DEV Community: Emmanuel C. Onyema

The Building Block of Insightful Data Analytics: Asking the Right Questions.

Emmanuel C. Onyema — Thu, 26 Oct 2023 01:26:25 +0000

Introduction

As a data analyst, solving and providing data-driven solutions to problems is at the heart of what you do every day. These problems can be tiny or vast, simple or complicated; no problem is the same, and each requires a somewhat different strategy; yet, the first line of action in the data analysis phases is always the same: "Ask." In this article, you are going to learn how to ask effective and SMART questions to help you define the problem you're solving and make sure that you fully understand expectations.

Some Guiding Tips And Questions.

Here are a few guiding tips and questions to always consider:

Who are the stakeholders? Ascertain that you completely understand their expectations.
Define the issue(s) you are attempting to address with your analysis.
How accurate and reliable is my data? According to studies, incorrect data costs businesses 15% of their income and wastes 40% of analysts's time.
What measures and key performance indicators will you employ to assess your data, achieve your objectives, and determine success?
How can this data provide actionable insights and assist stakeholders in making decisions?
What is the full picture? Do you have other critical data points to give context to the rest of your data set?

Here, Dennis wrote more about some of these guiding tips and questions.

A framework for asking effective questions

The SMART framework ensures that our queries are clear, quantitative, actionable, topical, time-constrained, feasible, and connected with the business objectives. It helps streamline the data analytics ask phase, develop a precision culture, and leverage the power of data to unleash important data-driven insights and contribute to business success.

Specific

Specific questions are straightforward and narrowly focused on gathering information pertinent to the problem to be solved. This aids in the collection of information relevant to the subject at hand.

An example: What specific variables should we examine when evaluating the performance of our marketing campaigns?

Measurable

Quantifiable questions can be measured and evaluated. Asking measurable questions is critical for defining clear, quantifiable goals and successfully tracking progress. By asking questions that can be specifically quantified, such as in terms of numerical values, percentages, or specified units, they can help us create reasonable goals and provide the benchmarks we need to evaluate the performance of our data analytics initiatives, ensuring that our efforts produce concrete and quantifiable results.

Tip: Before you ask your questions, are you able to measure the answers?
An example: How many new leads can we produce in the next quarter by implementing our data-driven marketing strategy?

Action

Action-oriented questions accelerate the implementation of data-driven plans and initiatives by framing questions that stress concrete actions and solutions.

Tip: Before you ask the question: Will the questions lead you to devise some type of action plan?
An example: What concrete efforts can we take over the next six months to improve our data processing pipeline and reduce processing time by 20%?

Relevant

Relevant questions ensure that your analytical efforts directly contribute to resolving the issues you are tackling by developing queries that are tightly related to the problem you are solving. It is critical to concentrate on questions that will have a direct influence on the issue to be solved, unlike irrelevant inquiries that might waste time and resources.

Tip: Before you ask a question, evaluate how relevant it is to the particular problem you are trying to solve.
An example: Given the rising global market rivalry and the requirement for operational efficiency, how can we utilize data analytics to improve our supply chain management and eliminate needless expenditures without sacrificing product quality?

Time

Time-based questions often focus on establishing deadlines and timelines for attaining specified goals or milestones within a defined period. These questions are critical for instilling urgency and responsibility in the data analytics process.

For example, instead of asking "How can we increase customer retention?", you might ask, "How can we increase customer retention by 15% in the next two months by using social media ads?

By concentrating on these essential factors, you'll be better prepared to meet the problems you'll face as a data analyst and ensure you're asking the correct questions to achieve success.

Mistakes to avoid

leading questions: questions that only have or suggest a particular response.
Example: Most people find our new feature very useful, don't they? Instead, ask: What has been your experience with our new feature so far?
Closed-ended questions: questions that ask for a one-word or brief response only
Example: Were you satisfied with the customer engagement on our pages? Instead, ask: What is your opinion of the customer engagement on our pages?
Vague questions: questions that aren’t specific or don’t provide context.
Example: Does the tool simplify your tasks? Instead, ask: How has the tool impacted the speed of your data visualization tasks?

Conclusion

SMART Questions: Ensure your questions are:

Specific: Clearly define the problem.

Measurable: Quantify the problem or goal.

Action-oriented: Focus on steps to resolve the issue.

Relevant: Relate to the project’s overall objectives.

Time-bound: Set a timeline for achieving the goal.

