Terms Beginning With 'r'

Robotic Process Automation (RPA)

  • January 06, 2021
  • Team Kalkine

What do we mean by Robotic Process Automation or RPA?

Robotic Process Automation is a technology that enables people and businesses to automate the task the way humans used to do across several applications. The main objective of RPA is to shift the process execution from human being to bots. Through this process, it is easy to automate the highly repetitive task by skilled workers.

What are the benefits of RPA?

RPA helps save time as well as money by automating the repetitive task. With the help of RPA, those skilled workers who were involved in these repetitive jobs can be used in other high-value jobs.

Some of the benefits of RPA include:

  • Accelerate time to value: Through RPA, it is possible to create, test and implement new automation schemes in just a few hours instead of days or months.
  • Lessening of human error: As things get automated, the chances of human error gets eliminated. RPA virtually removes copy and paste mistakes from entering the same data into several systems.
  • Improves throughput: As tasks are automated, RPA provides high value to the customers.
  • Less Technical obstacles: In RPA, programming skill is not required to configure bots. Even non-technical staff can use the drag and drop process designer or any related tools to set up bots. It is also possible to record your steps to automate the process using the feature “process recorder”.
  • Meet regulatory compliance requirement: RPA follow the instruction with which they are configured. They also provide audit trail history for each step it takes.
  • Can work tirelessly: As bots are automated, they can run the operations 24 X 7. It can work autonomously and does not require staff to re-activate them.

    ©Kalkine Group 2021

What are the processes which are suitable to RPA?

Being versatile and scalable, Robotic Process Automation has a wide range of applications across the enterprise in several industries. Some of the areas where RPA suits the most includes tasks that are:

  • Constant and routine work.
  • High volume of data.
  • Areas where the chance of human error is high
  • Manual data entry

Some of the cases where RPA can bring value to the core business are:

  • Payroll
  • Employee status change
  • Account payable
  • Account receivable
  • Invoice processing
  • Creation of report
  • Software installation
  • CRM data updates
  • Data migration
  • Vendor onboarding.

What are the steps to implement RPA?

RPA implementation process involves four steps. These are:

Planning Phase:

In the planning phase, there is a need to find processes which should be automated. In this step, we need to follow a checklist which would support in identifying the correct technique.

  • Whether the process is manual and is repetitive.
  • Is there any rule in the process?
  • Whether the input data is in electronic layout and is readable?
  • During the automation process, will the current system work or not?

Once we get the answers to these questions, the next step on the planning phase involves:

  • Set a project team and decide the timeline to implement the process and the methodology to proceed.
  • Agree on the solution design to perform RPA processes.
  • Find a logging method that should be applied to discover problems with running bots.
  • The plan should be outlined clearly to improve RPA implementation.

Development Phase:

Once the planning phase completes, in the development phase, we start developing the automation workflows.

Testing Phase:

In the testing phase, RPA testing cycles run for in-scope automation where any error is identified. In case of any defect, the required correction is made.

Support and Maintenance Phase:

Once testing is complete and RPA went live, support and maintenance provide constant support and helps in instant defect resolution.

Application of RPA industry-wise

  1. Healthcare: In the healthcare industry, RPA is used for patient registration and billing.
  2. Human Resources (HR): In the HR industry, RPA can be used for New employee entering procedures, payroll method, hiring selected candidates.
  3. Insurance: In the Insurance Industry, the application of RPA is seen in claim processing and clearance, premium information.
  4. Manufacturing and Retail industry: In this industry, RPA’s application can be seen in the calculation of sales and bills of material.
  5. Telecom: In the telecom industry, RPA is used for service order management and quality reporting.
  6. Travelling: In the travelling and logistic segment, RPA can be beneficial for ticket booking, passenger details and accounting.
  7. Banking and Financial services: RPA can be applied in card activation, any claim for fraudulent activities etc.
  8. Government: In the government sector, RPA is used to make any changes to the address and renewal of the license.

It is the average of the worst tolerable process that is considered acceptable. The quality level of a process is accepted if its data falls in between the range of the acceptable quality limits.

