Trending November 2023 # Celonis Process Mining In ’23: Products, Features & Competitors # Suggested December 2023 # Top 16 Popular

You are reading the article Celonis Process Mining In ’23: Products, Features & Competitors updated in November 2023 on the website Eastwest.edu.vn. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested December 2023 Celonis Process Mining In ’23: Products, Features & Competitors

Celonis claims to be the process mining market leader with 60% market share. Its popularity has been increasing (See Figure 1). However, recent acquisitions from tech giants, such as Myinvenio by IBM and Minit by Microsoft, have created formidable competitors. A good understanding of Celonis process mining and its competitors is necessary to find the right process mining software for your company.

Therefore, our research explains Celonis as a firm, mentions its products and compares it to its competitors.

Figure 1: Increasing interest for Celonis Source: Google Trends.

What is Celonis?

Celonis was founded in 2011 and raised the bar after launching a scalable process mining solution, Intelligent Business Cloud, in 2023 and Celonis Execution Management System in 2023.

Read our comprehensive analysis to learn about leading process mining vendors.

Is Celonis owned by SAP?

No, SAP is not an investor or owner of Celonis. Celonis is a partner of SAP and offers data connectors and applications to capture and analyze event logs data registered in SAP, ERP systems. Celonis also provides this functionality for other non-SAP systems as well.

Explore how to leverage process mining in SAP in detail.

What products does Celonis offer?

Celonis process mining provides products with on-premise, cloud, and hybrid options. These products can be found under three license types:

Academic.

Commercial.

Free trial. Learn what Celonis free trial offers and compare it to other process mining vendors with free trials.

Celonis bundles all its products under Celonis EMS:

Celonis Execution Management System (EMS)

Celonis Execution Management System (EMS) is a low-code environment that includes:

Connectors to gather data from desktops, documents, and other systems to automatically deploy automations

Task mining to capture user interactions

Process intelligence to map processes, find inefficiencies and suggest improvements

Latest developments in Celonis EMS

In 2023:

Celonis acquired Process Analytics Factory (PAF), a company developing process mining for Power BI. This way, Celonis integrated the Microsoft Power platform into EMS, improving business intelligence and connecting to the Microsoft Community.

Celonis extended the EMS platform with two new components: Process Sphere and Business Miner. Process Sphere creates more complete business process maps, while Business Miner facilitates breakpoints identification in process flows.

Check out Celonis EMS demo video for more:

Capabilities

According to the Celonis website, Celonis process mining features include:

Data extraction

Celonis Process Mining can pull data from various sources, such as Postgres, HANA, or Amazon Redshift. It also offers connectors to ERP (e.g., SAP), CRM (e.g., Salesforce and ServiceNow), and other systems to extract real-time data on business processes.

Celonis Task Mining collects data by recording user activities through an app, called workforce productivity. The tool automatically creates a dataset with screen records and analyzes them in terms of employee experience and performance metrics.

For more on how Celonis task mining works, read our task mining article.

Read our process mining software comparison to learn and compare these process mining capabilities in detail.

Is Celonis an ETL tool?

Yes, Celonis collects data from cloud-based applications and on-premise systems and creates a table containing steps involved in the given process with the help of Extract, Transform, Load (ETL) tools. Once the table is ready, Celonis Process Mining analyzes the data to map and develop process KPIs.

Celonis alternatives

The process Mining market was projected to grow by 40% to 50% last year, as market size stats indicate. Such growth suggests increasing competition. Currently vendors like IBM and UiPath are Celonis’ main competitors.

Process mining vendors offer additional capabilities to remain agile in a competitive market. Process mining trends show that in the following years, there will be more:

Top 7 Use Cases Of Process Mining In Finance

Process mining can be applied in multiple business areas in the financial services industry, such as Purchase-To-Pay, audit, and accounts payable to increase business efficiency and gain a comprehensive overview of business processes to implement and monitor digital transformation strategies.

This article provides a list of process mining applications and recommendations for financial institutions and businesses that want to leverage process mining.

