Before reading this blog, let me ask you this, we all know about big data, right? If yes, then great! But if not, you can read our blog on 7 Vs of big data.

Big Data isn’t about which data is big or large in volume. It is one form of data that is generated from various sources and consists of numerous types of data in different formats.

The various sources being Artificial Intelligence, Internet of things (IoT), cloud, etc. In 2019, Big Data is not a trend anymore rather it has become an established part of many businesses.

Let’s shed some light on how Big Data became an important entity as it is now.

Here are the few Big Data adoption trends which were dominant in 2018

The fast growth of IoT networks

AI became more accessible

Quantum Computing

Smarter chatbots

Dark Data revelation

Enhanced cybersecurity

2018 was a year where big data coupled with AI moved to the forefront of research and development. Now that we know what big data was all about in 2018, let’s have a look at some statistics about big data.

Big Data Adoption Statistics for 2019 & Beyond

40 percent of businesses say they need to manage unstructured data on a frequent basis.

95 percent of businesses need to manage unstructured data.

More than 150 zettabytes (150 trillion gigabytes) of data will need analysis by 2025.

The big data industry will be worth an estimated $77 billion by 2023.

40.3 percent of respondents suggested that big data adoption was held up by a lack of organizational alignment or agility.

Full of information right? Let us now cascade into what big data means to every industry in 2019.

Industry-wise trends of Big Data in 2019

Healthcare Industry

Patient-centric care

In this modern era, healthcare companies are more focused to refine the quality of patient care. More and more people in the healthcare industry are discovering the benefits of patient-related data.

This is imperative not only for patients but for the whole industry as it is increasing the quality of healthcare services

Healthcare IoT

IoT devices help in tracking the patient’s behavior, ranging from heart functioning to glucose levels.

However, most of the data is unstructured but few smart devices utilizing machine learning can possibly replace a doctor with the help of a simple phone call from a nurse.

Retail Industry

Dynamic Pricing

We all know the price is ever-changing and dynamic but using machine learning and artificial intelligence in the retail industry can be one of the most cutting-edge experiences.

Prices for a product are subject to fluctuate based on season, demand, supply, and competitor prices.

Machine learning will enable a company to take into account all these factors and generate the right price at the right time.

Retail Store Analytics

Consumer behavior insights are provided by in-store analytics, utilizing everything from video cameras to in-store Wi-Fi networks.

Big brands will track when a person entered and left the store, how he/she moved inside, and the key areas that they visited.

By use of these analytics along with some basic demographic data, stores will optimize their in-store experience.

Manufacturing industry

Production Quality

Big data will allow organizations to leverage from SixSigma and Lean management programs that will help them in reducing the waste and eradicate any variability in the production process.

With the usage of big data and advanced analytics, manufacturers will examine key performance indicators that affect the overall quality of production.

Supply Chain Optimization

With modern supply chains evolving and becoming ever more complex, a big data analytics solution will deliver clear supply chain visibility.

It will provide instant access to key supply chain information such as supplier performance, product quality along with order delivery time.

Banking industry

Employee Engagement

Big Data has received so much attention and there are so many uses to it that many companies forget about one application that has huge potential and can have a huge impact on their business and employee experience.

When done right, it can help in tracking, analyzing, and sharing employee performance metrics.

By applying Big Data analytics to employees’ performance, it can help in identifying not only the top performers but also the employees who are struggling as well.

These tools will allow companies to look at real-time data, rather than depending only on annual reviews which are based on human memory.

Fraud detection

Every financial transaction leaves behind a data trail. For example, in case a credit card information gets stolen, any transactions contradicting to buying habits of owners are detected by machine learning analytics.

Bank of the account holder can be alerted instantly, so they can put a hold on the transaction until verified.

Education Industry

Boosting Learning Effectiveness

By using Big Data, educators will receive numerous kinds of data about an individual learner. They will get personal evaluations, test results, attendance records, and other related types of data.

Teachers can analyze this data so that the learning process can be modified for specific needs.

Improved Student Results

In the long run, big data will provide teachers with insights into student’s behavior. Teachers can put students’ results in context so that factors influencing them can be understood.

Teachers will observe the amount of time taken by the student to answer a question.

Well, now that we are equipped with Big Data adoption trends from all the industries, let’s shed some light on the challenges that Big Data will face.

Here are some challenges Big Data will face

Issues with data management

The proliferation of data silos continue as new data generation continues

Data governance remains a concern

Recruitment and retention of big data experts

Data validation

Conclusion

In this ever-changing and dynamic era, data generation is inevitable and how we handle the data and what use we put the data to, is our choice.

It is the data that will drive our present and future. Data is required in every field today and applying that data to increase efficiency is the best use we can put it to.

But with roses come thorns. Similarly, with data come challenges like handling of that data, its negative uses, data is verified or not.

Imagine hackers accessing your financial data, or for that matter accessing and getting hold of a companies’ data, they can pretty much do anything with it.

So, we don’t know a full-proof solution to it right now but like everything future will have a plan for it too. It is a leap of faith the world has to take.

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