Big Data Challenges : How Blockchain Could Overcome Them?

How Blockchain Can Help In Big Data Challenges

Blockchain  - A rescuer of big data risks and challenges, we have listed around 7 big data challenges above, but the most important challenges are Security, Handling the Quantity and Identifying the Malicious Data. If we can sort out these 3 challenges, then we can easily overcome the other challenges. Blockchain is found to be the remedy for fulflling big data with security, quantity, and dirty data identification.

Blockchain can create fruitful relationship with big data in the following ways

Security - This is the top most asset of blockchain ever. Data’s inside the blockchain are non tamperable

Transparency In Tracking - The another biggest challenge of  data analytics is unable to find the origin of a data. The increased transparency nature of blockchain can easily find from where a data started to flow.

Data’s are Stored in Decentralized Environment - No chance for hacking, as the data’s are stored in a distributed decentralized server. So there is no chance to hack data, without getting validated by each node.

Flexibility In Handling Various Data - Blockchain Doesn’t restrict data in terms of it type, it can store and hold any kind of data.

So by combining all these primary nature of blockchain it could create a fruitful relationship with big data. 

Refer : blockgeeks

Blockchain Use Cases In Big Data

Blockchain is adding kind of characteristic for data while proceeding data analytics process. It results in.. 

• Blockchain generated big data,  which is secured, and can’t be hacked as it is tightened with network architecture. ​
   
• Blockchain based big data, is become more valuable as it can easily differentiate structured and unstructured data.

Refer: towardsdatascience

1. Blockchain ensures Data integrity and hence trust
   
   As blockchain goes through a detailed verification process in order to store a data, it ensures the data quality and integrity .
    
2. Ensures data security
   
   
   It is impossible to possess a data hack in side the blockchain networks, as the entire networks verifies the data with a certain consensus algorithm.
   
   
   
   source : hackernoon
3. Increases Computational power in predictive analytics 
   
   As blockchain integrates structured data in one place, it becomes easy to compute the data and bring better insights.
4. Real Time Data Analysis Become possible
   
   As blockchain provides fast and real time processing access to the data, enterprises adopts blockchain to  proceed real time cross border transactions.
    
5. Provides access to data sharing
   
   As data’s stored in blockchain is accessible for everyone, it prevents data processing teams to proceed data analysis on already analyzed data and reusing them.
   
    

Blockchain in Big Data : Future Statistics

​According to infoworld, The worth of blockchain ledger could be nearly 20% of toal big data market in 2030, and will cultivate a massive $100 billion as annual revenue.

Refer : infoworld

Top 15 Big Data Tools 2019

There are plenty of big data tools are available in big data industry, but it is very important to choose a tool that would require little setup cost and would bring clear cut insights.  For those Businesses and developers who are looking for emerging big data tools, this blog will explore the best data analytics tools of 2019 here below. Check out the list, and choose the best tool that matches your business.

The following contents are originally published from Top 20 Big Data Tools 2019

Top Big Data Tools

1. Apache Hadoop
2. Apache Spark
3. Apache Storm
4. Tableau
5. Apache Cassandra
6. Flink
7. Cloudera
8. HPCC
9. Qubole
10. Statwing
11. CouchDB
12. Pentaho
13. OpenRefine
14. RapidMiner
15. Data Cleaner
16. Kaggle
17. Hive
18. Kafka
19. Graph Databases
20. Elastic Search

Read more about the Tools reviews , Features and their working strategy here below.

Top 20 Big Data Tools 2019

Top 7 Big Data Use Cases in E- Commerce

Big data Use cases in E-Commerce Industry

#1. The use of Predictive Analytics

This analytics can be used to analyze what will be the trend and what will create a buzz in social media by predicting the future with forecasting algorithms. Also, predictive analytics is used to determine what a  customer can buy in future this can be known as sentimental analysis, which will analyze what a customer discussing e-commerce products on social media.

#2. Optimizing the Product Price  

Using real-time analytics in big data can help retailers to enable the best price for goods by tracking through the transactional records, competitors, and other things. So this is why the pricing of a particular product in an e-commerce store often varies. 

#3.Helps to Forecast the Demand

For websites like Amazon, it is very important to execute accurate forecasting on demand, because it is very complex to manage their inventories on shelves. Amazon uses some real time forecasting tools to track the historical data and those tools will have the provision of assessment in demand fluctuations. 