Things to avoid when asking questions:

Leading questions: Avoid biasing the respondent’s answer.

Closed questions: Allow room for open-ended responses.

Vague questions: Clearly state what you want to know.

Credits

image credit: Artem Maltsev on Unsplash

A Beginner's Guide to Data Analytics: Understanding the Fundamentals

Emmanuel C. Onyema — Sat, 30 Sep 2023 01:50:54 +0000

In today's data-driven world, data encompasses a wide range of data types and sources. Businesses, organizations, and various industries of all sizes rely on data as a key to making informed and sound business decisions, uncovering hidden insights, generating higher revenue, and driving innovation. Therefore, this article will unravel the fundamentals of data analytics, paving the way for your journey into this exciting field.

What is Data Analytics?

Data analytics refers to the entire process through which a data analyst collects data, examines it, refines and standardizes it, analyzes it, and interprets it for the purposes of uncovering patterns, drawing conclusions, improving processes, identifying opportunities and trends, launching new products, providing great customer service, and making sound, thoughtful decisions. The list literally goes on.

Why is Data Analytics Important?

The importance and benefits of data analytics are numerous and extend across several sectors, industries, and use cases, some of which include, but are not limited to:

Informed Decision-Making: Data analytics empowers individuals and organizations to enhance their marketing strategy, and make decisions based on evidence and facts, rather than gut feelings, intuition or guesswork.

Business Growth: It helps businesses understand customer behavior, optimize operations, and gain a competitive edge.

Problem Solving: Data analytics identifies issues and inefficiencies, allowing for effective remedies.

Operational Efficiency: Businesses may discover problems in their prior performance and try to improve them with a full report of their past performance.

What are the Processes of Data Analytics?

Data analytics involves a series of interconnected processes that transform raw data into valuable insights. These processes provide a structured approach to understanding, analyzing, and deriving meaningful information from data. Here are the key stages in the data analytics process:

Data Collection.
Data Cleaning and Preparation.
Data Exploration (Exploratory Data Analysis - EDA).
Data Analysis.
Interpreting Results.
Data Visualization.
Communication and Reporting.

The data analytics process is iterative, which means that when new data becomes available or issues emerge, the process is examined to provide new insights and enhance decision-making on a constant basis. It is a dynamic field that demands both technological competence and subject understanding to optimize the value generated from data.

What are the Types of Data Analytics?

Data analytics encompasses various types, each serving different purposes and focusing on specific aspects of data analysis. The four primary types of data analytics are:

Descriptive Analytics: Descriptive analytics aims to summarize historical data to provide insights into what has happened in the past. It focuses on understanding and characterizing data.

Common techniques include calculating summary statistics (mean, median, standard deviation), creating data visualizations (bar charts, histograms, scatter plots), and generating reports and dashboards.

Use Cases: Descriptive analytics is used for tracking key performance indicators (KPIs), monitoring business metrics, and providing a snapshot of the current state of affairs.

Diagnostic Analytics: Diagnostic analytics seeks to answer "why" certain events or trends occurred by examining historical data. It aims to identify the root causes of specific outcomes.

This type of analysis involves advanced exploration techniques such as regression analysis, root cause analysis, and correlation analysis. It helps uncover relationships and dependencies in data.

Use Cases: Diagnostic analytics is valuable for identifying the reasons behind changes in performance metrics or anomalies, enabling organizations to address underlying issues.

Predictive Analytics: Predictive analytics uses historical data to make predictions about future events or trends. It focuses on forecasting and understanding how variables will behave over time.

Machine learning algorithms, time series analysis, and statistical modeling are commonly used in predictive analytics. These techniques enable the creation of predictive models to make forecasts.

Use Cases: Predictive analytics is applied in various domains, including sales forecasting, demand planning, fraud detection, and risk assessment. It helps organizations make proactive decisions.

Prescriptive Analytics: Prescriptive analytics goes beyond prediction by suggesting the best course of action to optimize a desired outcome. It provides actionable recommendations based on predictive models.

Prescriptive analytics leverages optimization algorithms, decision trees, and simulation modeling to identify the most favorable actions or interventions.

Use Cases: Organizations use prescriptive analytics to optimize supply chain operations, make personalized recommendations to customers, and determine optimal pricing strategies.

It's worth noting that these types of data analytics are not mutually exclusive; they often complement each other in a data analysis workflow. For example, diagnostic analytics may help uncover the causes of a problem, while predictive analytics can be used to forecast future occurrences of the issue, and prescriptive analytics can provide recommendations on how to mitigate or exploit the situation.

