by Saviour Nickolas Derel Joseph Fernandez

The term “Big Data” may have been around for some time now, but there is still quite a lot of confusion about what it means. In truth, the concept is continuously evolving, as it remains the driving force behind many ongoing waves of digital transformation, including artificial intelligence, data science and the Internet of Things. But what exactly is Big Data and how is it changing our world?

Big Data:

It all starts with the explosion in the amount of data we have generated since the dawn of the digital age. This is largely due to the rise of computers, the Internet and technology capable of capturing data from the world we live in. Going back even before computers and databases, we had paper transaction records, customer records etc. Computers, and particularly spreadsheets and databases, gave us a way to store and organize data on a large scale. Suddenly, information was available at the click of a mouse.

We’ve come a long way since early spreadsheets and databases, though. Today, every two days we create as much data as we did from the beginning of time until 2000. And the amount of data we’re creating continues to increase rapidly.

Nowadays, almost every action we take leaves a digital trail. We generate data whenever we go online, when we carry our GPS-equipped smartphones, when we communicate with our friends through social media or chat applications, and when we shop. You could say we leave digital footprints with everything we do that involves a digital action, which is almost everything. On top of this, the amount of machine-generated data is rapidly growing too.

How does Big Data work?

Big Data works on the principle that the more you know about anything or any situation, the more reliably you can gain new insights and make predictions about what will happen in the future. By comparing more data points, relationships begin to emerge that were previously hidden, and these relationships enable us to learn and make smarter decisions. Most commonly, this is done through a process that involves building models, based on the data we can collect, and then running simulations, tweaking the value of data points each time and monitoring how it impacts our results. This process is automated – today’s advanced analytics technology will run millions of these simulations, tweaking all the possible variables until it finds a pattern – or an insight – that helps solve the problem it is working on.

Anything that wasn’t easily organised into rows and columns was simply too difficult to work with and was ignored. Now though, advances in storage and analytics mean that we can capture, store and work with different types of data. Thus, “data” can now mean anything from databases to photos, videos, sound recordings, written text and sensor data.

To make sense of all this messy data, Big Data projects often use cutting-edge analytics involving artificial intelligence and machine learning. By teaching computers to identify what this data represents– through image recognition or natural language processing, for example – they can learn to spot patterns much more quickly and reliably than humans.

Industrial impact of Big Data in 2020:

Machine Learning and Artificial Intelligence will proliferate

The deadly duo will get beefed up with more muscles. Continuing with our round-up of the latest trends in big data, we will now take stock of how AI and ML are doing in the big data industry. Artificial intelligence and machine learning are the two sturdy technological workhorses working hard to transform the seemingly unwieldy big data into an approachable stack. Deploying them will enable businesses to experience the algorithmic magic via various practical applications like video analytics, pattern recognition, customer churn modelling, dynamic pricing, fraud detection, and many more. IDC predicts that spending on AI and ML will rise to $57.6 billion in 2021. Similarly, companies pouring money into AI are optimistic that their revenues will increase by 39% in 2020.

Raise of Quantum Computing

The next computing juggernaut is getting ready to strike, the quantum computers. These are the powerful computers that have principles of Quantum Mechanics working on their base. Although, you must wait patiently for at least another half a decade before the technology hits the mainstream. One thing is for sure; it will push the envelope of traditional computing and do analytics of unthinkable proportions. Predictions for big data are thus incomplete without quantum computing

Edge analytics will gain increased traction

The phenomenal proliferation of IoT devices demands a different kind of analytics solution and edge analytics is probably the befitting answer. Edge analytics means conducting real-time analysis of data at the edge of a network or the point where data is being captured without transporting that data to a centralized data store. For its on-site nature, it offers certain cool benefits: reduction in bandwidth requirements, minimization of the impact of load spikes, reduction in latency, and superb scalability. Surely, edge analytics will find more corporate takers in future. One survey says between 2017 and 2025, the total edge analytics market will expand at a moderately high CAGR of 27.6% to pass the $25 billion mark. This will have a noticeable impact on big data analytics as well.

Dark data

So, what is Dark Data, anyway? Every day, businesses collect a lot of digital data that is stored but is never used for any purposes other than regulatory compliance and since we never know when it might become useful. Since data storage is easier, businesses are not leaving anything out. Old data formats, files, documents within the organization are just lying there and being accumulated in huge amounts every second. This unstructured data can be a goldmine of insights, but only if it is analysed effectively. According to IBM, by 2020, upwards of 93% of all data will fall under Dark Data category. Thus, big data in 2020 will inarguably reflect the inclusion of Dark Data. The fact is we must process all types of data to extract maximum benefit from data crunching.

Usage:

This ever-growing stream of sensor information, photographs, text, voice and video data means we can now use data in ways that were not possible before. This is revolutionising the world of business across almost every industry. Companies can now accurately predict what specific segments of customers will want to buy, and when to buy. And Big Data is also helping companies run their operations in a much more efficient way.

Even outside of business, Big Data projects are already helping to change our world in several ways, such as:

  • Improving healthcare: Data-driven medicine involves analysing vast numbers of medical records and images for patterns that can help spot disease early and develop new medicines.
  • Predicting and responding to natural and man-made disasters: Sensor data can be analysed to predict where earthquakes are likely to strike next, and patterns of human behavior give clues that help organisations give relief to survivors and much more.
  • Preventing crime: Police forces are increasingly adopting data-driven strategies based on their own intelligence and public data sets in order to deploy resources more efficiently and act as a deterrent where one is needed.
  • Marketing effectiveness: Big Data, along with being able to help businesses and organizations in making smart decisions also drastically increases the sales and marketing effectiveness of the businesses and organizations thus highly improving their performances in the industry.
  • Prediction and Decision making: Now that the organizations can analyse Big Data, they have successfully started using Big Data to mitigate risks, revolving around various factors of their businesses. Using Big Data to reduce the risks regarding the decisions of the organizations and making predictions has become one of the many benefits coming from big data in industries.

Concerns:

Big Data gives us unprecedented insights and opportunities, but it also raises concerns and questions that must be addressed:

  • Data privacy: The Big Data we now generate contains a lot of information about our personal lives, much of which we have a right to keep private
  • Data security: Even if we decide we are happy for someone to have our data for a purpose, can we trust them to keep it safe?
  • Data discrimination: When everything is known, will it become acceptable to discriminate against people based on data we have on their lives? We already use credit scoring to decide who can borrow money, and insurance is heavily data-driven.
  • Data quality: Not enough emphasis on quality and contextual relevance. The trend with technology is collecting more raw data closer to the end user. The danger is data in raw format has quality issues. Reducing the gap between the end user and raw data increases issues in data quality.

Facing up to these challenges is an important part of Big Data, and they must be addressed by organisations who want to take advantage of data. Failure to do so can leave businesses vulnerable, not just in terms of their reputation, but also legally and financially.

Conclusion:

As we all know Data started with 0’s and 1’s, but now it has evolved much more than our expectation, that’s how our technology has grown. It is going to be increasing further in the coming future, in simple term “Data rules the world”.

Data is changing our world and the way we live at an unprecedented rate. If Big Data is capable of all this today – just imagine what it will be capable of tomorrow. The amount of data available to us is only going to increase, and analytics technology will become more advanced.

For businesses, the ability to leverage Big Data is going to become increasingly critical in the coming years. Those companies that view data as a strategic asset are the ones that will survive and thrive. Those that ignore this revolution risk being left behind.