#4.To bring Better Customer Experience 

I. 89% of customers are refused to buy a product in an e-commerce portal, after experiencing poor service with them. 

II. 83 % of customers often require immediate customer support while purchasing a product online. 

Big data lets the business to create a 360-degree view on users in order to bring stressless and optimized customer experience by compiling the previous online/offline transactions, social media discussions, product reviews and etc.

#5.To create Personalized Stores : 

Big data can help e-commerce businesses to create dynamic websites that are filled with the relevant products by tracking the previous purchase history and browsing details of a customer.

#6.To Increase Conversion Rates and sales 

Big data can be used to personalize the purchase by avoiding the cart abandonment. A statistics report that a  huge volume of customers has been failed to make purchases at the last minute, even the product has been listed into the cart.  Businesses and retailers can use big data to offer personalized customer experience in order to prevent such cart abandonment.

#7.Can increase the decision making on micro-moments

Micro-moments are the trend and hot topic on the e-commerce industry last year, and this will be the trend of 2019 and 2020 too. Every customer looks for an immediate solution while purchasing. Around 70% of sales are made through smartphones. So retailers focused to improve the micro-moment decisions by connecting the smartphone technologies with big data analytics. Refer : medium.com

Conclusion

It is clearly shown that big data is directly impacting the increase in sales conversion, increase in customer experience, increase in revenue through advertisements, and finally increase in ROI. As listed above there is ‘N’ number of use cases can be derived while using big data in e-commerce. We have mentioned the top use cases of big data in e-commerce. 

Read more at : how big data can be used in e-commerce industry

How Big Data is Being Used In Facebook?

Nowadays, it is impossible to see a person without not connected with an social media. Because the world is getting exponential growth digitally around every corner of the world. According to a report,  2.77 billion peoples are using social media in 2019. In 2021 the count will be nearly 3.02 billion. 

Sure the world will achieve this count before 2021, because we all are using social media, without concentrating our daily work, like eating, sleeping or whatever. also the number of mobile users also has been increased when compared to the previous years.

This drastic growth of social media is directly impacting the data generation. Yes, Whatever we do in social media including a like, share, retweet, comments and everything has been stored as a record, and which has been generated data. 

So what kind of strategy that businesses like Facebook have decided to handle all this data? 

To handle all this data, Organizations like Facebook have adopted BIG DATA technology. Here we gonna discuss how Facebook is using big data analytics? Why they are using big data analytics. Let’s discuss more.

How Big Data is Used in Facebook?

The main business strategy of Facebook is to understand who their users are, by understanding their user's behaviors, interests, and their geographic locations, facebook shows customized ads on their user's timeline. How it is possible?

There are around billion levels of unstructured data has been generated every day, which contains images, text, video, and everything. With the help of Deep Learning Methodology ( AI), Facebook brings structure for unstructured data. 

A deep learning analysis tool can learn to recognize the images which contain pizza, without actually telling how a pizza would look like?.  This can be done by analyzing the context of the large images that contain pizza. By recognizing the similar images the deep learning tool will segregate the images that contain pizza. This is how data Facebook is bringing a structure to the unstructured data. 

Read more at How facebook using big data?

Business Intelligence Trends By 2019

According to the CIO review there are a lot of business intelligence that gonna bloom before the end of this year 2019. This intellectual report from CIO says, Business intelligence will be more compromising with various vertical. Let us see what are them below

Analytics Adoption — By using analytics as an tool stakeholder can get clear insights and data’s related to their function.

Cloud Based Analytics — As The BI Processes are migrating in to cloud based analytics, sharing of data have streamlined. This become a life holder for the various digital transformations trends, like IOT integration as it prevents the investment on setting up the infrastructure for storing and accessing the data’s.

Data privacy — CIO has denoted that the Code of ethics is ensure the following things,

i. Secure data sharing

ii. Avoiding the misuse of data applications

iii. over exhausted conclusion of that have drawn from the derived insights.

4. Explainable Artificial Intelligence (AI)

This explainable AI seems to be more useful for and will encourage users to get deep understanding on the obtained results by increasing the transparency.

5. Integration Of Natural Language

The processing of Natural language can help out both novice and expert users to understand the new data and focus on the obtained information along with visualizations or other data analytics outputs without actually needing a expert guidance.

Also CIO have claimed that Actionable analytics , Data collaboration for social welfare, convergence of Business Intelligence & Data management.

Look out the originally published detailed article here, for more reference. 