How to Become a Data Analyst?

Becoming a data analyst involves a combination of education, technical and soft skill development, and practical experience. While some start with complementary degrees in math, data science, or other related disciplines, here's a guide on how to become a data analyst with or without a degree:

Get a foundational knowledge.
Develop your technical skills.
Work on projects.
Learn how to present your findings.
Develop a portfolio showcasing your data analytics projects.
Apply for entry-level data analyst jobs
Consider certification or an advanced degree.
Attend data-related meetups, conferences, and workshops to network with professionals in the field.
Stay up-to-date with the latest trends and technologies in data analysis through online courses, books, and professional development opportunities.

Remember that becoming a data analyst is a journey that requires continuous learning and adaptation. Building a strong foundation of technical and analytical skills, along with practical experience, will pave the way for a successful career in data analysis.

What Skills do I need to Become a Data Analyst?

To become a successful data analyst, data analysts may need a combination of technical, analytical, and soft skills, depending on the specific project, industry, and organization they work in. Here's a list of some skills required for the role:

1. Techincal Skills

Programming languages: Python / R

Data Visualization: Tableau / POWER BI / Matplotlib, Seaborn.

Data Cleaning, Transformation and Manipulation: SQL / Excel

Statistical Analysis: SPSS, SAS, and STATA

Machine Learning and Predictive Analytics

Version Control and Documentation

Data Storage and Database Management

2. Analytical Skills:

Critical Thinking: The ability to think critically and approach problems analytically, breaking them down into manageable components.

Problem-Solving: Strong problem-solving skills to identify issues, formulate hypotheses, and devise effective solutions.

Data Interpretation: The skill to interpret data and draw meaningful insights, recognizing patterns and trends within datasets.

Quantitative Analysis: Proficiency in quantitative analysis, including quantitative reasoning and mathematical modeling.

3. Soft Skills:

Communication.

Attention to Detail

Time Management

Curiosity

Teamwork

4. Domain Knowledge:

Domain Expertise: Depending on the industry you work in, having domain knowledge (e.g., finance, healthcare, marketing) can be valuable for understanding context and making data-driven decisions.

5. Business Acumen:

Business Understanding: The ability to translate data insights into actionable business recommendations and align data analysis with organizational goals.
As the area of data analytics evolves, data analysts must often adapt to new tools and technologies in order to remain competitive and give relevant insights. As a result, adjust your skill development to the requirements of the specific data analyst profession you want to pursue. Having a solid foundation in these abilities and constantly strengthening them will help you flourish in the field of data analytics.

Resources

Online Courses and Learning Platforms:

Blogs and Websites:

Towards Data Science: A Medium publication with articles, tutorials, and insights on data science, data analysis, and machine learning.

FlowingData: Authored by statistician Nathan Yau, this blog covers data visualization and statistical analysis techniques.

YouTube Channels and Video Courses:

StatQuest with Josh Starmer: Offers clear and concise explanations of statistical concepts used in data analysis.

Data School: Provides video tutorials on Python, pandas, scikit-learn, and other data analysis tools.

AlexTheAnalyst: Provides video tutorials on the basic things you need as an analyst. I particularly recommend his free data analyst playlist bootcamp

Forums

Stack Overflow

Reddit's r/datascience

Certifications:

Google Data Analytics Professional Certificate

IBM Data Analyst Professtional Certificate

Remember that the best resources may differ depending on your individual data analytics aims and interests. To consistently expand your abilities as a data analyst, it's a good idea to mix formal courses with practical projects and continuing learning through books, blogs, and online groups.

Thank you

Thank you for taking the time to explore this article on Data Analytics. I hope you found it informative and insightful. If you have any questions, feedback, or would like to continue the conversation, I'd love to connect with you.

Feel free to reach out to me on LinkedIn, or Twitter if you'd like to stay updated on similar topics or if you have any professional inquiries. I'm always eager to engage with fellow enthusiasts in the field. Your thoughts and insights are valuable to me, and I look forward to connecting with you soon. Until then, happy reading and stay curious!

Understanding Smart contracts - Definition, pros, cons and application

Emmanuel C. Onyema — Wed, 23 Feb 2022 09:19:00 +0000

If you have been hearing about smart contracts and probably wondering what it really means, be assured that by the end of this post, you are going to understand in great detail what smart contracts are, its pros, some limitations, use cases and hopefully a little about the process of creation.