What is the Dark Web?  The dark web is one such portion of the World Wide Web which is not accessible by regular search engines. The dark web is considered a hotbed for criminal activities, and it is much more than that. Various websites exist on an encrypted network inside the dark web. Standard web browsers and programs cannot find these websites. Once inside the dark web, different sites and pages can be accessed like one does on the web. Scientists believe that the internet we see is only 4% of the entire ocean of the web, meaning the 96% consists of the "Deep and Dark Web".  The user interface used in the dark web is usually internet-based, but it utilises special software which is not part of the standard ones. There are dozens of web browsers to surf the internet, but they all work in the same way. These standard browsers use ports and protocols to request, transfer and view data on the Internet. The website you access may look familiar, but as you enter, it may be illegal or something familiar but otherwise not monitored by anyone else. Therefore, the deep web and the dark web are famous for being anonymous. Also read: Cyber Espionage Campaign: Strings that tie China, Australia and the US How to access dark web browser? In order to access a few areas which are restricted, the user may need a password and a process to follow. A special software called TOR (The Onion Router) or the Freenet has these non-standard connections. These browsers are unlike standard internet browsers and have a process to access. They allow the users to browse around the dark web and are focused on keeping the user identity anonymous. If hacked or accessed, the regular web browser can easily provide user information such as who the user is and whereabouts. Though the dark web is providing 100% anonymity, federal agencies have been successful in tracking down criminal activities on the dark web. It is often said that the person you are talking to on the dark web could either be an FBI agent or a criminal. Image: Kalkine   What happens inside the world of the dark web?  The dark web is famous for allowing sinister activities, but many users go on the dark web to access information which otherwise may not be accessible on standard internet. Such as users from extremely oppressive governments who cut access to the world for their citizens. Unfortunately, such confidential environments also provide open platforms to criminals, terrorists and other such individuals involved in illegal activities.   Hence, experts advise users to not access the dark web even out of curiosity as it is a lawless environment. There have been many incidents where innocent, curious users were trapped and forced to get involved in criminal activities or their digital devices hacked and compromised without their knowledge.  A study conducted by a University of Surrey researcher Dr Michael McGuires in 2019, Into the Web of Profit, shows that the dark web has become worse in recent times. Since 2016 of all the listings on the dark web suggested, 60% could harm companies. Everything illegal and criminal can be found on the dark web, it also has other legitimate options such as chess clubs or book clubs, but because of the anonymity, the user will not know whom he/she is interacting with. Inside the dark web, anonymity and lawless nature make the crimes which exist otherwise in our society hard to trace.  The payment procedure inside the dark web is also different from the World Wide Web. Most often, Bitcoin and Monero cryptocurrency are used for the transactions.    RELATED READ: Knock Knock! Cybercriminal at Your Doorstep   What’s the difference between the deep web and dark web? The dark web is part of the entire deep web and is hidden from regular browsing access. Most people confuse the deep web and the dark web as one entity. It is not. The deep web content includes anything hidden and restricted behind the security wall such as content which otherwise requires paywall or sign-in or blocked by the author. Content which cannot be easily accessible on regular internet such as medical records, membership websites, paid content are available on the deep web; hence it is also called Invisible Web.  No one really knows the total size of the internet, but the experts believe that the standard World Wide Web consists of only 4% internet, the deep web consists of 90% and dark web consists of 6% of the entire internet.  ALSO READ: Technology has changed the way we work amid the COVID-19 crisis: A look at in-demand technologies Image: Kalkine     Also read: It happens again, NZX being bullied by Cyber-attackers- Down for the fourth day   What kind of risk companies face due to the dark web?  The Into the Web of Profit report listed below threats various organisations around the world are facing, especially the ones who have weak or insufficient cybersecurity measures.   Malware attacks Distributed denial of service (DDoS) attacks Botnets Trojan, keyloggers, exploits  Espionage  Credentials access  Phishing  Refunds Customer data Operational data Financial data Intellectual property/ trade secrets    Also read: Cybersecurity and the Requirement of a Resilient Environment in Australia  Are there advantages and disadvantages to the dark web?  The dark web provides complete anonymity, the users get complete privacy to perform any activity, be it illegal or legal. Many countries in the world still have authoritarian regimes offering no civil rights to their people. To such oppressed lot, the dark web provides an opportunity to access news, information, data and also express their views. The dark web is also a perfect place for law agencies to map criminal activities while being undercover. It is also easy to commit gruesome crimes through the dark web as it is complicated and lawless. Criminals can easily use the dark web to compromise someone's privacy, steal data or private information or even hire someone to commit murder.  Do internet users need to be concerned about the dark web?  The simple answer is no unless the user is using the dark web. Study says that most young people visit the dark web out of curiosity. They do not want to indulge in any criminal activity but want to see how the hidden and secret world of the dark web operates. And that is where the possibility of the electronic device IP address getting hacked by other criminals to perform their criminal activities lies.  The earliest use of darknet dates back to the year 2000. Freenet was created at the University of Edinburgh based on a student research paper. Ian Clark wrote the paper in 1999 on the possibility of such an encrypted internet base. Freenet was created to oppose censorship and provide a platform for free speech. The most powerful dark web is TOR, and it was created by the United States government to have a secure encrypted communication in case of emergency and complete disaster. Even today, many law agencies are secretly active inside the world of the dark web to gain access in the criminal world and stay one step ahead.

