Blockchain technology is a decentralized digital ledger that records transactions across many computers so that the registered transactions cannot be altered retroactively. This technology is the backbone of cryptocurrencies, but its applications extend far beyond just serving as the infrastructure for digital currencies.
Content:
Key concepts of blockchain
Applications beyond cryptocurrency
How does blockchain impact the IT sphere?
What are the possible future and challenges of blockchain technology?
Summing up
Below is a breakdown of the basic concepts, applications beyond cryptocurrency, and its impact on the IT industry.
Key concepts of blockchain
Decentralization. Unlike traditional centralized systems, blockchain operates on a distributed network of computers (nodes), eliminating a single point of control and failure.
Transparency. All transactions on the blockchain are visible to participants, ensuring transparency while maintaining privacy through cryptographic techniques.
Immutability. Once a transaction is recorded on the blockchain, it cannot be altered or deleted, guaranteeing the integrity of the transaction history.
Consensus mechanisms. Blockchain employs various consensus methods (e.g., Proof of Work, Proof of Stake) to validate transactions, ensuring all participants agree on the ledger’s state without needing a trusted third party.
Applications beyond cryptocurrency
Blockchain’s potential extends far beyond cryptocurrencies like Bitcoin and Ethereum. Where else can it become a game changer?
In supply chain management. Blockchain improves transparency and traceability in supply chains, enabling more efficient tracking of goods and authentication of product authenticity.
In smart contracts. Self-executing contracts with the terms directly written into code, automating and enforcing agreements without intermediaries, applicable in finance, real estate, and legal processes.
In healthcare. Secure and immutable records can enhance patient data management
Data management is essential for businesses because it keeps data accurate, easy to access, and secure. Good data management helps businesses make better decisions based on reliable information. It also makes operations faster by reducing errors and duplicate work.
With well-organized data, businesses can understand customer needs and improve their products and services. Proper data management also helps companies meet legal rules and avoid fines.
What is data management?
In 2025, data management is a complex process. It includes collecting, storing, organizing, and using data. The process should ensure that data is accurate, easy to access, and properly protected. Data helps companies make decisions as well as meet legal and security requirements.
History of data management in business
The way businesses see data management has changed a lot over time.
It all started with the record-keeping of proto-civilizations, but let’s get to nearer eras. Data management as we know it today started in the 1960s and 1970s. At that time, companies relied on early computers and punch cards to handle basic tasks like tracking inventory or finances. However, in the 70s, relational databases were introduced, and they instantly made storing and retrieving data easier, setting the stage for modern systems.
In the 1980s, personal computers became common in offices, and companies started using data as a resource – they built systems to store and analyze data from different departments.
The 1990s saw a big shift as businesses started to collect large amounts of data from websites. At that time, ERP systems brought all business data together in one place, so it became easier to manage.
In the 2000s, the growth of the internet, social media, and mobile phones created huge amounts of data. Companies started to see this "big data" as a valuable asset. New tools like Hadoop allowed businesses to handle very large datasets for the first time.
By the 2010s, cloud computing made data storage easier and cheaper. We started to use advanced tools to analyze data and predict customer behavior. Around this time, governments introduced strict rules about how data could be used, which forced companies to focus more on security and governance.
Today, data is a core part of every business. We rely on real-time data to manage operations, understand customers, and prevent fraud. Over the years, data management has grown from a simple operational tool to a critical part of business success.
Key components of data management
When we talk about data, we use some notions and it’s better to get the same vision of the key terms. Here are seven components of data management:
I. Data collection is about gathering information from different sources, like customer surveys, website visits, sales transactions, or sensors in machines. For example, an online store collects data about what customers buy, how often they visit, and which products they look at.
II. Data storage. It means keeping information safe and organized. The storage can happen on physical devices like hard drives or in digital spaces like cloud platforms. A company can store customer details, sales records, and employee data in a secure database. Without proper storage, important information could get lost, damaged, or is hard to find.
III. Data organization means arranging data so it is easy to use. For instance, sort customer information into categories like name, email, and purchase history in a spreadsheet.
IV. Data security is about protecting data from theft, loss, or unauthorized access. To guarantee protection, companies use passwords, encryption, and firewalls. For example, a bank limits access to sensitive customer information to authorized employees only.
V. Data governance is about the accuracy and security of data. It involves rules for how data is collected, stored, shared, and used. Good data governance ensures data is trustworthy, protects against security risks, and keeps the organization compliant with regulations.
VI. Data quality means accuracy, completeness, and reliability of data. It focuses on fixing errors, filling in missing information, and making sure that data is consistent across different systems. Poor data quality can lead to mistakes, like wrong shipments or bad reporting, which can hurt the business. In short, data quality ensures that the information business acquires is correct and useful.