Some general and specific use cases of process mining include:

1. Increase process efficiency by discovering bottlenecks

Process mining can be applied to different financial processes that generate event logs such as account payables (AP), receivables (AR), and procurement to visualize process execution and identify bottlenecks.

Process Mining can provide strategies and recommendations to eliminate process inefficiencies. For example, in claims settlement, mining can identify root causes by measuring the average amount of time to settle a claim.

2. Discover automation opportunities

Leveraging process mining provides insights about processes that can benefit from automation. For example, process mining allows financial organizations to discover the possibility of automation in transactions such as:

purchase-to-pay processes that take longer due to mistakes and manual interventions can be enhanced by implementing automation solutions such as RPA.

Invoice processes where automation can enable quicker and less costly invoicing by automating repetitive tasks such as data extraction from PDFs.

You can read our in-depth article to learn more on how to implement RPA in finance.

3. Compliance checking

Process mining enables financial institutions to monitor and log process improvement overtime to ensure that their processes are audit-ready. Process mining also provides conformance check and root cause analysis, allowing financial institutions to compare their processes against rules and regulations and analyze the reasons behind the deviations.

To learn more on specific applications of process mining in compliance and audit:

4. Maverick buying in purchase-to-pay

Banks can leverage process mining in purchase-to-pay, which refers to a company’s entire purchasing process. For example, Process mining can help reduce maverick buying (i.e. purchases of employees without informing the procurement department). Process mining enables the user to check the necessary steps in a P2P process, including:

Generating a receipt after a purchase order (PO)

Matching the PO to a contract: There should not be a PO without a contract (especially if the amount of orders is large in quantity and regular)

5. Root causes for delays and incorrect invoices

Process mining enables users to discover the root causes of delays within the departments. Banks can uncover the reasons for the late fees (e.g. credit card debt payment, loan payments) and minimize late costs, for example, by introducing incentives to customers who pay their debts on time. Also, process mining allows banks to identify the root causes for mistakes and duplicate payments that generate extra workload.

6. Customer satisfaction

Some process mining techniques (e.g. process discovery) can constantly monitor processes in real-time. Such monitoring enables companies to optimize their processes which increase customer satisfaction.

For example, process mining can help financial service providers to understand customer behavior by discovering customer engagement insights (e.g. length of call, pattern in issues). Banks that understand customer perspectives can change their operations to improve customer experiences. Process mining allowed many retail banks to discover customers’ difficulties during the bank account confirmation process and provide fast confirmation for opening a bank account.

7. Risk mitigation

Process mining helps financial service providers to avoid potential risks when they innovate or modify processes in their systems by constantly monitoring processes and providing data-driven insights and optimization opportunities.

Recommendations

The best practices to apply process mining in finance include:

Understand the purpose and possible outcomes: Before applying process mining, providers must sort out the main purposes of the process mining application. For example, banks can update their website and FAQ section to prevent unnecessary customer calls.

Ensure data quality and availability: To implement process mining, banks and financial institutions must identify, collect, and clean the data.

Assure data security: Financial data is sensitive because it includes privileged customer information (e.g. spending records, credit score). To better protect customer data, financial organizations must leverage privacy-enhancing technologies, such as homomorphic encryption and zero-knowledge-proof to enable process mining users to process confidential data without exposing it to unauthorized individuals.

3 Steps To Implement Rpa With Robotic Process Mining In 2023

RPA can decrease costs and errors by 25-60% while allowing employees to engage in higher value-added activities, improving employee and customer satisfaction by 62%. This is why 64% of enterprises have started implementing RPA. Despite the growing interest in RPA, business leaders face numerous challenges when choosing a process to automate. Operations can be problematic in implementing RPA because they include:

Low-value added tasks and activities

High-level cognitive tasks

Sub-processes with numerous tasks and activities

Robotic process mining (RPM), also known as task mining, overcomes such pitfalls by automatically discovering activities and detecting tasks for further improvement and automation.

This article will explain RPM and how it can enable business leaders to succeed in their RPA projects.

What is robotic process mining?

After identifying the tasks to automate, business automation teams spend some time to look for ways to automate these tasks. Automatic bot generation can help shorten this time by automatically translating the user behavior recordings into a RPA script.