What is Data Analytics?  Data Analytics involves a set of quantitative and qualitative approaches and processes that can be used to determine useful information for business decision-making. The process involves various patterns and techniques, including: extracting a raw database, and categorising it to identify and analyse the behaviour, relation and connection of the results.  The ultimate goal is to acquire valuable information in order to make decisions for businesses’ benefit and productivity.  In today's competitive times, most companies chalk out their business plan with the help of data analytics. With organisations becoming customer-service oriented, data analytics has become a critical tool to reach the target audience in an effective manner while understanding their requirements. Once data is collected, it is analysed and stored according to organisations’ requirements.  The data analysis process has multiple layers involved, and its diverse modules are not just used in businesses but also in science and social science fields. Rather than making decisions based on just available information, one can utilise data analytics in examining the data in standard ways and churning out the results from it.  It has been observed that companies generally make decisions based on past references and future outcomes. Data analytics appears advantageous in providing useful information towards this end.  Why do Businesses Need to Use Data Analytics?  Many data analytics’ tools and softwares are readily available these days. These systems use resources, such as machine learning algorithms and automation.   Data scientists and analysts are counted amongst the leading career options as well. These professionals use data analytics techniques while researching and presenting useful information for businesses to increase productivity and gain. The process helps companies understand their target audience and determine effective ways to cater to their needs. Data analytics can further be used to design strategies in marketing campaigns and promotions and also evaluate its results.  Data analytics is primarily used in business-to-consumer (B2C) processes to boost business performance and improve the bottom line. There are data collection firms which gather consumer information and provide it to the businesses so that the companies can effectively influence the market. The collected data is not only used to understand and impact consumer behaviour but also determine market economics and its practical implementation.  The data used in the process can be either be data collected in the past or newly updated data. There are various methods to manage consumer and market information. It may come directly from the customers or potential customers or can be purchased from the data collection vendors. The data primarily includes audience demographics, behavioural patterns and expense threshold.  How Can Data Analytics be Effectively Used in Business Processes? Data analytics is an ever-evolving technique. Earlier, the data was collected manually, but with the rise of internet and technology, data is now collected online with the help of search engines and social media platforms. Subsequently, the information is analysed through available software.  Here is a list of some key steps businesses can follow to leverage the benefits of data analytics: Set up crucial metrics: This step reduces the guesswork and provide data-based insights to the businesses. Before embarking on the data analytics process, it is vital to determine the goal for your business. Analysing customer data helps in understanding conversion rate, consumer spending ability, demographics etc. The results of the analysis can support the businesses while making decisions in launching an advertising or marketing campaign. Similarly, the unwanted data can be erased from the database so that the brands can focus on their right target audience. The relevant metrics will change the course of the company and push it in the right direction. Moreover, once your key metrics are set, even when the market conditions change in the future, you can adjust the metrics according to the requirement and achieve the results. Set a clear module: It is important to examine the data correctly by avoiding common mistakes. An ambiguous path can produce confusing insights while wasting time and energy of businesses.  Therefore, it is recommended to draw a clear goal in order to achieve actionable insights. The data, when collected from different sources, need to be merged accurately in the analytics model. Businesses can modulate their data analytics systems either manually or through automation. There are various data modelling practices available in the market. The best use of these techniques can simplify the process of modelling complex data.  Data visualisation: Once the relevant data is collected, and the modules are set to analysis, visualisation of that data will assist in understanding the information correctly. When the businesses have an acute knowledge of what their target audience wants, they can then focus on strategising advertisement and content, which matches the consumers' interest.  It is the critical step in the data analytics process to distinguish insights from information.  Not everyone is comfortable dealing with numbers. Hence, ensuring that key stakeholders understand essential points and information can be displayed in a visually appealing format seem crucial to capitalise on data effectively. Right tools to implement insights:Having access to data and insights can get overwhelming. However, the information is worthless if the businesses are unable to implement it successfully. While it is important to collect the data and set critical metrics and modules to analyse it, it is also imperative to translate the data into practical actions. The eventual goal is to improve sales or grow profits. It is ultimately in the marketers' hands to transform the gained insights into a successful implementation. The consumers' insights should be incorporated while establishing a marketing plan and at all decision-making steps. 

