Integrate machine learning to enable companies to build high quality services
The world has experienced the industrial revolution, and now we are in the era of digital revolution. Machine learning, artificial intelligence, and big data analytics are the realities of today's world.
I recently had the opportunity to talk to Talend Vice President Ciaran Dynes and Datalytyx Supervisor JusTInMullen. Talend is a software integration provider that provides big data Solutions for businesses, and Datalytyx is a leading provider of large data engineering, data analytics and cloud solutions businesses that deliver faster, more efficient business operations. More profitable.
Evolution of big data operations
To gain a deeper understanding of the evolution of big data operations, I asked JusTInMullen about the challenges it faced five years ago and why they needed a modern, integrated platform. He responded, “We face challenges similar to our customers.†Before big data analysis, we all faced similar problems.
He responded, “We face similar challenges faced by our customers.†Before big data analysis, this was what I called “data analysis dilemmasâ€. For the most part, the analysis of data is primarily the manual aggregation and data processing of the basic system. Then, the biggest challenge we may face is how to smooth and manage the data before applying the different analysis algorithms to analyze the raw data and visualize the results in a meaningful way. â€
He further added, "Our customers want more than just an analysis, but also hope to continuously refresh KPI performance in months and years." In the manual data engineering practice, we are difficult to meet customers The requirements, and this is when we decided to need a reliable and reliable data management platform to solve these challenges.
The emergence of data science
Most economists and social scientists are concerned about automation technologies that are replacing manufacturing and commerce. If digitization and automation continue to grow at the same rate, machines have a great potential to replace humans. We have seen some similar phenomena in today's world, but in the future this situation will become more prominent.
However, Dynes said, “Data scientists are providing solutions to complex problems in different areas today.†They use useful information in data analysis to understand and solve problems. Data science is an input, and output is automation. Machine automation, but humans provide the necessary input to get the desired output.
This creates a balance between the needs of labor and machine services. Automation and data science are all parallel. No other process in a process is incomplete. Raw data has no meaning if it cannot be manipulated to produce meaningful results; likewise, machine learning cannot be achieved without enough relevant data.
Integrate big data into business models
Dynes said: "Companies are realizing the importance of data and incorporating big data and machine learning solutions into their business models." He added, "We see automation happening." This is in e-commerce and manufacturing. The field is obvious, and there is also a huge application prospect in the field of mobile banking and finance. â€
When I asked him about his views on the machine learning process and the shift in platform requirements, he added, “Requirements have always existed.†Data analysis has always been crucial. The only difference is that five years ago, companies monopolized data, and data was always stored secretly. “Who owns this data, whoever has the power, only a few well-known market participants can get the data.â€
JusTIn has worked with different companies. Some of the well-known clients include CalorGas, Jaeger and Wejo. When talking about the challenges these companies face before implementing advanced data analytics or machine learning, he said, "The biggest challenge for most of my customers is the accumulation of critical data in one place, allowing complex algorithms to run simultaneously. This shows a better analytical result. "Data analysis needs to be continuous, not one-off, which is crucial. â€
The reason for rapid digitization
Dynes said: "We are experiencing rapid digitization for two main reasons." "In the past few years, this technology has grown exponentially. Second, organizational culture has changed dramatically." Add, "With the advent of open source technologies and cloud platforms, data is now more easily captured." "More and more people are now getting information, and they are using it to gain benefits."
In addition to technological advances and developments, “the new generation entering the workplace is also dependent on technology.†They rely heavily on technology to do their daily work. They are more open to transparent communication. Therefore, it is easier to collect data from this generation because they are ready to discuss their views and preferences. They are ready to ask questions and answer impossible questions," Dynes said.
When it comes to the challenges companies face when choosing a big data analytics solution, Mullen adds: "While using machine learning, the industry's current challenges are twofold. The first challenge they face is data collection, Data collation, data management (quality), and data aggregation. The second challenge is the lack of anti-human skills in data engineering, advanced analytics, and machine learning.
“You need to bring the new world together with the old world.†The old world relies heavily on big data collection, while the new world focuses on real-time data solutions.
Dynes said: "You need to integrate the new world with the old world." The old world relies heavily on data collection, while the new world focuses on data solutions. Today, in this industry, there are already solutions that meet both of these requirements.
His conclusion is, "The importance of data engineering is not negligible. Machine learning is like Pandora's box." It has a wide range of applications in many fields. Once you build a high-quality service, the company will bring you Come to more business. This is a good thing.
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