By the end of 2018, the big data market will be worth nearly $50 billion, which means we can expect big data’s potential revolution in the coming years as well.
Most valuable resource for the last century was oil. For this century, it is data. – Economist
Table of Contents
What exactly is big data? Why it’s taking the world by storm?
Big data is emerging as a new world currency. Almost we are very next to the big data revolution. Why do we believe that? It is because we have already gone through the changes we’ve experienced from all the industries, which include online business to logistics which have taken off tremendously in recent years.
Most of the business owners are adopting big data for their daily operations because they try to reduce production cost and improve efficiencies.
Business giants like Microsoft, Facebook, TCS have been managing their active streamline operations with big data tools. They have agreed that their performance is progressive than it was earlier and more comfortable. And we’re able to deliver best when compared to their competitors in the market.
It is high time for any business to get big data integrated by 2020, because of the overwhelming response it has got so far.
Let’s dive deep into the topic for profound big data insights
Whether you are working in the manufacturing sector or service sector, your organization rely on data. Data is the heart of business decision-making.
We are continuously producing a lot of data, for example via social media, telecommunication, entertainment channels, etc. As the data goes way beyond one’s estimations. In general, daily we upload 58 million pictures, 440 million tweets and 2 billion files which entirely produces 2.5 quintillion bytes a day, that’s a lot of zeros, it is no wonder we call this a BIG DATA.
What’s more important is what we can do with this data? How do we process big data? Right tools with minimal resources can handle loads of data when analyzed and processed. Initially, people used to work with powerful algorithms to handle, which was later managed by automated systems using cloud and endless network of normal servers understanding powerful algorithms. We already have existing systems that can analyze over a million pieces of data and in a short time. Here are few of those use cases listed.
Use case: Netflix
For example, a video streaming website Netflix analyzes the data obtained from their viewers like most popular shows, watching patterns, and searches. This way they produce successful series with the perfect combination of interests, actors, directors, and storyline.
Use case: Driverless Cars
The future of automobiles can rely on self-driving cars (autonomous cars) which are capable of driving according to its environment, understanding the traffic, etc. Here the traffic data has been analyzed and processed by car manufacturers to develop a car that can drive completely accident free all by itself.
Use case: Healthcare
With the increase in negligence of human activities on the environment, we can anticipate new threats in healthcare. In the future, we can even use the previous/existing data of DNA to determine the necessary treatment. This way curing genetic diseases like cancer would become much more accessible.
Future trends of dig data
Research and developments are in progress to make use of existing big data tools more feasible and reliable in understanding and processing data. What can we expect from this research and developments in the coming years?
To stay competitive in the big data world today, trends in big data are compared to wind breezes. When you find which way the wind is heading, the next moment it changes its direction. The following trends are likely to happen in the future.
- Using the Internet of Things, we expect 20.8 billion connected devices by 2020.
- Artificial Intelligence by 2020, 85% of customers will interact with AI, instead of humans.
- Augmented and virtual reality estimates 64.8 million AR devices will be shaped in 2020.
- Digital assistants will handle 40% of mobile interactions by 2020.
- Security Analytics estimates fortune 500 companies to generate 10TB of data every month to look for security flaws.
Organizations smart way to handle big data
Big data software tool helps the user to organize huge raw data along with the past records and stores it for future reference.
Firstly data is generated in multiple types which includes semi-structured data, unstructured data, and structured data. This is collectively processed by big data and it gets converted to well organized structured data. This data helps in understanding the existing performance of the organization progress and thereby framing new goals for further enhancements and business developments.
How big data helps organizations?
Segmentation of tasks makes it easy to obtain a productive outcome. This is what every organization try to implement, and the same is with the case of applying Big Data.
Following are the different phases of using Big Data, that will focus on effective utilization of big data and how data creates value for an organization.
1. Visibility :
Making big data accessible in a timely fashion to relevant stakeholders create a tremendous amount of value.
Use case: Food Processing Unit
We have a food processing unit, which has three departments, say manufacturing, engineering, and R&D departments spread worldwide. If the data is accessible to all the departments, then it reduces processing time but also helps in improving the quality of the product.
2. Analyzing and Discovering information
Most of the organization rely on external sources for analytics, so that organization can easily understand current trends in the market, and they can schedule further enhancements. It leads to increase in delivering qualitative services with customer satisfaction thereby adds value to the organization profits.
Use case: eCommerce
Usually, eCommerce websites like Amazon, Flipkart, Zomato, FoodPanda rely their business on advanced analytics processing big data.
Let’s see how these advancements are impacting eCommerce landscape.
Have you ever wondered watching your favorite products you’ve browsed or searched for appearing on different platforms like Facebook, Google, and Twitter?
How do they follow you?
It’s only just because of advanced big data analytics. Here big data helps retailers to the target audience with appropriate alerts based on their previous engagements with an application.
3. Segmentation and customization :
The big data enables organizations in creating highly specific segmentation and, tailored products and services to meet their needs. This can also be used in the social sector to accurately segment populations and target benefit schemes targeted to specific needs. Segmentation of customers based on various parameters can aid to target marketing campaigns and tailoring of products to suit the needs of customers.
4. Aid decision making :
In the fast-changing world, to sustain the competition, organizations need to react faster to market changes. To obtain this, the marketing team need to predict and analyze existing marketing trends according to the competition. Here big data analytics can greatly improve the decision-making process. Automated Fraud Alert system in credit card processing to an automatic fine-tuning of inventory are examples of systems which aid or automate decision making based on Big Data Analytics.
Big data needs changes in both qualitative and quantitative aspects. New technologies need to adopt these changes and have to process data that can deliver balanced quality and quantity thereby enhancing organizational progress. With the kind of innovations carried out in the world of technology, we anticipate an equal number of chances to misuse technology as well.
Big data has got its potential spread into healthcare and have proven its outstanding abilities in delivering qualitative services. The same kind of response can be expected from other industries as well. As a matter of data/information, one has to take care of securing the data as this can lead to massive loss once unauthorized access is granted to any part of the data.