VII. Data integration combines data from various databases, applications, sales platforms, customer service systems, and marketing tools into one system. Without integration, the data would stay separate and hard to analyze or use.
Benefits and challenges of data management
Data management can help you in many ways. Its key benefit is that it gives you reliable information you can base your decisions on. The result? Your overall business efficiency is improved, and customer satisfaction is supported. Another key benefit is meeting legal rules, which avoids fines and protects the company’s reputation. Businesses that use data well often gain an advantage over competitors by spotting trends faster.
But not everything is bright.
Analyzing and managing large amounts of data is not easy at all, especially as businesses grow.
Keeping data secure from hackers or accidental loss is a constant concern.
Combining data from different systems or formats is often complex and requires advanced tools.
Ensuring data stays accurate and up to date can take time and effort.
Finally, complying with strict data laws and regulations requires careful planning and resources.
Despite these challenges, effective data management is essential. You can achieve it with the right team of data management specialists.
Who provides data management for business?
Data management consists of multiple components, and the further we go into the future, the more complex this process becomes. This means, in particular, that your company needs more and more people to deal with data. As of 2025, there are several established roles focusing on different aspects of data management. Here are just some of them.
Database Administrator
They set up and maintain databases. DBAs back up data, fix performance issues, and make sure databases are secure and accessible. These specialists focus on the technical side of storing and retrieving data and work mainly with database systems rather than broader data processes.
Data Analyst
They analyze data to find patterns, trends, and insights. Data analysts work with data to extract meaning, while roles like DBAs focus more on managing and storing the data itself.
Data Engineer
This role appeared in early 2000s, with the rise of big data. Data engineers build systems that collect, store, and process large amounts of data. They design data pipelines to move data from one system to another and prepare it for analysis. Data engineers create the infrastructure for data use, whereas analysts and scientists work with data after it is prepared.
Data Scientist
These people try making predictions and solving complex problems. They do so with statistical modeling and machine learning. Data scientists often work with unstructured data, like text or images, and focus on advanced analysis and predictions. Scientists create algorithms, while analysts often stick to descriptive or diagnostic analysis.
Data Governance Specialist
The role appeared in the early 2010s, alongside the rise of data regulations like GDPR. They ensure that data use complies with laws and company policies. They create rules for handling data and monitor compliance to protect data privacy and security.
What are the key differences between the roles?
Focus. Some roles, like DBAs and data engineers, focus on infrastructure and systems. Others, like analysts and scientists, work directly with data to extract insights.
Skills. Engineers and scientists need coding and technical skills, while analysts may focus more on interpreting data and creating visuals.
Scope. Leadership roles oversee the entire data strategy, while technical roles like DBAs or engineers work on specific tasks.
What tools and technologies are used for data management?
Here are some of the most popular and widely used data management tools and technologies:
Microsoft SQL Server. A highly popular RDBMS. Supports both transactional and analytical workloads.
Oracle Database. Known for handling large-scale operations.
MySQL. An open-source RDBMS commonly used for web applications.
PostgreSQL. Known for its scalability and strong compliance with SQL standards.
Apache Hadoop. Essential for big data management and used by companies that handle large datasets.
Tableau. A popular data visualization tool.
Apache Spark. Often used with Hadoop for real-time data processing.
AWS. Offers cloud storage (S3), databases (RDS, Redshift), and big data solutions (EMR, Athena).
Google Cloud Platform. Offers tools like BigQuery and Cloud Storage.
Snowflake. Popular for data warehousing and analytics.
Datadog. A monitoring and analytics platform widely used by IT teams.
How much do data management specialists make?
Below are the average annual salaries for mid-level data analysts and engineers in different countries. They are very approximate and can vary on many factors. Hopefully, these numbers will provide a broad picture of direct labor costs for data management specialists worldwide.
Data Analyst
United States: $110,000
United Kingdom: $85,000
Israel: $50,000
Brazil: $25,000
Ukraine: $15,000
India: $12,000
Vietnam: $10,000
Data Engineer
United States: $125,000
United Kingdom: $90,000
Israel: $55,000
Brazil: $25,000
Ukraine: $20,000
India: $14,000
Vietnam: $12,000
The salaries of data management specialists are quite high in developed countries like the USA, Israel, and the United Kingdom. However, in regions with a lower cost of living, you can find specialists as qualified as in the US but at a fraction of the price. Hire data management specialists abroad with staff augmentation services and cut costs on your data management.
Book a call with MWDN to find out more about staff augmentation and the services we provide. , ensuring privacy and enabling more efficient and accurate treatment and research.
In identity verification. Blockchain can offer a secure and unforgeable means of managing digital identities, applicable in voting systems, online authentication, and more.