I would also like to thank Alejandro for the question asked on the Understanding the absolute basics about blockchain which this post is an answer to.

A little about Ethereum

So in the Ethereum blockchain, there are two different types of an account that can be created, first, an externally owned account long for (EOA) and a contract account.

EOAs are essentially those type of accounts you and I can create and control through metamask wallet which is external to the Ethereum platform or protocol, these accounts are created without cost and controlled by anyone with the private keys cryptographically signed.

Contract account are not controlled by users unlike EOA, they are instead controlled by a program code commonly known as smart contracts that is executed by the Ethereum Virtual Machine (EVM). its creation has a cost and they do not have private keys hence are said to ‘self-controlled’ based on the logic of its program code. its worthy to note that, contracts only run if they are called by a transaction, Contracts never run “on their own”.

Similarly, both types of accounts have Ethereum address and can send and receive ether, but how they process these transaction is what differs, when a transaction destination is a contract address, it causes that contract to run in the EVM, triggering the codes to perform actions that were defined in it, such as sending, receiving etc.

Okay, let’s get down to the sweet stuff -

what then is a smart contract?.

A little history - The term “smart contracts,” was coined by Nick Szabo in the 1990’s, he was trying to figure out a way to incorporate legal principles into the digital space, such that a breach of contract is prohibited or made very expensive. He also went on to define it as “a set of promises, specified in digital form, including protocols within which the parties perform on the other promises.”

Vitalik Buterin gave his own definition as: “a mechanism involving digital assets and two or more parties, where some or all of the parties put assets in and assets are automatically redistributed among those parties according to a formula based on certain data that is not known at the time the contract is initiated.

In a simple sense as regards the (ethereum) blockchain, **Smart contracts are simply self-executing immutable (once deployed, unchangeable) programs that is stored and runs on an Ethereum blockchain when predetermined conditions are met. The codes, conditions and functions it contains exist across a distributed, decentralized Ethereum blockchain network.

The basic idea of how a smart contract works is as follows

1.Logic is encoded into a smart contract program.
2.Nodes on the EVM compile, validate, store, and replicate the smart contract across the network 223
3.When the predetermined condition(s) occur, the contract is executed by the nodes on the network
4.Changes are made to the appropriate accounts on the network as a result of successfully executing the contract.

Attributes and Benefits of the smart contract.

These are some of the benefits and properties of smart contract.

Security – smart contracts are encrypted and have a high degree of security

Accuracy – contracts are validated by many nodes on the network Autonomous – there is no need for a third party to be involved in the execution of a contract, lessening the potential for bias or mistakes

Automation- smart contracts are a tool for automating business logic, and allowing seamless transaction without intervention.

Speed and efficiency – with the elimination of third parties, meaningless and time consuming manual processes of enforcing a condition, smart contracts ensures an efficient and fast execution.

Transparency – on a theblockchain, all parties have access to the shared ledger; transactions can be audited and tracked.

Permissionless - anyone anywhere in the world can write smart contracts and deploy it to the ethereum network, provided you have required eth to deploy and you (or you can meet someone else) can code the smart contract.

Drawbacks of Smart Contracts

Nevertheless, there are also drawbacks to be aware of. Since the codes are written by people, they can make mistakes and this makes the contracts vulnerable to attacks, and leading to detrimental asset loss. A good example of this is the DAO, where developers’ mistakes in the code were costly for the users and the company, Hackers exploited the errors and stole about $60 million.

Furthermore, a smart contract cannot exceed 24kb or else it will run out of gas.

Tools of creation

Having understood the basics of smart contracts, it would not come as a shock that a programming knowledge would be needed to create a smart contract, there are multiple high-level programming languages for programming smart contracts. Such llc, viper, serpent, bamboo, solidity. However, Some of these languages are deprecated and others are yet to gain a certain level of acceptance. Solidity is by far the most popular, and hence de facto high level language of Ethereum and programming smart contracts, it was created by Dr.Gavin Wood.

Solidity code needs a compiler to take its code and convert it into bytecode that is understandable by EVM.

In addition, other tools like remix ide and metamask are other important tool of development.

Resources to help you get started with solidity –
freecode camp solidity vide,
Stephen grider,
crypto zombies.

In conclusion, as blockchain continue to evolve, smart contracts have enormous potentials to further disrupt the health, finances and banking, real estate industries, the possibilities are endless.

