Cloud Computing, Artificial Intelligence, and Blockchain Technology
The importance of data in modern tech can hardly be over-emphasized; because there are so many services and products, there have become so many reasons and channels for collecting user or enterprise data. Companies leverage data to improve the user experience of customers, while in-house, there is a need for effective data accumulation for record-keeping and effective operations. As we clamor and advocate for more frictionless operations in our businesses and everyday activities, we simultaneously create a channel for more data to be collected and used in order to automate processes.
In fact, the entire reason why we say companies and organizations should ‘upgrade’ is so that our services or operations are faster. However, this increase in speed or quality in service that we call for can only be achievable when operations are automated i.e. there is a digital record of that operation happening before, then when it wants to happen again, it happens with less human efforts because the existing data are enough to implement the operations automatically.
At times, there might not be any need for a previous occurrence of the event in order to automate it, we just have to program whatever digital platform or channel we are using to carry out the operation seamlessly without human effort or with the aid of minimum human effort as the case may be. The entire point is that as more and more companies go digital and more transactions or services become automated, more data would be collected and stored for efficiency.
The issue is that not just B2C companies need to collect these data in order to satisfy their customers but also B2B. ‘SaaS or ERP’ companies also need to collect data from the large or small corporations they serve. Companies, in fact, collect a lot of data on a day-to-day basis because they don’t just collect those from their customers but also internal operational information for smooth and easy operation. In the advent of this, some companies have made it a business model to help other companies store data in the ‘cloud’.
The cloud computing services are responsible for helping these other companies (small and large) worry less about both user data and operational data. These cloud service providing companies include Amazon Web Services (AWS) by e-commerce giants, Amazon, Google cloud services by Google Inc., and even Microsoft Azure by Microsoft Inc.
In the bid to improve user experience and maintain competition, (obviously, every layer of technology also has its own competitive battles), these cloud service providers have begun leveraging the enormous data in their hands to create ‘smart services’ and products for their clients. They leverage Machine Learning to provide so many smart services for their customers and clients like famous virtual assistants like Alexa by Amazon and Google voice assistants by Google. The essence of machine learning is to make sure the vast amount of Big data available in the cloud is well utilized to make products smarter and services more efficient.
On the other hand, blockchain technology seems to be a different beast entirely. Although the technology also deals with data management, blockchain is more concerned about data privacy, transparency, and data security. It leverages cryptography and decentralized form of data management to effectively secure user data and is fast becoming one of modern-day most important technologies.
However, these three forms of data management technologies seem to barely be hitting the strides we would have expected, especially as a unit working together. There is enormous value that can be derived if these three forms of technology — cloud computing, machine learning, and blockchain technology — could all be utilized effectively. In fact, my prediction is that the future of data management in tech will involve the strategic application of these three forms of technology. In this article, we will be doing an analysis of each individual technology and its features, then we shall also be seeing where and how they intersect and most importantly the promise they hold for the future of the IT industry.
Enter the cloud
According to Microsoft Azure, cloud computing is the delivery of computing services — servers, storage, databases, networking, software, analytics, and moreover the Internet (“the cloud”). Companies offering these computing services are called cloud providers and typically charge for cloud computing services based on usage, similar to how you are billed for water or electricity at home.
Cloud computing has become a major aspect of modern tech. The need to store data has made it a necessity for individuals and corporate entities to seek ways for storing their data. For individuals, hardware seemed to be the most feasible way to achieve this as people made use of flash drives or USB hard drives to store their data and files.
On the other hand, companies had even bigger responsibilities with respect to data storage as they have to not only store customer data but also data about their internal operations. These companies leveraged on physical data centers — buildings occupied by servers — and the bigger the size of the company, the bigger the data centers required to store all the company’s data.
In fact, buying and stacking up hard drives to increase server size for your organizational data, is the least demanding aspect of using data centers. As companies expand and take up more customers, they have to ensure that they keep expanding their data storage capabilities in order to cater to the increasing number of users of their platform. This can be so demanding and sometimes we hear cases of sites or software crashing because they have run out of server capacity due to an explosion in the number of users. The fact is, the cost and effort in leveraging hardware for data storage as an individual or an organization, is very demanding and as in most cases, there was the need for a more convenient software or digital alternative that would be better.
