What Is The Most Important Question For Data Science (And Digital Transformation)?

With so many buzzwords surrounding AI and machine learning, understanding which can bring business value and which are best left in the lab to mature is difficult. While machine learning offers significant power in driving digital transformations, a business must start with the right questions and leave the math to the development teams.

What Is The Most Important Question For Data Science (And Digital Transformation)?

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What Are The Challenges Of Machine Learning In Big Data Analytics?

Machine Learning is a branch of computer science, a field of Artificial Intelligence. It is a data analysis method that further helps in automating the analytical model building. Alternatively, as the word indicates, it provides the machines (computer systems) with the capability to learn from the data, without external help to make decisions with minimum human interference. With the evolution of new technologies, machine learning has changed a lot over the past few years.

Let us Discuss what Big Data is?

Big data means too much information and analytics means analysis of a large amount of data to filter the information. A human can't do this task efficiently within a time limit. So here is the point where machine learning for big data analytics comes into play. Let us take an example, suppose that you are an owner of the company and need to collect a large amount of information, which is very difficult on its own. Then you start to find a clue that will help you in your business or make decisions faster.

Here you realize that you're dealing with immense information. Your analytics need a little help to make search successful. In machine learning process, more the data you provide to the system, more the system can learn from it, and returning all the information you were searching and hence make your search successful. That is why it works so well with big data analytics. Without big data, it cannot work to its optimum level because of the fact that with less data, the system has few examples to learn from. So we can say that big data has a major role in machine learning.

Instead of various advantages of machine learning in analytics of there are various challenges also.

Let us discuss them one by one:

• Learning from Massive Data: With the advancement of technology, amount of data we process is increasing day by day. In Nov 2017, it was found that Google processes approx. 25PB per day, with time, companies will cross these petabytes of data. The major attribute of data is Volume. So it is a great challenge to process such huge amount of information. To overcome this challenge, Distributed frameworks with parallel computing should be preferred.

• Learning of Different Data Types: There is a large amount of variety in data nowadays. Variety is also a major attribute of big data. Structured, unstructured and semi-structured are three different types of data that further results in the generation of heterogeneous, non-linear and high-dimensional data. Learning from such a great dataset is a challenge and further results in an increase in complexity of data. To overcome this challenge, Data Integration should be used.

•  Learning of Streamed data of high speed: There are various tasks that include completion of work in a certain period of time. Velocity is also one of the major attributes of big data. If the task is not completed in a specified period of time, the results of processing may become less valuable or even worthless too. For this, you can take the example of stock market prediction, earthquake prediction etc. So it is very necessary and challenging task to process the big data in time. To overcome this challenge, online learning approach should be used.

• Learning of Ambiguous and Incomplete Data: Previously, the machine learning algorithms were provided more accurate data relatively. So the results were also accurate at that time. But nowadays, there is an ambiguity in the data because the data is generated from different sources which are uncertain and incomplete too. So, it is a big challenge for machine learning in big data analytics. Example of uncertain data is the data which is generated in wireless networks due to noise, shadowing, fading etc. To overcome this challenge, Distribution based approach should be used.

• Learning of Low-Value Density Data: The main purpose of machine learning for big data analytics is to extract the useful information from a large amount of data for commercial benefits. Value is one of the major attributes of data. To find the significant value from large volumes of data having a low-value density is very challenging. So it is a big challenge for machine learning in big data analytics. To overcome this challenge, Data Mining technologies and knowledge discovery in databases should be used.

The various challenges of Machine Learning in Big Data Analytics are discussed above that should be handled very carefully. There are so many machine learning products, they need to be trained with a large amount of data. It is necessary to make accuracy in machine learning models that they should be trained with structured, relevant and accurate historical information. As there are so many challenges but it is not impossible.

By Gunjan Dogra

Is Blockchain the Latest Revolution in Technology?

The blockchain is more like a digital ledger to store financial transactions just like a book that contains what comes in and what goes out. Unlike traditional ledger, the digital one is a lot more vast and secure with no intermediaries involved.

In Blockchain, each block contains, but not limited to, a cryptographic hash of the previous block along with the transaction data. It can be used by two parties to record transactions in a secure and permanent way. It is managed by a peer-to-peer network and allows the safe transit of digital information.

Why is Blockchain the latest revolution in technology?

Blockchain technology was originally designed to deal with Bitcoin but now it has become the talk of the town, a revolution. During its earlier stage, the technology confronted heavy criticism and rejection but after a thoughtful revision, it came out to be more productive, more useful, and more secure. It has now become a practical way to store data in a digital form that is reconciled from time to time.

Let's take a look at some of the benefits:

Authenticity - The information is stored in blocks that are further stored on Blockchain that cannot be controlled by a single person or identity. It simply means that there are no or very fewer chances of failure and the technology can serve as a reliable space for a business transaction.

Transparency - The tech-savvy people claim that the Blockchain technology is totally transparent. As the blocks are recorded and added to it in chronological order, the participants are able to keep track of the transactions with a lot of ease and without recordkeeping.

Quality - In case of any irregularity, a Blockchain system makes it easier for concerned partied to investigate any issue as the system can lead them all the way to its point of origin. The quality assurance makes it an ideal technology for sectors where tracking the origination and other crucial details are necessary.

No Tampering - As the transactions and records are verified every single time they are passed on from one block to the next, there are less or no chances of error. The accuracy of the process protects the data from tampering, making the technology more user-friendly and efficient.

Agile - In the era when the time is money, Blockchain can play an imperative role by allowing faster dealings. As the system does not require a lengthy process of verification and clearance, it can be used by different industries for closing the deals fast.

Cost Saving - Last but of course not the least, Blockchain is a cost-effective technology because it does not involve any third-party. It makes the system an ideal one for both startups and established organizations.

Well! The time has come to understand the technology and its benefits before applying it to any business...

By Avneet Kaur Sidhu