The New Hot Tech in Data

Introduction

Change is the only true constant in the ever-evolving world of data-driven technologies. Every year new technological advancements grow by leaps and bounds, changing the way databases are both administered and accessed. In fact, changes happen so fast that last year’s juggernaut is today’s dinosaur. Often times theses new technologies take a while to emerge, whereas others take the industry by storm. Here are some of the new hot tech data trends to keep an eye on:

Streaming Data

Streaming data has been around for some time and has allowed companies to assimilate and disseminate information in a faster, and more productive, way. New cutting-edge data streaming and processing techniques have allowed businesses to more efficiently manage and integrate their data to increase their own production and meet their customer’s needs.

In the very-near future database managers will have data that can be used in real time. While large-scale, or “big pipe,” streaming is fairly familiar to most tech businesses, as well as many tech-savvy consumers, the exciting new emerging trend involves the analysis of the data after it is collected, with the data being immediately visible. This results in the data being more complicated, but also more useful and relevant.

Automated Data Platforms

Automated customer data platforms, or CDPs, can uncover the real value in consumer data by providing sales staff with a more complete view of their customer’s demographic and transactional statistics. Some automated customer data platforms have automated machine learning that allows a business to have immediate access to extrapolated data. While CDPs are an emerging technology, automated customer data platforms are gaining in popularity and are one of the new hot tech data trends beginning to see widespread use.

Ecosystem-Integration Software

Ecosystem-integration software allows businesses to share information with their suppliers, partners and customers, allowing companies to stand out from their competition. While many companies are still struggling with separate business-to-business and application integration, EI software provides businesses with the ability to streamline their workflow to improve their business-critical operations.

While some view EI solutions as experimental, these solutions are currently being successfully utilized by retail, manufacturing and e-commerce businesses. Ecosystem integration solutions should become widely accepted within the next few years. Because the new technology is such a game changer for business, any company that does not adopt the new technology may pay the price in lost revenue and ultimate failure.

Next Gen Cloud Computing

While cloud computing has grown in acceptance in recent years, many businesses still view it as an experimental platform. However, the technology continues to gain acceptance as it also continues to improve in efficiency and reliability. Cloud-based computing allows businesses to better control the speed and reliability of their data, allowing businesses to more easily scale their systems up or down without having to manage large, cumbersome and expensive data centers. In fact, cloud-based technology has been gaining so much momentum it is expected to become mainstream within the coming years.

Machine Learning

Machine learning is one of the industries cutting-edge technologies widely seen as the future of data centers. Machine learning allows a computer to learn and improve by what it experiences, without the need to be programmed. In this sense machine learning is a type of AI, or artificial intelligence, and is one of the new hot tech data innovations coming of age. Forward-thinking businesses will recognize the competitive advantage AI can provide them in an ever-increasing competitive marketplace. However, research has shown that over 95 percent of businesses are currently rejecting artificial intelligence as a useful tool in managing their data centers. This may be due to the fact that machine learning is not on the same developmental curve as data processing complexity and storage. Currently only half of all data-science projects make it to production, however, that number should increase significantly as the technology continues to improve.

Digital Threads

While geared towards business that embed software into their products, digital-thread technology will help all companies become more responsive by providing a more comprehensive, real-time picture of how a product’s data is used. This will allow manufacturers to assess their product performance on the fly to determine what changes need to be made. Digital-thread technology is not currently in widespread use, but business that want to flourish in a new hot tech data world will need to adapt if they expect to compete and survive.

Virtual Test Data Management

While virtual databases have been in use for several years, a new level of data management is emerging. Virtual test data management operates within block storage, usually used in SAN, or storage-area networks, where data is stored in volumes. Virtual databases can have the ability to mask user data to safeguard personal information. This technology will be revolutionary for large companies that need to test databases quickly when creating temporary environments for simulating production systems.

Artificial Intelligence for IT Operations

Artificial Intelligence for IT Operations, or AIOps, is the next step in the evolution of AI, allowing machine to run advanced data-processing analytic functions. This emerging hot tech data will give companies that embrace the technology a huge advantage over there competitors as it will become increasingly difficult for human operators to keep pace with AIOps ran systems. Currently the development of this tech has been long on promises and shirt on actual ability, however, that will change rapidly in the years ahead. Predictions indicate that within five years all database-operations software will have some form of AIOps integration.

Automated Machine Learning

Another form of AI, Automated Machine Learning, or AutoML, refers to the process of totally automating machine learning to deal with real-world issues. AutoML will enhance a company’s ability to manage data by automatically selecting the most efficient algorithm for each purpose. While automated machine learning is still in beta testing, AutoML is predicted to become mainstream in the next few years.

Conclusion

As demand for new technology keeps increasing the development of new software to operate the new systems will continue to evolve with it. With new technologies advancing at a frightening pace, the emerging hot tech data of today may be obsolete in just a few years time. The only way for developers, database operators and business owners to know what is coming is to constantly stay abreast of new developments.

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