In decentralized finance. Beyond traditional cryptocurrencies, blockchain supports the development of DeFi platforms, offering financial services without central financial intermediaries.
How does blockchain impact the IT sphere?
Blockchain introduces a new data management, security, and collaboration paradigm, massively affecting the whole IT sphere. Here are the trends we see in 2024:
1. Enhanced security and trust
Blockchain enhances data security and integrity through its decentralized nature and cryptographic hash functions. It provides a tamper-proof system where modifications to the data are virtually impossible without consensus, significantly reducing the risk of fraud and cyber-attacks. This has led IT sectors to adopt blockchain for secure transactions, data management, and identity verification, fostering trust in digital interactions.
2. Shift in skill sets and job opportunities
The rise of blockchain technology has created a demand for professionals with specialized skills in blockchain development, smart contract programming, and blockchain system design. This demand extends beyond technical roles to include legal, regulatory, and business strategy positions focused on blockchain applications. IT professionals are now seeking education and certification in blockchain technology to meet the growing need for expertise in this field.
3. Decentralization of applications and services
Blockchain enables the development of decentralized applications that operate on a peer-to-peer network rather than being controlled by a single entity. This shift challenges traditional centralized IT architectures and business models, prompting companies to explore decentralized solutions for enhanced transparency, security, and efficiency.
4. Innovation in infrastructure
The deployment and management of blockchain applications require new types of IT infrastructure, including distributed computing resources, specialized storage solutions, and enhanced network capabilities. This has led to innovation in cloud services, edge computing, and other IT infrastructure technologies to support the scalability, performance, and security needs of blockchain systems.
5. Regulatory and compliance challenges
As blockchain technology becomes more prevalent, IT departments must navigate an evolving regulatory landscape. Compliance with data protection regulations, understanding the legal implications of smart contracts, and managing cross-border data flows in a decentralized network are complex challenges that IT professionals must address.
6. Emergence of new business models
Blockchain technology supports new business models and revenue streams, such as tokenization, DeFi, and blockchain-as-a-service offerings. IT companies are exploring these models to provide innovative services to their customers, requiring shifts in business strategy, service delivery, and customer support.
7. Data management and interoperability
Blockchain offers new ways to manage and share data across organizations and systems securely. This potential for enhanced interoperability and data exchange is driving IT initiatives to leverage blockchain for supply chain management, healthcare records, and cross-industry data platforms.
What are the possible future and challenges of blockchain technology?
The future of blockchain technology is promising, yet it faces challenges that need to be addressed. Here’s a look at the prospective future developments and the hurdles blockchain technology faces.
Possible future of blockchain technology
Widespread adoption. Beyond finance and cryptocurrencies, blockchain is poised to revolutionize supply chain management, healthcare, real estate, and even government operations by providing transparent, secure, and efficient ways to record transactions and manage data.
Integration with other technologies. Blockchain is expected to increasingly integrate with other emerging technologies, such as IoT and AI, creating more secure and efficient systems for data exchange and automation.
Advancement in DeFi and DAOs. The finance sector may see a shift towards more decentralized platforms, reducing reliance on traditional financial institutions and promoting financial inclusion. DAOs could redefine organizational structures, with blockchain enabling truly decentralized and democratic decision-making processes.
Enhanced privacy and security features. Ongoing developments in blockchain technology will likely produce more sophisticated privacy-preserving technologies, enabling transactions and data management with enhanced security and anonymity.
Regulatory evolution and standardization. As blockchain becomes more mainstream, regulatory frameworks worldwide will evolve to better accommodate and facilitate its growth, including standards for interoperability, security, and privacy.
Challenges facing blockchain technology
Scalability issues. One of the major challenges blockchain faces is scalability. Many blockchain networks struggle to process transactions at scale, which is crucial for widespread adoption.
Energy consumption. Particularly for blockchains that use PoW consensus mechanisms, the energy consumption is significant, raising environmental concerns. There is a growing push towards more energy-efficient consensus mechanisms like PoS.
Regulatory and legal hurdles. The decentralized nature of blockchain poses regulatory challenges, including issues related to compliance with existing financial regulations, data privacy laws, and cross-border transactions.
Interoperability. As more blockchain networks emerge, the need for interoperability between different blockchains becomes critical to enable seamless exchange of information and value.
Public perception and understanding. Misunderstandings and the complex nature of blockchain technology can hinder its adoption. Clearer communication and educational efforts are needed to improve public perception and understanding.
Summing up
While blockchain technology holds transformative potential for numerous sectors, realizing this potential depends on overcoming technical, regulatory, and societal challenges. The future will likely see a combination of technological advancements, regulatory adjustments, and broader cultural shifts as blockchain technology matures and becomes more integrated into everyday business and society.