The likes of Dropbox and Google drive have extensively tried to solve the issues of data storage for individuals but the real essence of cloud computing is mostly found in B2B. Cloud computing is more lucrative and more influential as a sector in the tech industry when it comes to its application as a service for business entities. As stated before, the large amount of data these companies have to store and keep storing has made it a necessity for better solutions to come up. It is no surprise that the major players in the cloud computing space are the big tech companies who have already made a name for themselves in order aspects of IT.
The three most dominant cloud computing services are AWS (Amazon Web Services) by Amazon, Google cloud services, Microsoft Azure. AWS leads in terms of market share and it remains the most lucrative channel for the e-commerce giant, a testament to how lucrative cloud computing service can be. However, just like the definition of cloud computing already given above, these services are like regular subscription models and customers are charged based on usage. The charge can be centered around the features an organization would like to have, the duration of the subscription plan, or the number of customers that they would be offering their services to.
Many companies have moved their data to the cloud because of the numerous advantages in it. Amongst these numerous advantages includes:
- There is an increase in the speed of operations in a cloud computing environment because IT resources are just one click away. The company is more efficient due to this increase in speed and achieve more with less.
- Money is saved because there is removal in cost of maintaining or expanding physical data centers or even employing more IT personnel to man the data centers.
- There is more certainty and assurance in operations due to the fact that there is another reliable party that handles all server requirements.
Therefore, so many companies are beginning to leverage cloud computing services either is Infrastructure-as-a-service (IaaS), Platform-as-a-service, or Software-as-a-service, a form of cloud computing.
We can teach machines how to learn
The whole essence of technology is automation. There is hardly anything that a computer or machine can do, that a human cannot do. However, in order to save time and effort, we created machines and computers to help us carry out these activities. The core value in these machines and computers is derived from their ability to repeat the processes or actions we outsource to them. This repetition — with little fuss — is what is called automation.
We can hardly talk about innovation and not point to the fact that its end goal is automation. Hence, as modern tech continues to advance and as more data is needed in the tech space, it is only imperative that we began to find a way to improve the level of automation and ‘independent thinking’, in how data is managed.
That form of automation or independent thinking in data management processes is called Machine Learning. Machine learning can be simply described as how machines “learn” by finding patterns in similar data. Machines here could mean an already existing algorithm, and the learning process now involves the algorithm being supplied more data or information that it can interact with, sieve, connect dots, all for the purpose of finding more patterns in the entire data framework or for the purpose getting smarter.
The concept of getting smarter involves the machine (algorithm) being given more information — mostly related to existing information — that it now relates with existing one to discover more insights about its most important duty or reason for being written in the first place. The entire concept of machine learning is that, as the program keeps getting exposed to more data, it begins to understand more and solve more problems all by itself. Machine learning is just like automation, but more like automation in data.
The majority of us don’t even know that we are already interacting with machine learning almost every day; the Google algorithms and other software platforms that make predictions or tailor our requests to previously consistent searches, are all examples of machine learning at work. These algorithms have gotten used to the consistent nature of your internet exploration, and have gotten smarter over time to the extent that they can predict the most likely next line of action from you.
There are several ways machines (algorithms) learn. It all depends on the learning of supervision given to the machine. The learning can be supervised, semi-supervised or unsupervised.
This is a time-intensive form of machine learning. In this form of machine learning, humans know the right answer and they supervise the machine to find it. In this type of machine learning, machines often learn from sample data that has both an example input and an example output. A typical example can be input data on the spending and saving patterns of individuals and output data on their credit ratings. The machine can be exposed to this duo of data sets for different scenarios such that it can then be able to apply it to new or future forms of situations.
In this type of machine learning, the machine learns from data for which the outcomes are not known. It is therefore given only input samples, but no output samples. The machine has to figure out the output by itself, although humans already have a predefined understanding of what that output is supposed to look like.
A typical example can be the machine being supplied with different and random datasets on various fields of study. The machine has to cluster similar information together and separate non-similar ones. For example, the machine can cluster data relating to Physics and separate them from say data relating to geography.
