Complete Guide to Big Data and Blockchain

Rajarshi Mitra

5 months ago
Complete Guide to Big Data and Blockchain


It feels like every single day we are stumbling across more and more use-cases for the blockchain technology. More and more industries are finding out that either the blockchain is going to take them to the next level, or it may end up becoming their biggest threat. One of the many fields that have discovered a symbiotic relationship with blockchain is big data. In this guide, we are going to explore this relationship. Before we go any further, let’s understand what blockchain and big data mean.

Complete Guide to Big Data and Blockchain

What is a Blockchain?

We have talked about Blockchain basics a lot of times on this site before. So, to give you a concise description, a blockchain is, in the simplest of terms, a time-stamped series of immutable record of data that is managed by a cluster of computers not owned by any single entity. Each of these blocks of data (i.e., block) are secured and bound to each other using cryptographic principles (i.e., chain).

The reason why the blockchain has gained so much admiration is that:

  • A single entity does not own the data stored inside the blockchain
  • The data is cryptographically stored inside
  • The blockchain is immutable, so no one can tamper with the data that is inside the blockchain
  • The blockchain is transparent so one can track the data if they want to

As you can see, it makes sense as to why companies are interested in incorporating the blockchain. In fact, Juniper Research asked employees of some big companies (with >20,000 employees) whether they are looking to incorporate the blockchain. This is what they found out in the survey:

Complete Guide to Big Data and Blockchain

57% said “Yes” while only 9% said “No”. In fact, 76% of the employees quizzed said that blockchain could be ‘very useful’ or ‘quite useful’ for their company.

As a result, many industries like finance, supply chain, healthcare have found immense use cases in the blockchain technology.

Alright, so now let’s look into big data.

What is Big Data?

According to Wikipedia, “Big data is a term used to refer to data sets that are too large or complex for traditional data-processing application software to adequately deal with. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.

So, how do you characterize big data? For that, you use something called the six Vs of big data:

  • Volume
  • Velocity
  • Variety
  • Veracity
  • Value
  • Variability

The Six Vs of Big Data

Volume: As the term implies, with big data you have to deal with a lot of data. Mostly, this data has high volume, low-density, and unstructured data. Most of the time, companies deal with terabytes and even petabytes of data, some of which could be of unknown value.

Velocity: Even though these companies deal with huge amounts of data, they need to act on it fast. Velocity is the rate at which the data is received and acted upon. Some industries need to work in real time or near-real time scenarios, which will require a high velocity.

Variety: In big data, there is a large variety of data available. This wasn’t much of a problem before with traditional data types. Traditional data types could be easily structured and fit in databases. However, big data is immensely unstructured or, at best, semi-structured. This is why, more often than not, big data requires a lot of additional pre-processing because of the sheer variety.

Veracity: According to the dictionary, veracity means the “ability to be true or honest”. Since big data models collect a high-volume of diverse, raw data from multiple sources, it can become extremely difficult to know how accurate the data really is. This is crucial because bad data can lead to incorrect business analytics and that can be, as you imagine, extremely problematic. For companies that need to deal with so much data, to get the accuracy desired, they need to trace the data to its source to correct all the issues.

Value: In the current age, data is money. The more data a company has, the more value it can generate. One thing to keep in mind, in order to generate that value, the data must be mined and processed. As we have mentioned before, not all data collected has any intrinsic value and inaccurate data may cloud the results provided by the analytics operation. To make the most out of the data, organizations must use data cleansing techniques.

Variability: The sixth V of big data in variability. Variability has multiple definitions in the context of big data. Firstly, variability refers to the number of inconsistencies found in the data. These inconsistencies can be discovered by various outlier detection methods. Lesser variability leads to more meaningful analytics. Another reason why the data set can have high variability is the sheer variance in data types and sources.

Use Cases of Big Data

Now that you know the 6 Vs of big data, let’s look at some of the use cases. If used properly, big data can help in multiple areas of business activities.

 

  • Enhanced Customer Experience: Customers are everything. If a company gains more customers then they will grow and if they lose out, then they are going to die, it is that simple. Big data will help you collect customer data from various sources like social media, web visits etc. to help polish your customer acquisition strategy.