Say hello - twitter linkedin

Bonding Curve Offering -An efficient and transparent funding model.

Emmanuel C. Onyema — Mon, 14 Feb 2022 03:01:30 +0000

In my previous post about the fundamental of blockchain, I referred to how amazing the blockchain is and how we keep on finding more and more effective unexplored ways of doing things.

In this post, we are going to learn about bonding curve offering (BCO), one of the relatively new innovative solutions in the decentralized world of finance (DEFI) utilized through decentralized exchanges (DEX) with respect to how startups raise capital. We are going to see some of its advantages and how it solves some of the pitfalls of previous models, especially the Initial coin offering(ICO) model.

Dexes actually have a lot of ways to help startup projects raise capital, but many projects aren't willing to explore or just aren't patient enough because of the get everything fast mentality. - Austin Ogiza.

Let's get started, to understand the (BCO) model, let's quickly review the Initial coin offering (ICO) model.

Similar to the Initial public offering (IPO) model used in the traditional business industry, ICO is a model used by startups to raise funds for financing cryptocurrency-related ventures. All interested investors buy into the Initial coin offering, and in turn, receive token equivalent which can sometimes be equated with the shares of a company or just represent a stake in the project.

Whilst this ICO model did bring some level of innovation and helped break barriers for some projects to take off, it did present loopholes to be exploited by ill-meaning individuals and some level of concern for the continuity of the projects.

Some of them being

1) Owing to the unregulated nature of this model, these projects aren't accountable to anyone hence the tendency to whatever they feel like doing even if it's against the project.
2) False sense of liquidity whilst in essence having none or very little to withstand a selling pressure thus adversely crashing down token prices.
3) huge tendency for a project to be a scam or not deliver up to its promises.
4) Flawed price discovery mechanism: The price of the first token is arbitrarily fixed by the people behind the project with 0% signal from the market usually causing instability once it interacts with the market.

Bonding curve offering to the rescue

At its core, the bonding curve is (often referred to as a mathematical formula defining) a set of instructions contained in a smart contract that enables users to mint/burn tokens and hence define the relationship between its price and supply. This whole mathematical relationship is based on the simple economic law of demand and supply.
As demand increases, more people buy(mint) the tokens which invariably increases token supply and in turn increases the value(price) of the asset and vice versa.

Core take away: price of a token is determined by its supply

Bringing this definition home to further explain how the IBCO Initial bond curve offering works towards start-up funding and correcting the loopholes created by the ICO model.

Here, interested individuals(liquidity providers) buy into a project/protocol by depositing a reserve asset (let us say Ether), and in exchange, the bonding curve would calculate under the current market condition the amount of native token to be minted and given to the user, strictly according to the mathematical formula contained in the bonding curve smart contract and vice versa.

The interesting part is, as this transaction gets executed, token supply is increased which influences token prices to also increase such that the subsequent buyer mints at a more higher
price than it was before, and vice versa. This process helps ensures token's price to be set deterministically by the market rather than arbitrarily by the project promoters.

Advantages of the bonding curve offering model

Solving liquidity issues

Liquidity is the measure of how easily you can convert an asset into cash or another asset, and it is essential for any tradable asset, now start-up projects can own their liquidity because of users ability to exchange their LP tokens (or reserve asset) for the project's native tokens at a discounted rate through the bonding process. As these LP tokens are locked in the bonding curve without being sold, there is the permanence of liquidity. Its also worthy to note that that is one of the reasons the newest cryptocurrency-related projects are flocking to Olympus dao which solves for liquidity problems by providing bonds-as-a-service for a small fee.

Reduces the need for a centralized entity and secondary market.

Ideally, before any seller could be able to sell, the seller would need to be matched with a buyer willing to buy and vice-versa, this process is traditionally supervised by a centralized entity using an order book. Automated market makers (AMM) differs, in that it is the protocol that powers all decentralized exchanges (DEXs), and an autonomous trading mechanism that eliminates the need for intermediation or centralized exchanges to match buyers and sellers. The smart contract through the bonding curve on the DEX such as uniswap mints tokens when buy orders are received and tokens are burnt when sell orders are received.

Encourages investors and project promoters long term commitment to the project:

Since It's dynamic, supports the project's organic growth, and takes the community and its ecosystem into consideration, the overall growth of the project also influences the token value of the project thus mitigating self-seeking actions of some investors and individuals behind the project and incentivizing them into creating effective progressive developmental strategies for common good. And when a project doesn't have any commitment and isn't accountable to its community it simply gets weeded out.

