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RPA and Data: How Robotics Is Facilitating Innovation By Automating Data Science

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RPA and Data: How Robotics Is Facilitating Innovation By Automating Data Science

Learn more about how robotics, data science, and automation come together in businesses that span industries.

· AI Zone ·
Free Resource

What are the things that go into making a business succeed?

While customer service, marketing, adept employees, and cutting edge technology are all required to take businesses to the next level, something that companies need even more than any of those today is data. Data helps companies make decisions, identify trends, and enable better customer service.

Every day, businesses generate an astronomical amount of data. Data is the most valuable asset today, and businesses are increasingly seeking ways to gather, enrich, and generate value from it. In fact, a Big Decisions Survey reported that 92% of businesses worldwide are already data-driven. However, analyzing big data is not easy, and doing it manually can be a long and painstakingly difficult process.

Enter Robotic Process Animation.

RPA is being used by companies today to not only automate repetitive processes but also to sift through huge volumes of data to identify relevant information for users. In a Deloitte conducted Global RPA Survey, it was found that 90% of organizations were aiming to adopt RPA to improve data quality/accuracy. This only goes to show how eager businesses are to automate data collection processes.

Here, we will look at the earlier connection between RPA and data science, the initiatives being taken to gear RPA for data science automation, and the ways in which it can be brought about.

RPA and Data Science

Robotic Process Automation and data science have enjoyed a mutually advantageous relationship that has been perfectly equitable. With the help of the insights accrued from data science's advanced analytics, RPA bots would implement punctual actions, thus enabling the bots with more intelligence and greater enterprise applicability.

New Initiatives in RPA Towards Data Science

Of late, however, RPA's automation has evolved into the realm of data science. This is part of a bigger movement to digitize data science along with self-service analytics platforms, machine learning, and visual solutions for constructing predictive models.

This initiative of RPA is furthered in two principal ways. The first of them involves a swathe of AI approaches like deep learning, natural language processing, and computer vision. And RPA will be used to automate the crucial facets of the predictive model building process for selecting the best algorithm to achieve business tasks and deploying them.

This development will eventually realize RPA's expansion and diversification into data science and enable business users with better data to help them perform their jobs better and for truly democratizing AI.

Ways in Which This Initiatives Can Be Brought Into Action

1. Training Predictive Models

The development of machine learning models to equip virtual agents with greater intelligence requires huge amounts of labeled training data. However, finding that data to instruct models on how to predict desired business outcomes is difficult because data scientists often lack the adequate amount of labeled data to teach advanced ML models how to anticipate the necessary business outcomes. Additionally, the data that's available to them could be theoretical or data science sandbox datasets, finding little application in real-world cases.

Data scientists, therefore, believe that it is better to train AI models with data from production settings. For example, they can collect a host of information from disparate sources to determine whether an applicant has been approved for a loan or not. Once that information has been gathered and formatted, it becomes the basis for the banker's decision. The AI models and the bots learn to recognize these decisions in the form of labeled input data for training machine learning models to make them smarter.

2. Algorithm Selection and AutoML

Before training predictive models, it is crucial to know and be able to choose the right predictive model and its underlying algorithm from the data science pipeline for a particular use case. Often, data scientists create and combine various models for this purpose. However, RPA can automate this part of data science with the help of AutoML, which applies a host of different algorithms to determine the best model based upon the one that predicts the human results most accurately.

3. Bot Learning

It has already been established how useful production data is for training machine learning models. Even though the user's labeled decision is the principal source of data for training machine learning models, it is influenced by other modes of learning that indirectly shape the model training process. These modes of learning are:

Computer Vision: Computer vision allows bots to "see a screen," analyze data sources, and decide from where to get the data that best support business use cases.

Natural Language Processing: NLP in RPA works in tandem with deep learning to create bots that can evaluate structured and unstructured information to extract and structure data for further analysis.

Connectors: Connectors let bots carry out the different steps required for their processes to be able to work with varying OS(s).

These capabilities inform the action required to retrieve the necessary data for a particular business use case and provide the labeled input data to train machine learning models as well. Automating this facet of data science facilitates the training of machine learning models by pushing it to the business users instead of the data scientists.

Advantages of Automated Data Science

A while ago, McKinsey had said that:

"The amount of data in our world has been exploding, and analyzing large data sets — so-called big data — will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus… leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers."

The conditions for RPA in data science have never been better, and the supposed benefits of leveraging automation have never been greater. For example, automating data science leads to:

  • being able to extract precise reports from your data
  • spending lesser time organizing, fixing, and compiling the data
  • being able to use data to predict user behavior
  • extracting measurable insights into what is working for your business

A general occurrence is that poor quality data is often the result of human error. Frequent inefficiencies and poor adherence to processes by users can disrupt your data chain. However, with an efficient RPA solution, your collection and extension will not be affected by any of these elements, and your data application will have a better yield.

Conclusion

A non-invasive approach to automating repetitive tasks, RPA is intensifying towards innovation by automating data science to simulate, visualize, and recognize areas of improvement in existing processes. Apart from attaining early process improvements, lowered process cycle time, cost savings, and increased throughput capacity with RPA, you can use it to leverage additional data to truly revolutionize business processes.

Topics:
ai, artificial inteeligence, data science, machine learning, robotic process automation

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