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blockchain technology

A Quick Guide to Blockchain

This is a quick guide to blockchain and blockchain technology.

In this guide, we’ll explain:

  • What blockchain is
  • Whether it is only used for Bitcoin and other cryptocurrency
  • The practical use of this technology
  • Which projects and companies have adopted blockchain

So if you’ve ever been curious about this technology, this article is the perfect place to find out.

Let’s begin!

What Is Blockchain?

blockchain projects

The concept of this technology is difficult. But at its core, the blockchain is quite simple.

A blockchain is a type of database; a collection of information, or data. For simplification purposes, think of it as a spreadsheet.

But unlike a regular spreadsheet on your computer, this one is duplicated thousands of times across a network of computers.

The network regularly updates the spreadsheet, unlike when you manually input information using Excel.

Technically, a blockchain is a shared and continuously reconciled database which isn’t stored in any one single location. This allows information to be distributed but not copied.

That’s why when it was first conceived by the person or group known as Satoshi Nakamoto, it’s original use was intended for cryptocurrency like Bitcoin.

But the blockchain and its technology has evolved far beyond its original purpose.

What Is Non-Cryptocurrency Blockchain?

blockchain technology trends

If you’re aware of blockchain, then you’re probably also aware that it’s the technology behind cryptocurrencies like Bitcoin. But its technology isn’t unique to digital currency.

In fact, the usefulness of blockchain doesn’t stop with currency. Some of the most promising projects have nothing to do with crypto.

And there are a lot of examples of blockchain applications that are being implemented at the moment. Countless more are surely yet to come.

But for now, here are 3 non-cryptocurrency blockchain applications you should be aware of:

#1 Identification Records

Most of us take our identification records for granted, like social security and ID cards or birth certificates.

But for a lot of people living on the margins of society like the homeless and refugees, that’s not the case.

In these extreme situations, it’s difficult to gain access to identification records and reclaim identities, funds, and personal property.

Blockchain identity platforms solve this problem. This technology enables these people to reclaim their identities and get access to much needed help.

For example, in Austin, blockchain technology is used to identify homeless people. This raises their chance to get access to social programs which they rely on to survive.

#2 Online Privacy

Currently, blockchain networks are being developed that enable users to browse the Internet anonymously without allowing websites to access their personal data.

The data is handled on a decentralized network instead of being handled by the website or ISP.

The data is broken into blocks and dispersed across the network, and only someone with the right decryption key can reassemble and access it.

In the future, we could see widespread use of this kind of data protection, as it becomes an essential part of cybersecurity services.

#3 Supply Chains

This technology has a significant impact on supply chains. It’s already used to trace products to their place of origin.

For example, you can potentially trace a loaf of bread back to the crop and farm it came from.

This kind of clarity and transparency over the supply chain from source to consumer could have a profound effect on consumer behavior as well.

As more people become eco-conscious, blockchain technology will allow them to source products that are actually sustainable and good for the environment.

Which are the leading companies in blockchain technology?

Blockchain companies are paving the way for the future in many areas of business. So when it comes to companies that use blockchain, it’s worth knowing who the biggest players are.

These are the top ten companies using this technology:

  • SALT Lending
  • Mythical Games
  • Gemini
  • Circle
  • Coinbase
  • Chronicled
  • IBM
  • Voatz
  • Steem
  • Shipchain

Recent Developments in Blockchain Technology

blockchain 2021

Blockchain is continuously evolving. It’s an interesting field of study and development, and a lot of researchers are making efforts to innovate it.

Here are some of the most promising blockchain projects and business ideas in 2021:

#1 Status

Status is a secure communication tool. The messaging app aims to provide users with a safe way of having a private conversation, while providing additional features like a crypto wallet, encryption tools, and a Web3 browser.

#2 Augur

Augur is a no-limit betting exchange. It enables users to trade crypto wagers on a platform that’s completely decentralized and uses a lot of prediction market protocols.

#3 OpenMinded

OpenMinded is a community for building apps. It gives users free access to a decentralized data pool of advanced cryptography techniques and machine learning through libraries such as TensorFlow and PyTorch.

#4 Namahe

Namahe aims to use this technology with AI to raise the efficiency within the supply chain industry. The main focus of the platform is creating economically sustainable value chains that connect to the global marketplace.

