“Big Data” has become a buzzword in the business world. More and more companies are investing in data analytics and data-driven decision making. As the trend grows companies that stay up to pace will outrun those who fail to utilize the technology. It is essential for managers and executives to stay on top of the data analytics game. In this article, as a data analyst myself, I have made some predictions regarding the direction this all is moving towards. I have also tried to address the challenges that will come with each change in the future.

The Future of Data Analystics

Data Will Grow!

The major player behind the advent of big data analytics is the automation of processes over the last few decades. Our day to day tasks can now be recorded in different forms of data, from transactions to browsing history, everything gives out a tiny bit of info about who we are. Experts predict that by 2020 every individual in the developed world will generate more than 7 Megabytes of data every second. Anybody who knows anything about data knows that’s a lot of data.

As the volume of data grows so does the challenges that come with it. The first challenge is storage. Gigantic proportions of data need mega infrastructure investments. The cloud databases we use on a regular basis are stationed in huge server docks. These server docks require a lot of investment to build and then to keep on running. Companies like Google, Facebook and IBM are trying to step up to the challenge by investing large sums in storage and computation infrastructure. However, I feel it is not a one mans job. Other organizations and even the government needs to step in and start investing to tackle what the future holds.

Any data engineer and data scientist knows one of the most difficult tasks in data analytics is data discovery. Analysts are already overwhelmed with the amount of data we already have. Finding the relevant sets of data in such a vast ocean of information is like looking for a needle in a haystack and analysts spend most of their time doing so. As the volume of data grows the task is only going to get more difficult. However, there is hope. A lot of new people are taking up professions in data sciences. Artificial Intelligence and Machine Learning algorithms are evolving and they give us hope that they will make things easier.

AI and Machine Learning Will Play a Huge Role

On the verge of widespread adoption of predictive analysis, we data scientists find hope in the integration and implementation of AI-based intelligent systems and machine learning protocols. Machine learning techniques are already highly in use. As an example, Google can separate useful mail and spam with over a 95% accuracy. This is done using historical analysis that grows automatically using machine learning-based algorithms. As the volume of data grows the focus will shift towards building smart and self-sufficient systems that can grow as more data is fed. The future of data analytics lies with AI and ML. It is high time for companies to invest in this technology if they do not want to play catch up with their competitors.

Privacy Is Going to Be a Huge Concern

With the recent controversies surrounding Facebook, it should be clear to anybody that privacy and accountability are going to be a huge concern as technology grows. Internet of Things is growing and with it comes a fear of privacy. New regulations will come into place to protect citizens from the claws of corporations with large stakes in the data business. Data has always been bought and sold. However, the question is which data is okay to be shared and which is off limits. A question I put out to you guys to discuss in the comments.

Prescriptive Analysis Will Take Over

The trend in Business Intelligence started with descriptive analysis but soon moved to predictive analysis. Companies are more interested in what the future holds rather than just taking account of what has already happened. The next big leap in data analytics is the incorporation of prescriptive analysis tools within the Business Intelligence atmosphere. Another big shift is going to be all in one software. Gone are the days when businesses bought separate software for everything. Business Intelligence systems are becoming more and more thorough in providing different services to stay competitive.

Productivity Records Will Be Broken

One of the biggest adopters of data analytics and machine learning techniques is the shipping and delivery industry. Companies like UPS, FedEx and DHL have already created new productivity benchmarks with the incorporation of the technology. A common example used is the UPS no left turn policy. Managed strictly by data, UPS saves about 10 million gallons of fuel every year by calculating the best route possible and avoid left turns in right side driving countries. Other courier services have also reached the peak of efficiency using machine learning driven route creation and delivery plans. The aviation industry has also become much more efficient and has cut costs effectively by using data to plan routes and schedules. Taking this precedent, we can surely assume that as more and more industries adopt data-driven intelligent systems to manage their activities, new records in productivity will be created.

Data Professionals Will Be in High Demand

Statistics show there is a serious deficit in the market for data scientists, data engineers, and AI specialists. IDC analysts predict that the industry will grow a whopping 33% annually over the next few years. This means all data related professions will be high demand, high paying jobs.

A Paradigm Shift Could Happen

According to experts in the field of Data Science, a paradigm shift is just waiting to happen. As I have already mentioned, the future of data analytics lies with Artificial Intelligence and Machine Learning. Both of these technologies are based on capable algorithms. In the future, we might see companies competing in advanced algorithms. The software could become irrelevant and the market for algorithms may be born.

The future looks exciting nevertheless!

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