What Exactly Is Big Data

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.

Where is Big Data headed in 2020?

To begin with, predicting what’s going to be in the future has never been an easy task. We don’t know for sure if the machines will ultimately become smarter than man or we will be able to buy self-driving cars. Not yet. We are in the middle of the year 2020 and know for sure that the power of Big Data will dominate our discussion forums for a long time to come.

Well, the wave of innovation is far from over and if this is anything to go by, then Big Data is going to be Bigger, Faster and more Cloudier than any other time in recent memory. The Cloud is everywhere! That’s a positive sign if you ask me, but let’s delved a bit deeper on this burning subject to know what’s going to be trending in the next few months as far as big data is concerned. Once a year we try to take stock of the top trends in the world of big data that is changing the world of business as we know it.

Here’s a rundown of important big data trends we believe is going to make a big splash in the coming months.

Trend #1: Quantum Approach to Big Data

Quantum computing concept has been around for a quite some time. There is a real possibility that it will come into full force in the 2016. But we will have to wait and see if the use of quantum computing becomes more commonplace and widespread. There’s a possibility of coding the machines in a more understandable way. If forward thinking, tech giants are to be believed then a quantum approach to handling massive datasets could solve complex problems. From gene mapping to space exploration, every dataset is solvable with the new quantum-based approach. Quantum computers are going to be more powerful than today’s computers. Quantum computing is said to be the biggest technological breakthrough since the invention of the microprocessor. ‘Bits’ may become passé, welcome quantum ‘qubits’!

Trend #2: The NoSQL Conquest

Especially in the last few months, there has been a significant adoption of NoSQL (Not Only SQL) technologies. A NoSQL technology is modeled with “big data” needs in mind and has many proponents. The benefits of shifting to NoSQL databases are becoming more pronounced. In the enterprise IT landscape, SQL is set to become the dominant query language, even for NoSQL databases. As the SQL ecosystem matures, we will see a gradual shift towards the benefits of schema-less databases. Companies dealing with massive amounts of both structured and unstructured data will move away from traditional SQL database approaches and lean, heavily, on NoSQL databases. SQL databases can only handle structured data, but what about the data that is unstructured? NoSQL can deal with that. Alteryx, Trifacta and Informatica Rev are some of the NoSQL tools that are on the rise and are making a mark in the industry.

Trend #3: Hadoop Adds To Enterprise Standards

Hadoop, an open source technology and created in 2006, has already become a big part of the enterprise IT landscape, driven mainly by the demand for big data analytics. It remains to be seen whether Hadoop is going to completely take over the current database architecture or not, but it is fast gaining traction in many companies for its indispensable advantages. End-users want faster data exploration techniques and with Hadoop you can have it. Many businesses around the world are embracing Hadoop. By using this they will not have to worry about dumping all their enterprise data into Hadoop repository. Without it, extracting valuable insights whenever they want from the vast pools of data is simply not possible. Lines are already blurring between big data analytics and traditional concepts.

Trend #4: Start Fishing In the Big Data Lakes

In 2016, Data Lakes are going to be a big thing. Though it is of recent origin and in early stages of development, the concept of big data lake is becoming a familiar term as far as big data processing and cloud analytics are concerned. For many companies, Data-Lake-as-a-Service is fast emerging as a solution to manage and secure all data. The basic premise of this concept is how to manage, store and use the massive amounts of incoming data from a variety of mediums. Google and Facebook who are in the forefront of cutting-edge technologies are considered to be early adopters of big data lakes.

Trend #5: Increased Data Security and Breaches

Everything is going digital or is set to go digital, and in this context the issue of data security is still a major concern. Data breaches have been in the news and perhaps due to the growth of connected Internet of Things and fast networks, there’ll be more attacks from hackers. Hackers can even kill a Jeep driving on a highway. Data security should never be overlooked. But the thing is that there have been many data breaches in the past and there will be many more in the future too. So what’s the difference? The difference lies on how to implement the right crisis plan in the event of an attack, and how to prevent one. Basically, organizations should focus more on how to handle data security before, during and after a hack.

It’s really exciting to see how big data, connected car, driverless cars, cloud computing, and even emotionally aware robots are changing our lives. While we still go by our blind faith and believe in our gut instincts, but it’ll be interesting to see that how these trends are going to play out and change the way we live.