Semi-supervised learning combines the features of both supervised and unsupervised learning respectively. It is sometimes referred to as ‘weak learning’ because, in this type of machine learning, a model uses unlabeled data to gain a general sense of the data’s structure, then uses a small amount of labeled data to learn how to group and organize the data as a whole.
Semi-supervised learning helps in reducing the amount of labeled data required for learning a new model. A typical illustration of semi-supervised machine learning is when a model that is already trained in differentiating between images of human beings in terms of color skin, gender or height, is being trained to differentiate let’s say millions of images of mannequins and play dolls. The machine can use the existing defined labels for humans to create insights in differentiating amongst these mannequins or play dolls.
Semi-supervised learning, therefore, reduces the need for large amounts of labeled data sets. As a data scientist, one can even use such an advantage in a business proposal or internal pitch to an executive of a company. The data scientist can tell the executive that he already has a model that can quickly learn how to differentiate the mannequins or play dolls by using existing data on human beings instead of the executive having to spend trying to get millions of images on dolls and mannequins to train a new model.
Blockchain: The distributed Ledger
If you have ever created a document and shared it with Google Docs, you would agree that the document seems to be distributed and not necessarily shared. Everyone has access to the document at the same time and every change to the document is usually recorded in real-time with these changes being transparent to everyone as no one is compulsorily locked out whenever these changes are made.
Although Blockchain technology is more sophisticated than Google docs, the above illustration might just be the perfect example of how Blockchain technology works.
A Blockchain is majorly a digital record of transactions that is duplicated and distributed across the entire network of computer systems in the chain. A Blockchain collects information together in groups, also known as blocks, these blocks have certain storage capacities and, when filled, are chained onto the previously filled block, thus forming a chain or linkages of data referred to as “blockchain.” All new information that follows that freshly added block is compiled into a newly formed block that will then also be added to the chain once filled. The goal of blockchain is to allow digital information to be recorded and distributed, but not edited. Each block in the chain contains a number of transactions, and every time a new transaction occurs on the blockchain, a record of that transaction is added to every participant’s ledger. This form of a decentralized database management system is why blockchain is also often referred to as Distributed Ledger Technology (DLT).
Blockchain guarantees security and also transparency. The technology acts as a decentralized system for recording and documenting transactions that take place involving a particular digital currency. Blockchain technology enables the transactions around a particular digital currency — like Bitcoin — to be transparent to every participant and most importantly, without the need of a central authority like a bank.
Blockchain technology is one of the backbones of modern-day decentralized finance and is the proprietary technology behind the first major digital currency (Bitcoin). With the help of Blockchain technology, cryptocurrencies like Bitcoin have been able to operate in a decentralized form. Blockchain technology enables crypto transactions to be recorded across all ledgers, hence, bypassing the need for a single central body to control these transactions. One key feature of blockchain is immutability; Blockchain technology makes sure that no one can modify any data in the network without the consensus of the entire participants in the network.
Blockchain technology has improved trust because unlike central bodies (like a bank), who can decide how your money can be allocated without your permission or consent, an individual in a blockchain has absolute control of his/her money.
Why these three will define the future of modern tech?
It might seem like an exaggeration to say that cloud computing, machine learning, and Blockchain technology, will define the future of modern tech.
But is it really an exaggeration?
Take a cue from the previously detailed explanations of the roles these three forms of technology are already playing in the IT sector and it will be hard to argue against them playing major roles in the industry. Cloud computing seems to be a ‘normal’ service for companies and individuals these days as so many SaaS platforms are being launched daily. Machine learning seems to be a major competitive factor as companies try to improve on user experience and blockchain technology keeps having more advocates for larger use cases, especially from the crypto space.
Asides from utilities and clamor, what really is the case that the unification of these three forms of technology will play a major role in the future of the tech industry? That is the major burden of this article and the case behind whatever arguments or predictions are made. It must be noted that the fusion of these three already exists here and there, especially the fusion of machine learning and Blockchain technology, but why will they be playing a major role in the future of technology?
The already detailed insight into the three technological forms of this article lays the groundwork and helps the reader understand their intrinsic roles in society and the tech industry at large. It is evident that although they seem to all be useful in terms of data management and they seem to have specific and well-defined roles.