Efficient, Fair, and Transparent token price calculation.

The BCO models have helped mitigate the Arbitrary absurd initial prices fixed by individuals behind the project which usually results in sudden pumping and dumping of tokens crashing down its value.

Decentralized Autonomous Organization (DAO)

This organization are governed by rules not influenced by a central authority, through this means, projects are made accountable to their community.

Conclusion

Bonding curve offering ensures that worthy projects are funded and their tokens fairly, efficiently, and transparently distributed to project adopters, also with the ability of custom-tailored formula in the bonding curve smart contract the possibilities are endless.
Existing protocols can also leverage this model as it offers exciting opportunities for token sale that goes beyond funding and ICOS etc.
The goal of the post is to help us understand the basics of this technical term, however, it can turn very complex especially when multiple variables are introduced.
For more additional information about the curves, shapes and meaning, I have provided additional resources below.

https://phemex.com/academy/what-is-bonding-curve

https://medium.com/molecule-blog/token-bonding-curve-design-parameters-95d365cbec4f

On Blockchain — Understanding the absolute basics.

Emmanuel C. Onyema — Sun, 13 Feb 2022 02:26:58 +0000

In this post, we are going to learn about blockchain in its simplest form, my objective for this post is that by the end, you would have a better understanding of the blockchain, and also be able to explain to your friends or that 5-year-old-kid. I’m going to start with an introduction, then we define blockchain and clarify some of our misconceptions, after which we would talk about the types and quickly see some applications.
Alright, let’s dive right in.

Before we begin to learn what blockchain is, first, I would like to start with what the blockchain is not. Contrary to what some people think or assume, blockchain is not a cryptocurrency, it is not the bitcoin, it is not limited to financial transactions, the blockchain is much more than that, as it’s not for solving just one issue for some people, but can fix many problems for everyone.
It’s the foundation on which these other structures are built upon. It’s so much more that we keep on finding more and more ideas where this technology can help solve problems every day. Hopefully, by the end of this piece, you would get to understand this revolutionary technology.
For simplicity, I would limit my examples to the use of financial transactions.

So, What is blockchain?
First, what are blocks?
Let’s say I’m to record all my financial transactions and present them as a single big database encyclopedia, I would record on a single sheet of a paper list of transactions on grocery, including amounts, date, grocery store, etc. until it gets filled up.

I would run a validation exercise, cross-checking my bank statements, and asking 6 of my friends I went with to the grocery store that day to help me check if the values are accurate. On successful validation, I would save this sheet of paper inside the encyclopedia where I can’t remove it until the day, I’m going to submit it. Then I would get a new sheet of paper on which I would continue to record subsequent transactions.

The database encyclopedia can be likened to represent the blockchain, while the sheet(s) of paper the block(s).
However, in reality, the blockchain operates a little differently. It abides by certain ideologies which make it unique from our traditional database (record-keeping) system. This is then going to lead us into understanding some of the principles guiding the blockchain.

The blockchain core principles

Decentralization

This principle is all about delegating power, decision-making ability, supervision, maintenance, control from a central entity (a person, group, authority, or an administrator) to a dispersed network of nodes, all the nodes on the network contain the same information and have equal rights. It ensures no entity can claim ownership or control of the blockchain.

Decentralized database

A decentralized database is one where there is no centralized storage of data or system administrator hence no single source of failure or weakness in a system. And that is essentially what the blockchain is all about.

Distributed peer-to-peer network

P2p short for peer-to-peer is a model based on a simple decentralization principle, instead of relying on a centralized administrator or server to control communication functions, the blockchain rather utilizes a decentralized network communication model between two nodes (peers) which aids storing and sharing of records without the need for a central intermediary. Unlike traditional client-server systems where there is a dedicated central server where a client downloads from and a server to store, the p2p system is maintained by a network of nodes — where each node can act both as server and a client (server when another client is making a request and a client when it is making its request.

Security

All records are individually encrypted, The blockchain is built in such a way that together with all other underlying principles, security is prioritized such that taking part in the network is far more economically incentivized than attacking it. Also, because of its decentralized nature and no single central point of control, it’s nearly impossible for hackers to attack or attempt to hack into it, if anyone were to alter a single copy of the block, it would no longer contain the same copy of information distributed across other nodes hence would not be recorded into the block.