 

  • Machine Learning Models: Along with blockchain technology, machine learning is the other hottest topic in the world right now. The reason being that it allows machines to create working models based on the data that it is fed. You can see why accurate big data can be useful in this context.
  • Product Development: Using big data, it is possible to garner exactly what the customer wants and predict their needs beforehand. The model is built by the classification of key attributes of past and current products.
  • Fraud Prediction: Successful companies are not just up against a few isolated hackers. There could be teams of experts who might be trying to take them down. Big data can help these companies identify patterns to help predict fraud.
  • Predictive Maintenance: By identifying certain indicators and patterns once can easily predict the occurrence of flaws before it happens. Big data analytics can help companies save millions of dollars by deploying cost-effective maintenance.
  • Improving Company Operations: One of the biggest use cases of big data lies in improving the operations of a company. Using big data one can analyze various parameters like customer feedback, returns, and various other factors to improve decision making and be more in tune with the current market demand.
  • Improve Innovation: Big data can help companies study the relationship between humans, institutions and various other entities to create insights. These insights can help companies innovate and create newer products or strategies to gain an edge over their competition.

 

 

The Benefits of Big Data

Now that we have gone through the use-cases of big data, let’s look into why we should go through the trouble of analyzing big data in the first place. Let’s look at the benefits of big data analytics.

  • Saves time
  • Cost efficient
  • Helps in product development
  • Helps in understanding market conditions
  • Helps in conducting sentiment analysis to understand the company’s online reputation

Biggest Challenges of Big Data

As you can imagine, big data implementation has multiple challenges.

  • Big data, as the name suggests, deals with a huge volume of data. Even with modern advancements, the fact of the matter is that the sheer amount of data floating around just keeps growing exponentially. As such, it becomes extremely difficult to keep store all the data in a secure manner
  • Because the volume of the data is so large, fraud detection in and data cleaning is an extremely labor-inducive task. Data scientists spend a huge chunk of their time just cleaning up data.
  • Keeping up with big data technology is an ongoing challenge since it is incredibly innovative.

Big Data and Blockchain: Quantity and Quality

The reason why big data and blockchain can have a very fruitful relationship is that the blockchain can easily cover the flaws of big data. There are three reasons why this partnership can be fruitful:

  • Security: Blockchain’s biggest asset is the security that it imparts to the data stored inside it. Remember, all the data that is inside the blockchain is non-tamperable
  • Transparency: The transparent architecture of the blockchain can help you trace data back to its point of origin.
  • Decentralization: All the data that is stored inside a blockchain is not owned by one single entity. So, there is no chance of data getting stolen if that entity gets compromised in any way.
  • Flexibility: The blockchain can store all kinds and types of data.

If you consider all these factors, the conclusion that we can draw is that whatever data comes out of the blockchain is valuable It has already been cleaned and it is fraud-proof. That is a potential goldmine that many companies are looking to exploit.

So, this brings us to the next question.

What exactly are the properties of blockchain technology which enables this relationship?

  • Decentralization
  • Transparency
  • Immutability

#1 Decentralization

Before Bitcoin and BitTorrent came along, we were more used to centralized services. The idea is very simple. You have a centralized entity which stored all the data and you’d have to interact solely with this entity to get whatever information you required.

Another example of a centralized system is banks. They store all your money, and the only way that you can pay someone is by going through the bank.

The traditional client-server model is a perfect example of this:

Complete Guide to Big Data and Blockchain

When you google search for something, you send a query to the server who then gets back at you with the relevant information. That is simple client-server.

Now, centralized systems have treated us well for many years, however, they have several vulnerabilities.

  • Firstly, because they are centralized, all the data is stored in one spot. This makes them easy target spots for potential hackers.
  • If the centralized system were to go through a software upgrade, it would halt the entire system
  • What if the centralized entity somehow shut down for whatever reason? That way nobody will be able to access the information that it possesses
  • Worst case scenario, what if this entity gets corrupted and malicious? If that happens then all the data that is inside the blockchain will be compromised.

So, what happens if we just take this centralized entity away?

In a decentralized system, the information is not stored by one single entity. In fact, everyone in the network owns the information.

In a decentralized network, if you wanted to interact with your friend then you can do so directly without going through a third party. That was the main ideology behind Bitcoins. You and only you alone are in charge of your money. You can send your money to anyone you want without having to go through a bank.

Complete Guide to Big Data and Blockchain

#2 Transparency

One of the most interesting and misunderstood concepts in blockchain technology is “transparency.” Some people say that blockchain gives you privacy while some say that it is transparent. Why do you think that happens?

Well… a person’s identity is hidden via complex cryptography and represented only by their public address. So, if you were to look up a person’s transaction history, you will not see “Bob sent 1 BTC” instead you will see :

“1MF1bhsFLkBzzz9vpFYEmvwT2TbyCt7NZJ sent 1 BTC”.