However, as already stated in the introductory part of this piece, the importance of data in these modern times, cannot be over-emphasized. It is this importance that makes the present and future fusion of these three forms of technology more likely and more all-encompassing.
The reality is that more companies will need to store more data as more and more people go digital, virtual, or remote. On the other hand, the need to make better use of these data would keep increasing as competition to create smarter, more intelligent, and efficient products, increases. Also, it now boils down to the already existing issue of trust and data privacy as more data is being given out on a daily basis.
Therefore, it is hard to bet against an even more increase in adoption of cloud computing, machine learning, and Blockchain technology but even hard to bet against these three forming ‘the’ alliance of the tech space.
Firstly, cloud computing already has the responsibility of making data storage easier and more accessible for corporate and individual bodies, but machine learning allows these organizations to solve problems in smarter ways and create more intelligent services for customers and users in the world. The integration of blockchain into the system adds more layers of security and its immutability feature changes the dynamics of so many sectors and industries.
Cloud services backed with blockchain technology will ensure that data stored in the cloud cannot be changed or tampered with. This sort of possibility can truly revolutionize industrial and even manufacturing processes as a whole. Unlike traditional manufacturing processes, with blockchain, records are stored and distributed across nodes in the network which is considered to be an efficient, secure, and transparent way to record transactions and vital data, thus, it makes it difficult for records to be changed or falsified, ensuring that industrial and manufacturing processes are transparent and secure. But not just that, and that is the beauty of combining these technologies; with machine learning, maintenance schedules and predictive maintenance can already be implemented in the entire process starting from records keeping to budgeting which have already been secure and transparent via blockchain backed by the cloud. All of these reduce time in manufacturing and production and also make processes seamless and more efficient.
The same analogy also applies to almost any industry leveraging tech to maximize its operations. E-commerce and retail platforms can also maximize their supply chain as blockchain technology enables the inventory management data or warehousing to be transparent and efficient. With the cloud already a major store for data, these companies can improve their operations by making them more transparent and combining them with already existing machine learning technologies being used by these companies. In 2018, IBM and Twiga Foods introduced blockchain-based microfinancing for food Kiosk owners in Kenya. They also used machine learning to improve the entire process by using machine learning to create an effective credit scoring system.
However, the added beauty of these trios is the seamlessness of their integration and why their fluid combination holds so much promise. It is possible to imagine only a sequential use case for their combination, that is, cloud computing must come first in terms of data storage, then blockchain in terms of data transparency or immutability, then finally machine learning in terms of maximizing data for intelligence or insights.
However, that is not the only possible dynamic this combination can manifest itself. For example, another use case is how intelligent models and machines that have been trained to explore the big data available to a company, can quickly derive insights from this bulk of data and forward it to blockchain in order to ensure that it is changeless.
This is so critical because the rush to gather more data would only increase in the coming years as companies try to maintain a competitive advantage. However, these clamors and pursuit of more data also make some companies gather unnecessary or inaccurate data at times. Hence, with the help of machine learning, these companies can intelligently harness the right data, send it to the blockchain to ensure that it is changeless. What blockchain brings to the table in this instance, is a more deliberate data mining and data processing activity unlike ever seen before.
Besides, the combination of blockchain and machine learning can also help reduce fraud and insecurity in financial services especially. Although, blockchain technology is one of the most secure technologies, the usage of simply private keys or public keys to complete transactions, also makes it a place common for fraudulent activities. Hence, by combining machine learning with some blockchain verification and registration processes, financial services can even be more secure because machine learning can help discover and monitor anomalies or suspicious endeavors.
A typical example of how machine learning monitors fraudulent activities was how PayPal, in their early days, created an algorithm that was able to combat identity theft or robots signing up. Captcha and other forms of machine learning algorithms have also been created in time past for similar purposes.
Conclusively, it is evident that data will only become more and more important in modern tech and people will try to innovate and find ways to ensure that data collected or mined is properly managed, utilized, and secured. The emergence of cloud computing services, machine learning, and blockchain technology have all taken data management to another level, but to meet the coming demands of trust, security, privacy, and increased convenience, these three forms of technology will most likely combine to play a major role in achieving these targets in the tech industry.