#5 OMG Network

OMG Network focuses on scaling the operational capacity of the Ethereum network. It’s a value transfer solution built for enterprise grade production, unlike many other protocols.

If you’re interested in learning more about blockchain and other innovative technologies, please continue to follow Demakis Technologies.

Or contact us if you want to learn about how our managed services can help you incorporate new technology into your business.

Data Mining

Data Mining

We’ve recently discussed data collection and data-selling technology on our blog. But what happens to big data once you capture it? You have to process it somehow. And that analysis and extraction of information from big data is data mining.

But understanding data mining is also more complex than that. So if you want to know more about this topic, you’ll enjoy this article. Let’s begin.

What is data mining?

Data mining is the process of analyzing large volumes of raw data (data sets) to extract information from it.

Typically, this information includes patterns, irregularities, and connections within that data.

Based on the findings, individuals and organizations can extract value from big data.

In most cases, this means generating statistical forecasts that predict risks, opportunities, and outcomes within the context of that data.

In other words: data mining is the process of finding meaningful information in big data.

How to extract data patterns in statistics?

understanding data mining

Technology is critical if you want to extract meaningful information from data sets. The reason for this is the volume, complexity, and structure of big data.

Typically, the data sets you capture can be:

  • Structured
  • Unstructured
  • Semi-structured

Even with the simplest, structured model, manually analyzing large data sets requires a lot of time and resources.

So instead, researchers use software and innovative technology like artificial intelligence (AI) and machine learning.

These technologies can automatically process and analyze data sets to uncover patterns from them.

You can then use these statistical patterns in the data and apply them practically.

For example, when launching a new product, you’ll want to know what your target audience is, and whether they’ll welcome its arrival.

On the other hand, as huge as big data is, it’s never complete. It’s always provisional. So instead of applying it directly, you may first want to test it against more or other sample data.

In the product launch example, you could examine its effectiveness against existing products through a focus group.

What are some data mining techniques?

what are some data mining techniques

New technologies contributing to data mining are continuing to evolve. As they become more accessible, data miners can use them to adopt them and develop new techniques to extract information from big data.

And according to the International Journal of Computer Applications, there are 16 different data mining techniques in use today:

  1. Data cleaning and preparation
  2. Tracking patterns
  3. Classification
  4. Association
  5. Outlier detection
  6. Clustering
  7. Regression
  8. Prediction
  9. Sequential patterns
  10. Decision trees
  11. Statistical techniques
  12. Visualization
  13. Neural networks
  14. Data warehousing
  15. Long-term memory processing
  16. Machine learning and artificial intelligence

Who can use data mining?

While you’ll need the support of managed tech services, the importance of data mining can be felt across fields and industries.

A data mining example and its common use is science.

Researchers can collect data sets from across their field and use AI and machine learning to analyze and extract crucial results and findings for their research projects (regardless of their location).

But the addition of data mining techniques and algorithms isn’t limited to science alone. And there are many other uses for it in both the private and public sectors.

Here are a few types of data mining uses:

  • People search
  • Credit reporting
  • Market testing
  • Advertising effectiveness
  • Researching political outcomes
  • Risk evaluation
  • And many others
data science news

Successful data mining steps you can take

Now, let’s take a look at how you can effectively apply data mining techniques.

Here’s a quick step-by-step guide on how you can make the best use of data mining:

#1 Choose the project carefully.

If you want to extract maximum value from big data, align your data mining goals with your top business goal.

When you know which information you need out of big data, it’s easier to collect, process, and analyze the right data to acquire it.

#2 Collect a lot of data from multiple sources

This is straightforward. The more data sets you use, the more varied the data is, and the greater the accuracy you’ll achieve for your forecasts using that information.

This step has the biggest impact on user behavior analytics and predictive analytics.

#3 Simplify your sampling strategy

Even when you use powerful data mining platforms to process large data sets, try analyzing smaller subsets of data instead.

Simplifying samples to make them clear and concise is the key to generating the best outcome from your efforts.

#4 Always use holdout samples

A holdout sample is a benchmark. It’s a reference point that you can use to evaluate the validity of your predictive models.

This ensures that your predictions aren’t based on other predictive patterns from a defined set of data. But, instead, on actual estimates from the real world.

#5 Refresh your models frequently

Once you generate a forecast or data prediction, start applying it to your research, business, or operations. But don’t hold onto it forever.

