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22 March, 2019

Machine Learning and other Big Data Trends for 2019

The use of data in business has changed enormously recently. Using big data effectively is deeply correlated with having a successful, tech-savvy business as it helps to reduce costs, improve marketing and drive growth.

Most experts agree that now we’ve truly entered the age of data as it appliances have grown exponentially and it relates to almost every business field imaginable. Understanding what the most important trends are in big data is key in today’s business environment.

Now, more than ever, big data is here to stay and to help businesses process information on target audience habits to improve their products, and much more.

What’s coming next? Here are the three most important big data trends for this year:

Machine Learning

Probably this is one of the most hyped terms of the year, and it has a profoundly disruptive effect in many industries like finance and retail. However, GPU optimizations, better processing power, and open source algorithms have helped to improve the available technology.

In Machine Learning algorithm takes control using stored data in a “controlled” environment. In newer models, like MapR, streaming data gives information from IoT (Internet of Things) allowing real-time Machine Learning. This process permits flexible, environment-adapted responses to each situation, improving results and reducing costs.

Obviously, passing from a standard model with a controlled environment and a somewhat limited learning data to an open system increases algorithm complexity. Thus, as the primary model adjusts, Machine Learning predicts outcomes with better accuracy.

AI Platforms

Recent improvements on AI platforms have improved its use for processing Big Data, which means their impact will increase. For example, Absolutdata ran an email campaign but used AI instead of regular Analytics which resulted in a 50 % sales increase.

AI platforms are improved frameworks that allow faster and more effective communication with Data Scientists. This reduces effort duplication, automates tasks and eliminates time-consuming activity such as data processing and customer profile building, which AI platforms automate.

AI platforms guarantee work is distributed evenly and completed as quickly as possible, this through five layers: 1) Data and integration, allowing access to the data; 2) Experimentation, which allows data scientist to advance and test their theories; 3) Operation, which backs model governance to deploy containerized models; 4) Intelligence, which organizes the services and supports the Intelligence, and 5) Experience layer, which interacts with the users through augmented reality, or conversational intelligence.

Augmented Analytics

If you hear the term “augmented” it will probably make you remember Augmented Reality, like in Pokémon Go. Augmented analytics use the same principle but in a completely different process, augmented analytics are a mix of machine learning and real analytics.

It is a process where machines automate data and share insights. So, it is an automation practice that will reduce time dedicated for the most tedious processes allowing data analysts to focus on more specialized problems.

Conclusion

All of this data trends mean that there is going to be even more data to be managed, thus, increasing the amount of personnel needed to handle it and even more innovation in the industry. Big data management is part of the future of every industry, so all businesses better plan to start using big data analytics and marketing to stay at the top of their market.

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