Immutability

Remember when I said the blockchain operates a little differently from how our database functions, here is another unique difference. There are what are known to be CRUD (Create, Read, Update, Delete) functions performed on persistent storage applications. Ability to create a record, read a record, update a record and finally delete a record. On the blockchain, the last two operations were omitted. You can only be able to create and read a record without updating or deleting it. This is how the blockchain got its immutability property, the ability of records to remain unchanged or almost impossible to reverse a recorded transaction.

Group consensus

Before data can be recorded on the blockchain in permanence, all the data that are being independently recorded by the participating nodes (computers) are periodically checked and if 51% (or more than) of those nodes agree upon the validity of a record, it gets committed to the blockchain, never to be altered again. This ensures that whatever data we read of the blockchain comes with the backup that was recorded by the majority of the participating nodes. So essentially, a consensus mechanism allows many participants to maintain one truth together which roughly translates to ‘’we are participants, therefore we can all check it, and whatever majority of us decides is the truth, because the majority of us cannot independently record false data.’’
Having established the basics and core principles of the blockchain, we can now agree on a higher level overview that the

Blockchain is a decentralized database distributed across participating peer-to-peer nodes which is immutable and very secure.

Now, we have understood what blockchain is, Let’s go back to clarify one of our earlier misconceptions about the relationship between blockchain and cryptocurrencies.

Blockchain and Cryptocurrencies

Most people got exposure to the blockchain through the knowledge of a cryptocurrency of which bitcoin is an example and the first to be created in 2008, so it’s not unusual for misconceptions to exist. But here is the real deal, cryptocurrencies are digital, decentralized, disintermediated, trustless currencies that get transacted through the blockchain. In essence, because of the infrastructures and principles by which the blockchain operates, cryptocurrencies can run on them to make it possible for secure peer-to-peer transactions to happen between people without any third party whilst ensuring privacy and accurate record of transactions.

To say that the blockchain is equal to bitcoin or any other cryptocurrency is like saying that a railroad is equal to a train, they are not, rather the train can transport people because it can travel on top of the railroad.

Types of blockchain

Oftentimes, when we hear of the blockchain, we generally think we have just one type of blockchain on which every other thing is built upon. Although all blockchains operate on certain common principles, They can also be built in a variety of different ways to best offer solutions to a specific use case or application. Fundamentally, we have just two types of blockchain, the public, and private blockchain but as we are going to see shortly, we have four of them in total.

Public blockchain

In a public blockchain, anyone anywhere in the world can become part of the blockchain network, read the data, send and receive data, and upon validation would be added to the ledger. They would be able to join the consensus process hence they are often referred to as permission-less because they are non-restrictive. By nature, this type of blockchain is open source and completely decentralized. Examples are the Bitcoin and Ethereum blockchain.

Private blockchain

They are blockchains controlled by a single entity, often referred to as a permissioned blockchain because the central admin decides and permits who becomes a part of the blockchain. As a result, there is a partial level of decentralization, security and they are more likely to encounter fraud or error whilst enjoying a faster and shorter time validating data as a result of few participating nodes, high level of privacy, and a higher level of scalability.
This type of blockchain is usually used within a single organization where only the selected members are allowed to be part of the blockchain.

Consortium blockchain

The consortium blockchain is a semi-decentralized type of blockchain unlike the private blockchain, more than one entity manages the blockchain, as a result, there is a higher level of decentralization, security than the private blockchain.
In this type of blockchain, they can consist of a group of large financial firms, here each (selected) participating firm would manage a node on the consortium blockchain.

Hybrid Blockchain

A hybrid type of blockchain is that type that can combine the best of both public and private types of blockchain. examples are the Dragonchain and Xinfins hybrid blockchain.
However, there are some advantages and disadvantages attached to each type of blockchain, so it’s always best when we define what we want, set priorities and then choose what type of blockchain to implement that would offer the best solution to our problems.

Applications of the blockchain

There is no use for medical knowledge if it can’t be applied to solving an abnormality in the body. You might have that same question for the use of blockchain, well we are going to quickly go through a few of them.
The use of blockchain can help us ensure

Transparent voting system.
Insurance through a smart contract,
Effective cloud storage
Record management,
Curbing money laundering
Financial management and accounting,
Securely share medical information,
Data protection, and storage
Effective Identity management,
Borderless transfer of money across countries easily
Proper Asset tracking across the supply chain. Thank you