These models are only as good as the relevance of the patterns that you find. And as the data changes, it will affect the validity of your forecasts.

That’s why it’s essential to feed new data to the models every week, day, or even hour.

If you’d like to learn more about how Demakis Technologies can help you manage your data, contact us.

best websites to collect data from

Data Collection and Data Selling Technology

In this post, we turn to big data. Specifically, we’ll give you a brief overview of data collection and data selling technology today. We’ll address the following questions:

  • What is big data?
  • How does it affect your personal information?
  • Why and how do companies collect big data?

So if you want to know why companies like Amazon, Google, and Facebook think data is more valuable than oil, you’ll find the answer here. Let’s begin.

What is big data?

data collection services

Big data describes large volumes of data (or data sets) that are so huge and complex (and continue growing exponentially over time) it’s impossible to manage or process them using traditional software.

Typically, these data sets contain publicly available or privately permitted information about human behaviors and interactions online.

When the data is processed, it can generate statistics which identify patterns and trends among those activities.

What is publicly available personal information?

Publicly available data refers to information about a person that is disclosed to the public. But this isn’t always the case.

Data privacy remains an open topic since many .com companies regularly capture information without consent.

For example, Facebook had to pay a record $5 billion to settle a privacy concerns case in 2019.

These events and others like it have prompted nations and international organizations to create laws that protect personal data.

One such legislature is the EU General Data Protection Regulation. In this document, we can find the answer to the main question of this paragraph.

Data Collection and Data Selling Technology

And according to the GDPR, personal information is publicly available:

  • If it’s contained in official documents of public interest, or related to public officials;
  • If it contains the source of the personal data with permission for public disclosure.

What kind of data collection is there?

Currently, there are three types of big data:

  1. Structured: formatted data that can be stored, accessed, and processed.
  2. Unstructured: complex (and usually huge) data sets without form or structure.
  3. Semi-structured: data sets with a structured form that is unintelligible through that structure.

Why do companies collect data?

As a consumer, you may ask yourself: what are companies doing with my data?

Usually, companies capture data for one of two reasons.

The first has to do with user behavior analytics. Businesses want to get a deeper level of insight into how consumers interact with their brand, marketing, products, and services.

Companies will use a statistical representation of this behavior to align their sales and marketing strategies. The goal here is to use big data to persuade consumers to interact with this company instead of its competitors.

how to collect big data

The second reason companies use data is to create future forecasts, so they can uncover risks, trends, and new market opportunities. This is called predictive analytics.

Predictive analytics relies on several statistical techniques such as predictive modelling and machine learning. Companies use these solutions to extract value from present data and align it with their future business goals.

How to collect big data?

Companies can collect data in many ways and from many sources. Some capturing methods are technical, for example, website cookies. Others are more deductive, like Google Analytics.

That said, there are three ways companies can collect data:

  • By directly asking users to provide data
  • By indirectly tracking user behavior
  • By sourcing data from third parties

The most obvious way businesses collect data is through interaction with their websites.

Here, companies typically deploy all three strategies that we’ve listed.

For example, companies can use gated content to capture email addresses with user permission, or third-party software to create website heatmaps that track cursor movement on a web page.

Here are a few other big data collection methods:

  • Loyalty cards (retail and e-commerce websites)
  • Browser games (World of Tanks, Words with Friends)
  • Online gameplay (Fortnite, League of Legends)
  • Satellite imagery (Google Earth, Google Maps)
  • Employer databases (HR and headhunter databanks)
  • Popular email services (Gmail, Yahoo Mail)
  • Social media platforms (Facebook, LinkedIn, Instagram)
  • Ratings and feedback (online surveys, Google reviews)

Note: Companies tend to use managed services to protect their technology systems when capturing data.

Besides collecting information for business use, it’s common to see companies trade data either via data marketplaces or consumer data vendors.

Data Selling Technology

Personal data and big data are routinely bought and sold by companies. Data brokers are those who facilitate these deals.

The brokerage of data includes:

  • People search (Spokeo, ZoomInfo, White Pages, PeopleSmart)
  • Credit reporting (Equifax, Experian, TransUnion)
  • Advertising and marketing (Acxiom, Oracle, Innovis, KBM)
  • Political consultancy (Cambridge Analytica)
  • Risk mitigation

Before monetizing the data, data brokers use advanced technology to acquire, store, access, and process big data sets.