Ref : Business Intelligence Trends 2019

The Latest Business Intelligence Trends Published By CIOReview

Data Analytics Vs Data Science : Understand The Difference

If you are a learner to the big data industry, then i am damn sure you will be get confused with these two terms. That is data science and data analytics. These two terms seems to be pretty similar to each other, but as your secondary mind thinks, this two topics are having huge variations between them. So this article is for the novice people who are getting struggled to understand the difference between data science and data analytics..

The Similarity Between Data Science and Data Analytics

Before understanding the difference we have to fully accept the similarity between these two fields. Yes, these two fields are totally focused on analyzing the data. This is the only and primary similarity to both data science, and data analytics(business analytics). But, inside the data engineering cycle, this two process starts to vary between each of us, as they are occur in two different phases.

Data Analytics :

It is a systematic process, to extract valuable information’s various structured and unstructured data source. Getting the past, present and future business performance through collected business information’s. Determining and explaining the best statistical &data driven business model to the concern business owner

In simple terms :

Collecting, extracting, visualizing the business insights from various structured and unstructured business data, and helping business owners to take timely, data driven, and logical business decisions.

Data science :

Designing, developing, and deploying logical, automated, machine learning algorithms that should support any business intelligence tools inorder to analyze a huge volume of data. it is the foundation for data analysis, through which an applied business problem can be solved.

Image source : The Ultimate Comparison: Data Science vs Analytics

Originally published on : What is the difference between Data Science and Data Analytics?

Some Interesting Predictions On Big Data

Big data is not just a term, it has been tied up with a lot of emerging technologies like artificial intelligence, Machine learning, Blockchain, augmented reality, IOT and a lot more. The reason why I have listed above technologies is, many reports predict that these are the technologies that are gonna be viral and are going to make a revolutionary growth in 2020 and after that too. For all these technologies, Big data is gonna be the key source or even we can say it as a “life of emerging technologies”. 

We are living in a digital era, and in every corner of the world, someone keeps finding a new technology or something to renovate this world. And, as a result, many technologies or many trends are introduced every day. Some sticks on top with large popularity and acceptance, but some technologies are stayed viral for a particular period, after that it gets disappeared. But, Big data is not one of those disappearing technologies.

Many industry experts predict that big data is having a brightful future and are not gonna dim at any situation. 

Why Big Data Would Become Viral in 2020

There are “N” number of reasons to figure out that the big data is having huge potential in the future, but we just want to list out some bit from it. 

• As we have described above,  2020 is gonna be the most promising year for a lot of technologies. Most of the technologies are comes with the nature of artificial intelligence, so to automate everything there must be a big need for data processing methods.
   
• By 2025, there would be a big and tremendous volume of data collections, and that would be nearly 163 zettabytes ( Nearly 163 trillion GB)
   
• A number of online startups, online transactions, Digital works & everything regarding digital will attain exponential growth. So, all these things will retain a huge amount of big data.
   
• Mobile applications will return a huge amount of data.
   
• Social media will generate 10x bigger datasets than now. 

So, as we are entering into the digital universe gradually, everything will be stored as digital data. Hence handling this kind of data will become more important and necessary too. 

Big Data Predictions 2020

Here are some of the most notable expert predictions about big data.

Harry Dewhirst - The President of Blis, stated that “ Data has a staying, power, and it is not going to stay away soon.” Also, he points that “Data scientist job is the sexiest job of this 21st century” by denoting the Harvard Business Review

Sam Underwood - Vice President at Futurety, says that by 2021 that big data will become more accessible and will be more useful enterprises to boost success.

Jeff Houpt - President at DocInfusion, says that the landscape of big data that is the usage will become easier and simpler. He is sharing that the big data is evolving from highly technical and expensive to more self-service. So we would be charged for what we use. 

KG Charles Harris - CEO Of Quarrio predicts that by 2021, Data retrieval from big data repositories can be done using natural language. 

Ben Bromhead - Co-Founder of Instaclustr predicts that, In next three years, most of the Databases as a service (DBaaS) providers will embrace the big data analytics as they are in the need to serve the exponential growth of clients.

Likewise, we can list out the “N” number of positive predictions about big data in 2020. 

Reference : Big Data Predictions

Technologies that Gonna Prevail With Data Analytics

• Artificial intelligence & Big Data
• IOT
• Machine Learning
• Blockchain
• Augmented Reality & Virtual Reality

We will discuss in detail about, how big data is gonna renovate all these technologies in upcoming articles. 

Orginally Published On : The Future Of Big Data