primarily a security enhancement measure. Healthcare, retail, and government establishments have started leveraging blockchain to prevent hacking and leaks.

In Real-Time Analytics

Real-time fraud detection was practically just a concept till the arrival of blockchain. Organizations relied on using technologies to predict and prevent attacks contemplating past events. With the blockchain’s database record for each transaction available, they can use real-time analytics to mine for patterns if necessary.

This feature, however, can be seen from two different perspectives.

  1. Because it can provide a record of every transaction, there is a concern that this can be exploited for wrong purposes.
  2. Such improved transparency in data analytics granted by blockchains also promises analytics accuracy much better than that achieved with other means.

A Massive Potential to Uncover Data

Blockchain is gradually establishing its presence across multiple sectors. Considering its own growth, the data within the blockchain on banking, microtransactions, remittances etc. will soon be worth trillions of dollars. The blockchain ledger is speculated to be worth at least 20% of the total big data market by 2030. The potential revenue is huge enough to overshadow the combined revenue of Visa and Mastercard.

As big data will be crucial for this, data intelligence services will start popping up everywhere to help organizations uncover social and transactional data, and identify ‘hidden’ patterns.

Simply put, blockchain would most likely be the harbinger of new forms of data monetization by creating new marketplaces where businesses and individuals can share and sell data and insights directly without a middleman. Businesses intending to leverage blockchain will have to use the best AI/ML solutions on top of the blockchain-driven data layers to get a competitive edge in the present market. The wide-scale adoption of Bitcoin and the growth of blockchain in parallel combined can revolutionize conventional data systems to facilitate faster and secure data transactions in a seemingly insecure cyber realm.

It’s high time to start thinking about leveraging blockchain for your business. AoT technologies have the AI/ML capabilities to support just that. Feel free to talk to us to learn more on how blockchain combined with AI/ML can transform your business.

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