For example, data brokers typically use large private clouds to store these data sets. They can then use a combination of AI and machine learning to process the data to extract value and meaning for their customers.

What is the future of data collection?

Big data is here to stay. Companies will continue capturing data and using it to understand consumers and make predictions about future markets.

We’re still unsure how new privacy laws will affect big data. Or which new technologies will emerge to simplify data processing. All you can really do is stay informed.

If you’d like to learn more about innovative and emerging technology, please follow Demakis Technologies and continue reading about it on our blog.

Quantum Computing

Quantum Computing

Today we’ll explore one of the latest innovations in technology: quantum computing.

In this post, we’re going to address the following questions:

  • What is quantum computing?
  • How do quantum computers work?
  • What can quantum computers do?
  • Why are people investing in quantum computers?
  • And how long until we see their commercial use.

So if you’re looking to learn about quantum computing and expand your knowledge base, you’ll enjoy this article.

Let’s dive right in.

application of quantum computers

What is quantum computing?

Quantum computing is a new way of processing information by harnessing the laws of quantum mechanics.

In doing so, quantum machines promise to surpass the supercomputers of today, becoming the supercomputers of tomorrow

So what makes them different from typical computers?

Quantum computers vs. Classic computers

The difference between traditional computers and quantum computers is in the way they process information.

A classic computer uses “bits” of information. Long strings of bits encode information into ones and zeros. The machine can then use that to process the encoded data.

On the other hand, a quantum computer uses qubits (or quantum bits) to process information. A qubit allows a quantum system to encode data into two different states (ones and zeros), which behave quantumly.

That allows quantum computers to capitalize on the phenomena of superposition and entanglement.

What are superposition and entanglement in quantum computing?

Superposition and entanglement occur in the quantum realm at the quantum particle level (atoms, electrons, protons, and others).

Superposition is the ability of a quantum system to adopt multiple states at the same time. So, a zero can be a one, and a one can be a zero.

Entanglement, on the other hand, is a correlation between paired quantum particles. That enables them to exist in a quantum state regardless of the distance between them.

Simply put, let’s say that you have two qubits and that they form a pair. Changing the state of one qubit will instantly alter the state of the other qubit (even when separated by vast distances).

For example, if you have two strings of ones and zeroes like this 1010, changing it to this 0101 will change them both.

But why should these phenomena matter to you?

quantum technology

Why do quantum effects matter?

Quantum computing uses superposition and entanglement to process tremendous numbers of calculations at the same time. That enables quantum mechanical computers to use ones, zeros, and the superpositions of those ones and zeros.

These quantum effects make it possible to complete specific tasks that have previously been too great or complex for traditional computers.

The road to quantum computational supremacy may also lead to the evolution of universal quantum computers.

These systems may use quantum effects of superposition and entanglement to produce states that scale exponentially with the number of qubits they process.

What is the application of quantum computers?

While quantum computer services today are still in development, there are already practical applications of these systems.

For example, one promising application may be to simulate the behavior of matter. Giants in the car manufacturing industry – Volkswagen and Daimler are using quantum computers. Their scientists are creating models that imitate the behavior of particles in batteries of electric vehicles.

That is helping them find new ways of improving the performance of the batteries; to extend their power output beyond the current standards, for example.

Another application of quantum computing may be in cybersecurity services.

Quantum Key Distribution (QKD) will improve cryptographic protocols, like the one-time pad.

The problem with the one-time pad is the distribution of the random secret key. At the moment, it relies on exchange books. In the future, QKD will allow this distribution to happen at a distance.

QKD uses another property of quantum mechanics: any attempt to gauge or view a quantum system disturbs it.

So the two users who share the random encryption will receive the same code (sets of ones and zeroes).

But if an unauthorized person wants to access the same code, they’ll disturb the system, and the authorized users will be able to detect this because their secret codes won’t match.

Quantum tech trends

Is quantum technology years away?

Quantum technology is already here. And it’s commercially available. For example, QKD is already in use at the Institute for Quantum Computing, where researchers use it to develop quantum encryption.

But when will quantum computers be available to you?

The practical use of quantum computers is relatively low, mainly because they lack the power to replace traditional computers.

Currently, only large-scale enterprises and research centers see the benefit of quantum computers. The technology is still pretty low on the Gartner hype cycle.

Still, the future of quantum computing looks bright. With new research and development emerging every day, 20 years from now, all of us may use quantum computers in our everyday lives.

