RPA vs Bigdata

RPA vs Big Data

In short form we can say Robotic Process Automation as RPA. It is one of the leading process streamlining technology. RPA is industrial advancement in data processing & storage. It is also making waves across a variety of industries. It presents intriguing options for companies when stacked against RPA. We’re talking about the advent and generation of Big Data. It impacts on how companies perform the following. They are,

 

  • Gather.
  • Analyze.
  • Share reporting.
  • Advanced metrics to refine business processes.
  • Achieve defined benchmarks or goals.

McKinsey is a global leader in management consulting. According to him “the data has exploding & analyzing large data sets. It is so called big data. It will become a key basis of competition. It will underpin new waves of the following. They are,

 

  • Productivity growth.
  • Innovation.
  • Consumer surplus.

Leaders in every sector must grapple with the implications of big data. It is not just a few data-oriented managers.”

 

In other words, much like RPA, Big Data is a technology trend. It is becoming transformative across every industry. In our digital world, the amount of captured data is expected to grow.

 

This alignment between RPA & Big Data has led some analysts to question. Whether these two platforms can be coordinated to function. Which is in conjunction with each other for enhanced optimization. This optimization is for back office and even front office tasks?

 

But would such a partnership function? What are the value propositions in coordinating these two technologies? With the help of RPA & its focus on automating repetitive tasks. This combined with the insights from Big Data is possible. The companies can combine data & action. This is to operate on truly lean principles. It will provide increased visibility. It will provide transparency across a company’s entire value chain.

 

Defining Big Data

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The Big Data has certainly permeated such as follows. They are,

  • Industries.
  • Automotive.
  • Manufacturing.
  • Host of others.

The definition of the term Big Data is somewhat tricky. It will be for debate depending on the analyst and industry. The MIT Technology Review considers six different meanings. This will suggest by companies like follows. They are,

 

  • Gartner.
  • Oracle.
  • Microsoft.
  • Intel.

Since, there are some adjust ability. It is to clarify what is Big Data?  Similarly, how it manipulates in the context of RPA.

 

We can define Big Data as any compilation of unstructured data. This may be on the open web in a company’s databases. This may also even from the social media. The data can include the following. They are,

 

  • Product pricing information.
  • Customer records.
  • Web browsing history.

It will accumulate very quickly. It is complex in its variety as well as exists in massive volumes. Big Data is a volume endeavor. It has ability to apply analytical & computing power. This is possible without an initial the structure. Big Data’s true value emerges when this data becomes structured. It can help the companies accomplish deeper inquiry through the following. They are,

 

  • Application of algorithms.
  • Analytical techniques.
  • Software technologies.

When analyzed & structured in this way, Big Data can become the most useful tool. This is used to extract knowledge from the large quantities of information. The companies are constantly collecting the information.

 

The recognized trends can point to places where processes are already optimized. They can also reveal problem areas and deficiencies in back office tasks. Once we identify these weak points, companies can target them for improvement.

 

Relation between RPA as well as Big Data

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The process of extracting meaning from Big Data is one where RPA is helpful. It can provide analytical capacity to examine data. Yet, how specifically do RPA and Big Data work together? What can companies learn from utilizing both to the greatest extent? RPA is a tool. It not only generates Big Data. It also provides useful analysis to obtain its value. The insights provided by the relation between RPA as well as Big Data. It can be used to recognize hiccups within business processes. It will help the companies streamline these points within their operations.

 

For example: Software robots track. It will record their own actions as follows. They are,

 

  • Whether automating data entry.
  • Claims or order processing.
  • Copy-paste tasks.
  • Ability to gather information about customers.

Robots can provide much more information than a human employee. All this generated data isn’t useful without analysis. This is another vital capacity RPA can provide. The analytic potential of RPA can be used to examine. It will make sense of this collected information. For example: An online retailer collects data from multiple sources. These data are about the following. They are,

 

  • A shopper’s buying habits.
  • Including product preferences.
  • Desired price range.

By using RPA to link data sources, we can compare this information. The information is compared to the company’s current offerings. Company will be able to perform more effectively. It will offer the customer certain products as well as promotions.

 

Big Data can check to gain information about customer needs & desires. It can also provide responsive of internal business activities. This is for both in the back office and the front office. RPA can provide data analytics. This will be on the number of transactions software robots have completed. This will also provide the time taken to do so. The transactions that generated exceptions. It required human intervention & time needed to finish running current tasks.

 

RPA is used to glean the knowledge from Big Data. It is useful. It becomes exponentially valuable for companies. A global staffing & IT consulting company will suggest as follows. “The use of big data analytics through RPA installation can pinpoint. It can be action able tasks for improvement and optimization. Turning large amounts of raw data into useful patterns. This is for institution decision-making. From this where we can identify the real beauty of RPA.” This is from the white paper by Digital Intelligence Systems.

 

Responding to this uncovered information, the companies can leverage the following. They are,

 

  • The benefits of automation.
  • The advanced data process.
  • Analysis.

The automation is one of the important avenues. Big Data can become valuable and trans formative through this. The information provided from Big Data & the analytics done by RPA. It becomes easier for companies to gain insight about the following. They are,

 

  • Customers.
  • Business patterns.
  • Industry trends.
  • Internal workings.

Exercising initiative in response to combination of RPA & Big Data. The companies can use the insights gained to make business processes more efficient. Thereby saving time and increasing accuracy. By gaining more robust knowledge of customers, the companies will offer the following. They are,

 

  • Services in a more targeted and personalized way.
  • Ensuring a more viable.
  • Competitive business platform.

The process of analyzing data & applying these new insights to current business activities. It is still dependent on the following. They are,

 

  • Human direction,
  • The relationship formed by merging RPA and Big Data.

It is incredibly beneficial. RPA is currently one of the best tools. It can be used to extract insights from Big Data. This is in order to substantially reduce the process bottlenecks. It will increase the optimization of the business outcomes.

 

Example Application

Robot workforce uses a set of human-determined parameters. It is used to shift through tones of data. Setting these parameters correctly is a vital step. It is used to create the effective RPA solution. It will define the step on your way to amazing data analytics.

 

Automation Anywhere is a leading automation provider. It has delivered an interesting ‘action framework’. It will command RPA to sort data. This is possible using a variety of parameters. Here are three ways data we could distinguish.

 

  • Good to know: All numbers are “green” and within their thresholds.
  • Data seems interesting: The number seems better than expected. It may drill me to drill down further. Invites a drill-down or ad-hoc analysis action.
  • Data to act upon it: Numbers are “red”. Outside of their thresholds. Action needed immediately.

You can see in the diagram below how this may action.

 

RPA Big Data

RPA excels in two main areas when applied to data. Cutting down big data to make it useful for human controllers. Cleaning up existing data to aid in identifying processes.

 

In an example: outlined by the Abridge Report. We found the following. “When first deployed in a claims processing operation, 70% of incoming claims. This may automate with the rest tagged as exceptions. It routed to human reviewers. The reviewers are trained to adjudicate the exceptions. The base data is the insurer’s criteria.”

 

The best example is the robot worker analyzing data. It is passing anomalies to human controllers. In a customer-facing chatbot role, RPA can lighten the load of human. It will allow them to act on more complex and higher value tasks.

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October 13, 2019
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