The following snapshot of Ethereum transactions will show you what we mean:

Complete Guide to Big Data and Blockchain

So, while the person’s real identity is secure, you will still see all the transactions that were done by their public address. This level of transparency has never existed before within a financial system. It adds that extra, and much needed, level of accountability which is required by some of these biggest institutions.

Speaking purely from the point of view of cryptocurrency, if you know the public address of one of these big companies, you can simply pop it in an explorer and look at all the transactions that they have engaged in. This forces them to be honest, something that they have never had to deal with before.

However, that’s not the best use-case. We are pretty sure that most of these companies won’t transact using cryptocurrencies, and even if they do, they won’t do ALL their transactions using cryptocurrencies. However, what if the blockchain technology was integrated…say in their supply chain?

You can see why something like this can be very helpful for the finance industry right?

#3 Immutability

Immutability, in the context of the blockchain, means that once something has been entered into the blockchain, it cannot be tampered with.

Can you imagine how valuable this will be for financial institutes?

Imagine how many embezzlement cases can be nipped in the bud if people know that they can’t “work the books” and fiddle around with company accounts.

The reason why the blockchain gets this property is because of cryptographic hash functions.

In simple terms, hashing means taking an input string of any length and giving out an output of a fixed length. In the context of cryptocurrencies like bitcoin, the transactions are taken as an input and run through a hashing algorithm (bitcoin uses SHA-256) which gives an output of a fixed length.

Let’s see how the hashing process works. We are going to put in certain inputs. For this exercise, we are going to use the SHA-256 (Secure Hashing Algorithm 256).

Complete Guide to Big Data and Blockchain

As you can see, in the case of SHA-256, no matter how big or small your input is, the output will always have a fixed 256-bits length. This becomes critical when you are dealing with a huge amount of data and transactions. So basically, instead of remembering the input data which could be huge, you can just remember the hash and keep track.

A cryptographic hash function is a special class of hash functions which has various properties making it ideal for cryptography. There are certain properties that a cryptographic hash function needs to have in order to be considered secure. You can read about those in detail in our guide on hashing.

There is just one property that we want you to focus on today. It is called the “Avalanche Effect.”

What does that mean?

Even if you make a small change in your input, the changes that will be reflected in the hash will be huge. Let’s test it out using SHA-256:Complete Guide to Big Data and Blockchain

Do you see that?

Even though you just changed the case of the first alphabet of the input, look at how much that has affected the output hash. Now, let’s go back to our previous point when we were looking at blockchain architecture. What we said was:

The blockchain is a linked list which contains data and a hash pointer which points to its previous block, hence creating the chain. What is a hash pointer? A hash pointer is similar to a pointer, but instead of just containing the address of the previous block it also contains the hash of the data inside the previous block.

This one small tweak is what makes blockchains so amazingly reliable and trailblazing. In fact, this is why data extracted from the blockchain is 100% reliable. You know for sure that no one has tampered with the data in the first place.

Examples of Big Data and Blockchain Projects

Let’s look at two projects which are combining big data and blockchain.

Storj

Complete Guide to Big Data and Blockchain

Storj is an open source, decentralized file storage solution. They use cryptography, sharding, and hash tables to help store files on a decentralized peer-to-peer network. Storj has a distributed set of storage nodes which utilizes the spare hard drive space from its community members, who are called “farmers”.

Storj uses their native token STORJ to fuel their internal system. The idea is for users to pay these farmers with the token in order to utilize their storage and bandwidth. There will be an upper limit of 500 million STORJ tokens and they will use the proof-of-work consensus mechanism.

Omnilytics

Complete Guide to Big Data and Blockchain

Omnilytics will combine blockchain with big data analytics. They use artificial intelligence and machine learning in conjunction with marketing, auditing, and trend forecasting.

The Omnilytics Platform Coordinator processes the requests made by the users for data and forwards the data acquiring task to the Data Acquisition Nodes. The Data Validation Nodes will then validate the acquired data which is then normalized by the Data Sharper Nodes. At the end of this process, the system sends the data to the concerned user. The entire system is fueled by the OMN tokens.

Conclusion

Big data and blockchain technology can join forces to truly revolutionize the way we process and analyze data. In this day and age, data is money. In order to come out on top of this race for acquiring more high-quality data, we will probably see more and more companies trying delve into this powerful partnership.

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