If you’d like to learn more about innovative and emerging technology, please follow Demakis Technologies and continue reading about it on our blog.

Top 7 Technology Trends in 2021

Top 7 Technology Trends in 2021

In this article, we take a look at technology trends in 2021.

Most of the technology trends are a result of COVID-19, the disruption it caused, and the accelerated digital transformation that followed.

The outcome is a tech landscape that’s adopting innovation in three key areas:

People centricity: Technology as a service of people, as the core of all business.
Location autonomy: Technology that supports the increasingly remote landscape.
Resilience: Technology that helps organizations adjust to any disruption.

Top 7 Tech Trends in 2021

As typical, all of the tech trends we’ll mention don’t operate independently. Instead, they build on each other, and cover multiple industries and facets of everyday life:

● Work
● Education
● Transportation
● Healthcare
● Retail
● Security
● And more

With that in mind, we’ve selected seven trends you need to watch out for.

Let’s take a look.

1. Internet of Behaviors

Internet of Behaviors (IoB) is a direct response to the disruption caused by COVID-19. As public and commercial organizations collect more data in both the physical and digital worlds, IoB could help them use that information to influence peoples’ behavior through feedback loops.

For example, institutions could apply IoB in education. Schools and universities could monitor students’ interaction with online courses and adjust the UI to reflect actual use in the remote classroom. As a result, IoB may enhance the learning experience in the New Normal.

2. Total experience

Total experience links different experiences to give organizations a more holistic view of how people want to interact with them and adjust those interactions according to desired business outcomes.

The total experience combines:
● Multi-experience
● Customer experience
● Employee experience
● User experience
● Product and service experience

By collecting this type of data and analyzing it with the use of AI, businesses can find out where all of these experiences intersect. In doing so, the role of technology in business can allow companies to take advantage of the disruptions caused by COVID-19, including remote work and virtual customers.

Top Tech Trends in 2021

3. Privacy-enhancing computation

Privacy-enhancing computation is a new data-sharing system. It combines three cybersecurity tech innovations that allow rivalling organizations to share, process, and analyze data with zero risks to confidentiality.

Pharmacy researchers from rival companies have used this trend to develop treatments for COVID-19. But its application could spill into other industries, as well.

For example, in retail, rivalling businesses could share data to penetrate a new market or manage a takeover of a third party.

4. Distributed cloud

A distributed cloud is a cloud service that distributes the cloud to different physical locations, while the public cloud provider remains responsible for controlling, managing and operating it.

According to Gartner:

Distributed cloud is the future of cloud.

Bringing cloud services closer to the organization could improve the capabilities of their public cloud. For example, it could increase response time, reduce the cost of data, and help with regulations that determine where data is stored.

Top Technology Trends in 2021

5. Anywhere operations

The anywhere operations model is “digital-first, remote-first” tech strategy. It allows customers, employees and partners to interact with an organization remotely.

For example, in e-commerce, online stores can allow customers to use click & collect if they don’t want their goods delivered at home. Or in finance, with mobile banks that transfer funds, open accounts, or handle payments without physical contact.

6. Cybersecurity mesh

The cybersecurity mesh is the next stage in the evolution of a “walled city” and a response to next-gen cyber-attacks.

It allows organizations to create a security framework around parameters specific to a person or object.

Cyber security providers already use it to encrypt data and keep systems safe by centralizing the way they enforce and distribute security policies.

7. Hyper Automation

As companies scrambled to accelerate their digital transformation in the wake of COVID-19, many of them did so without a clear strategy. Hyper Automation is a direct response to that problem.

Technically, hyper automation is the concept of automating all business activities, processes and operations with digital technologies. But instead of relying on a host of disconnected systems, hyper automation focuses on efficiency.

That means organizations have to create systems that are streamlined, optimized, connected, fast, and agile.

For example, many distributors rely on delivery management software. It enables them to control fulfilment throughout the lifecycle of every order and across the entire supply chain.

How will future technology affect business in the future?


This was our pick of the top seven technologies that will impact 2021. As technology changes over time, we’re sure more trends will emerge.

What will the influence of technology on consumption look like? What will be the up and coming role of technology post-COVID? And what will be the application of technology in business?

Only time will tell. All you can do for now is stay informed, and continue following our Demakis Technologies blog for the latest updates from the world of technology.