Data science is changing as technology advancement is continuing to shape our future. Modern business is evolving and getting transformed by big data and AI to make business decisions that are causing great impact due to the application of Data Science in analyzing and predicting the future. For any organization to gain a competitive advantage, even survive in this ever-changing environment, it must use advanced technology for it to thrive, therefore, many companies are being forced to overcome these challenges by the use of Data science to stand out.

This is inevitable, organizations must adapt to the new reality and change the way they do business and business models because business is not as usual anymore. Things are going to be different in the coming years as disruption is on its initiation. AI and Machine learning are impacting digital transformation and it is becoming crucial for every business to bend its way of doing business towards Data science for it to survive.  As the field of Data science grows, every day, some factors are shaping all these changes we are seen and yet to see.

Real-Time or in-memory computing

There is a need to do everything in real-time, which is doing real-time data analysis to improve on decision making. Large companies are noticing the effects of making a decision fast, therefore, this is fueling demand to have Data Science technology that can be able to have in-memory computing.

In-memory computing allows data to be processed without getting stored in the hard-disk of a computer. This is the ability to process everything in memory without storing the data and later accessing it for processing. This wastes time, therefore, for a better time saving, in-memory computing can be used to make sure that everything is done in a flash of a second, deciding in a real-time. The quicker a company analyses data, the better it will be and more competitive it will become.

Adoption of this type of computing is going to shape the future of data science as in-memory computing will help in meeting the need of businesses to quickly analyze data and make informed decisions that are reliable and that bring a lot of change in a business environment.

Most of the organizations have been using a system known as distributed caching, which is still slow, but with in-memory computing, everything will be processed as it gets inputted into the system without being stored in the hard-disks. Another advantage is that; this technology will help in reducing infrastructure cost as there will be no need for a complex analytics system that has big storage hard-disks.

Containers and hybrid clouds

Data environments are becoming flexible and capable of being transferred to where the business demand is. Cloud and containers are shaping the future of Data science completely by having the ability to scale computing across the data centers and public cloud. This happens without changing applications and is helping developers become more creative and ambitious in what they do. There is an increase in teamwork, coordination, and working together, with the use of containers and hybrid clouds. With the hybrid cloud data management and IT department can satisfy any demand without having a lift or sifting data. Investments are data centers is helping companies and bring more benefits as cloud help in integration in data management.

Database as a service

As cloud computing is becoming more relevant to every organization and its benefits are being witnessed, most companies are beginning to consume databases as a service to capture the befits of cloud computing. One of the befits of using a Database as a service will be an operational cost that most companies have, like having a database expert which is costly. Therefore, outsourcing this by having it as a service, will make a great impact on the company. Another benefit is that taking advantage of open source services such as Microsoft Azure, Google Cloud Platform and even Amazon Web Services will help to make sure that data is stored in the nearby where it is needed the most reducing latency. Database as a Service (DBaaS) is easy to manage and makes engineers and the experts of data science start focusing on more important and productive activities. Besides, DBaaS is going to change everything we know about Data Science.

Innovations like the Internet of Things (IoT)

Innovations like the Internet of Things are shaping the way Data Science works. All the devices that are connected to the Internet are sharing resources. This has dramatically shaped the way Data Science works. A lot of data and information is available for use due to the use of IoT. Therefore, the Internet of Things will make data science work smoothly with easily accessible data, which is better prepared and freshly generated. IoT is dealing with complex data, which is giving better results, helping in making an informed decision.

Blockchain technology

The future is promising, that’s for a fact. The use of blockchain technology will bring a new refreshment in a whole new way. The way data is stored, shared, and protected is being revolutionized by the use of blockchain technology. Therefore, this technology is shaping Data Science day by day as this technology is growing and most experts are getting to understand the way it works. The Blockchain is ensuring trust by maintaining the decentralized ledger. Both blockchains will impact Data Science by controlling erroneous data by making sure it’s clean and is not duplicated. In addition, Blockchain will ensure that the data is secure and will maintain its privacy, protecting it from cyberattacks and any other security breaches that can occur on the internet. Therefore, blockchain will be there to validate larger amounts of data for data science to analyze and predictions from reliable information.

Blockchain is a game-changer. This technology is a problem solver as it will be able to make sure all the decentralized and centralized data is validated. Furthermore, Blockchain integrates well with other technologies like cloud computing, AI, the Internet of Things (IoT), and many others, making it highly useful to the field of Data Science.

Data science will continue to be shaped by the advancement in technology and innovations that make it more productive. With the use of new technology, it will be able to more informed decisions in real-time causing a great impact on the business environments.

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