What is Data Mining? Data mining is a process that facilitates the extraction of relevant information from a vast dataset. The process helps to discover a new, accurate and useful pattern in the data to derive helpful pattern in data and relevant information from the dataset for organization or individual who requires it. Key Features of data mining include: Based on the trend and behaviour analysis, data mining helps to predict pattern automatically. Predicts the possible outcome. Helps to create decision-oriented information. Focuses on large datasets and databases for analysis. Clustering based on findings and a visually documented group of facts that were earlier hidden. How does data mining work? The first step of the data mining process includes the collection of data and loading it into the data warehouse. In the next step, the data is stored and managed on cloud or in-house servers. Business analyst, data miners, IT professionals or the management team then extracts these data from the sources and accordingly access and determine the way they want to organize the data. The application software performs data sorting based on user’s result. In the last step, the user presents the data in the presentable format, which could be in the form of a graph or table.         Image Source: © Kalkine Group 2020 What is the process of data mining? Multiple processes are involved in the implementation of data mining before mining happens. These processes include: Business Research: Before we begin the process of data mining, we must have a complete understanding of the business problem, business objectives, the resources available plus the existing scenario to meet these requirements. Having a fair knowledge of these topics would help to create a detailed data mining plan that meets the goals set up by the business. Data Quality Checks: Once we have all the data collected, we must check the data so that there are no blockages in the data integration process. The quality assurance helps to detect any core irregularities in the data like missing data interpolation. Data Cleaning: A vital process, data cleaning costumes a considerable amount of time in the selection, formatting, and anonymization of data. Data Transformation: Once data cleaning completes, the next process involves data transformation. It comprises of five stages comprising, data smoothing, data summary, data generalization, data normalization and data attribute construction. Data Modelling: In this process, several mathematical models are implemented in the dataset. What are the techniques of data mining? Association: Association (or the relation technique) is the most used data mining technique. In this technique, the transaction and the relationship between the items are used to discover a pattern. Association is used for market basket analysis which is done to identify all those products which customer buy together. An example of this is a department store, where we find those goods close to each other, which the customers generally buy together, like bread, butter, jam, eggs. Clustering: Clustering technique involves the creation of a meaningful object with common characteristics. An example of this is the placement of books in the library in a way that a similar category of books is there on the same shelf. Classification: As the name suggests, the classification technique helps the user to classify and variable in the dataset into pre-defined groups and classes. It uses linear programming, statistics, decision tree and artificial neural networks. Through the classification technique, we can develop software that can be modelled so that data can be classified into different classes. Prediction: Prediction techniques help to identify the dependent and the independent variables. Based on the past sales data, a business can use this technique to identify how the business would do in the future. It can help the user to determine whether the business would make a profit or not. Sequential Pattern: In this technique, the transaction data is used and though this data, the user identifies similar trends, pattern, and events over a period. An example is the historical sales data which a department store pulls out to identify the items in the store which customer purchases together at different times of the year. Applications of data mining Data mining techniques find their applications across a broad range of industries. Some of the applications are listed below: Healthcare Education Customer Relationship Management Manufacturing Market Basket Analysis Finance and Banking Insurance Fraud Detection Monitoring Pattern Classification Data Mining Tools Data mining aims to find out the hidden, valid and all possible patterns in a large dataset. In this process, there are several tools available in the market that helps in data mining. Below is a list of ten of the most widely used data mining tools: SAS Data mining Teradata R-Programing Board Dundas Inetsoft H3O Qlik RapidMiner Oracle BI

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