Automation is Triggering Huge Changes to Data Science

Abhinav Gupta
Abhinav Gupta, Director Mobility at Techugo
Published on Aug 06, 2019 in Custom Software Developers Resources
Automation is Triggering Huge Changes to Data Science

Have you heard about Data Science? Did you read anywhere that data science is transforming various industries and infusing a big change in human life?

Well! the fact is that there is no dearth of tales in reference to AI and data science. Though, it is hard to say that which tale is true and which one is just a rumor.

However, there is great hype around data science, and you can find its mention in various magazines and can read about it so often. The stories of data science always hover around two narratives, one is a better future and other is an industrial revolution. As per various studies, data science and automation will bring both opportunities and flaws under their hoods. With its emergence in the market, some people will surely lose their jobs, though numbers of new jobs will be created.

However, before you assimilate data science, you need to grasp automation tactics. So, read further, as I am unlocking the esoteric aspects of data science and automation.

What Is Data Automation?

To understand this technique, first, you need to understand Automation. Basically, automation is a tech solution that enables machines to perform the process with minimal human assistance.

While Data automation is all about the utilization of automatic equipment, processes or systems for collecting, storing and processing data procured from a source. This takes the implementation of various AI tactics and algorithms that can learn from the heaps of data and incur fruitful information.

And! Here comes the data science, which is all about extracting knowledge and insights from data of both structured and unstructured form.

From this, it seems that data automation doesn't have any limitation. This space is expanding and the resultant technology- Data science has become a major area where businesses are keen to invest. Indeed, this technology is in vogue, as it is not just capable of curving the customer experience but also bring a significant change in operations, supply chain, risk management, revenues, and many other business functions. This technology enables organizations to establish a data-centric decision-making process, and aids in running digital transformation and AI initiatives.

Actually, this technology is too tempting that makes investors to seek business in this space. Consequently, 4% of CIOs have already implemented artificial intelligence in collaboration of digital transformation and 46 % have winded up their plans to begin the implementation.

Data Science Processes

Though, it is quite daunting to implement data science and data automation in enterprises. It seems that owing to the cumbersome task that enterprise data science projects include makes implementation dreary. And! the tasks that make it hefty are data collection, featuring engineering, visualization & production, machine learning, and last-mile ETL. To accomplish all of these tasks, it takes several months and a deft team that can bring all the processes in collaboration. All of these processes require a specialized skill set including software architect, domain experts, data scientists, data engineers, and business intelligence engineers.

For instance, a mobile app development enterprise system that manages employee data located in different countries in distinct branches and subsidiaries. The automation system will include the following tasks, after identifying a business use case.

  1. Data Collection: the first system will load all data and will make profile data for each employee separately.
  2. Last Mile ETL: In this process layer, the system validates the data and form data architecture.
  3. Feature engineering: It includes hypothesis, implementation, computation, selection, and non-linearization of features of employee data.
  4. Machine Learning: This layer is dedicated to "train" and "validation" of data.
  5. Visualization: It is all about visualization decision that what data needs to be displayed to users and whatnot.
  6. Production: At last, production takes place that includes replacement process and process deployment.

This whole process looks quite simple but it requires hundreds of hours beating on the craft. Though, this is the traditional approach. Now there are numbers of tools that are inculcating great ease. Actually, businesses need faster systems that can perform data automation in a minimal time frame.

Incidentally, the fact is last-mile ETL and feature engineering are two main pain points of a data science project. As ML needs a single flat table of data, known as a feature table. Any data scientist can play with that table through the ML algorithm. However, the fact is enterprise data is never a single flat table data set. It includes several data tables having an in-comprehensive and complex relationship.

Thereby, these are the most challenging and time-taking steps in data science projects. Owing to that, data automation is an essential trail that is impacting data science. Rather, automation tools are impacting the practice at a wider extent. The data science wasn't as wide awake as now and that is only because of the automation tool.

The building block of automation tools is machine learning that simplifies data automation in a variety of ways. While the question is how this will impact data science? Don't stress yourself ! Take a look at the further explanation.

How Will Automation Tool Impact Data Science?

You can always hear an essential question spluttering in the market, will automation tools replace data scientists and domain experts?

Well! this is competently a hypothetical foresight, as no tool can replace human skill and expertise. More importantly, the automation tools with impact data science in three critical ways:

  1. The biggest change will be the change in the process, as the " waterfall" approach is being practiced in traditional automation. These tools are beneficial, they make easier and faster try on concepts related to ETL, data cleansing and feature engineering, so data scientists can utilize more powerful use cases.
  2. These tools make are very effective, as they allow people with diverse skills to perform data use case and execute them on data science.
  3. The last but not the least, it will surge up the use of AI and data science, and many other enterprises will implement these technologies and will move to a data-driven culture.

The significant aspect is that the world will see a new phase, once data science will get completely immersed in urban space. Surely, this technology will bring a huge change. From enabling faster outputs, to a completely new class of data science users, data science and automation is able to surge return in investment and bring more value for a business. Developers and data scientist at companies like Techugo, are engrossed in utilizing the potential of these technologies. To know more, stay tuned with us.

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About the author
Abhinav GuptaDirector Mobility at Techugo

Abhinav Gupta is Director Mobility at Techugo- a Mobile App Development Company. Abhinav has a keen interest in technologies and is willing to capture every detail utilized within the space of innovation. He drives the best practices of process and t...

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