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Machine learning

Machine learning will determine competitiveness shortly

Machine learning will determine competitiveness shortly


Machine learning is rapidly restructuring the business landscape. With the rise of cloud computing, Big Data, and the increased demand for storage data, new services like DWaaS arise and AI is becoming a determining factor for competitiveness.

While this level of technology was only a vision years ago, thanks to the increase of software development, it will soon be the shaping factor of almost every industry.

The ideal latency between click and the expected response is less than 50 milliseconds. Anything above this is causing a loss for the vendor company. To put this into perspective, the average blink of the eye lasts 150 milliseconds.

Latency is a costly problem

A decade ago, Amazon estimated that every 100 milliseconds of latency cost them 1% in sales. Since then, consumer expectancies have only grown, pioneers of technology setting the standard for all companies.

Latency is the byproduct of legacy systems and outdated processing capabilities, often characteristics of nonnative companies that find it harder to adapt to the required speed. On the other hand, digital-native companies capture, structure, and analyze data with the speed that satisfies the new response time requirements.

Big Data and the cost of decision-making

With the amount of data captured, decision-making is becoming so demanding that organizing data for human consumption will no longer fit growth. AI-based decision-making will be at the basis of modern decision-making.

Humans, on average, cannot process data, simultaneously considering more than three main factors. They look at data, organize it and make a decision based on the labeled data. In this process, a lot of data companies have gathered over the years are lost, mainly data that has a lower statistical significance. Furthermore, humans struggle with the randomness and the heterogeneity of data like video, text, image, or other manifestations of the data. In contrast, machines have no problem analyzing and objectively correlating a vast amount of data. More so, as technology further evolves, the cost of machine decision-making will cost exponentially less.

Altogether, the cost of imperfect human decision-making could easily be so high that it risks the whole company. CEO’s recognizing this risk are setting their vision on modernizing data infrastructure for AI, according to a Deloitte survey, State of the AI and Intelligent automation in business.

The goal is to create models that are predictive

The question is: how is machine learning helping human decision-making, or even replacing it? The machine learning goal is to observe patterns and create predictive models, after processing a vast amount of data. These models answer business questions and predict trends. Briefly, it shows what is the direction and where should the investment of resources go. But also, these models can be used for different specific process-optimization.

Data warehouse-as-a-service – DWaaS

Machine learning needs Big Data. In order to feed the AI with data, the increasing need for data storage appeared. These storage requirements created a new demand on the market. Hence, data warehouse-as-a-services (DwaaS) emerged. In essence, it is an outsourcing model where the service provider configures and manages the hardware and software resources a data warehouse requires based on the client’s data.

More companies are incorporating machine learning in their decision-making process gradually; it becomes a determining factor for their competitiveness.

Predicting the future based on data is more precise and cost-effective in the long run. It also makes companies more agile and also allows the hyper-personalization consumers require. The data-centric approach is the new client-centric approach.

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