RPM enables process automation in three stages:

1. Collect and clean data

RPM captures the user activities as UI logs. UI logs include events that are executed for one or more tasks by a user in a given time. For example selecting a field in a form, editing it and opening an application or Web page. These activities are recorded through some extensions or plugins and then merged into a raw UI log.

UI log data specifies these activities based on the event type, timestamp, origin and other information, such as the labels for buttons, as Table 1 illustrates. Once the data is collected, RPM tools identify and filter events that are irrelevant to any action and should not be automated.

Table 1

Source: Robotic Process Mining: Vision and Challenges

2. Detect best tasks for RPA

RPM discovers repetitive sequences of actions executed by users to complete the tasks. Once the routines are discovered, RPM identifies the ones that can be automated by evaluating the execution frequency and length of the routines.

For example, customer reps pull all records of a customer to help them regarding their recent payment issue. RPM can discover the routines the customer reps do to pull and understand these records before solving the issue. The tool then can pinpoint the specific activities that can be replicated by an RPA bot.

3. Implement RPA

Once RPM detects the candidate routines for automation, it digs into analyzing the activation conditions of these routines. It looks for specifications and time that triggers the routine and performance of the routine. It maps the actions based on these specifications and then generates an executable RPA script.

The RPA bot performs the routine when the specified conditions are met. During this phase, users can stop the execution of the bot to make small corrections if needed. Otherwise, the bot replaces the manual work.

Figure 1 illustrates how robotic process mining enables RPA implementation in three steps. RPM identifies the routines that users execute, determines the tasks that are candidates for automation, and automates the tasks by recognising the patterns and specifications.

Figure 1

Source: Robotic Process Mining: Vision and Challenges

How to choose an RPM vendor?

One challenge business leaders and analysts may face is that they often get lost among process intelligence tools. Leading process mining vendors leverage ML and other AI applications, known as intelligent process mining, or offer task mining capabilities along with process mining to provide a more profound understanding of business operations. Therefore, business leaders and analysts can benefit from RPM through such process mining tools.

Data Transformation In Data Mining

Data transformation is an essential phase in the data mining process. It entails transforming unprocessed data into an analytically useful format. Data transformation seeks to enhance the consistency and relevance of the data for the desired analysis while reducing redundancy and improving data quality.

The significance of data transformation in data mining as well as some typical data transformation techniques will be covered in this article.

Importance of Data Transformation in Data Mining

Data transformation is an essential element of data mining for several reasons. Firstly, analyzing unstructured, erroneous or incomplete raw data can be challenging and time-consuming. Therefore, the primary objective of data transformation is to tidy up and organize the data to facilitate further analysis.

Second, data transformation aids in bringing down the data’s complexity. For data mining algorithms to find patterns, trends, and linkages, structured data is necessary. By eliminating superfluous or unnecessary information and translating the data into an appropriate format, data transformation aids in the data’s simplification.

Thirdly, data transformation makes sure the data is reliable and pertinent for the analysis that is being performed. Different data sources may use different formats, scales, and measurement units. Data transformation aids in standardizing the data, allowing for better comparison and analysis.

The accuracy and efficiency of data mining algorithms may also be increased with the aid of data transformation. Data mining algorithms can more precisely and successfully find patterns and trends by translating the data into a suitable format.

Common Techniques for Data Transformation

Data transformation may be done using a variety of methods. Data cleansing, data integration, and data reduction are the three basic categories that may be used to group these procedures.

Data Cleaning

Finding and fixing data mistakes, inconsistencies, and inaccuracies is known as data cleaning. This can be accomplished using a variety of methods, including −

Missing values can be removed by utilizing the data’s mean, median, or mode or by interpolating, among other methods.

Eliminating duplicates − Duplicates are found by comparing the values of each record, then deleting those that match.

Managing outliers − By utilizing statistical approaches, outliers may be located and either eliminated or rectified.

Data Integration

Data integration is the process of merging information from many datasets. This can be accomplished using a variety of methods, including −

Merging − Merging is the process of integrating datasets with similar variables.

Joining − Merging datasets that contain similar observations is known as joining.

Appending − Adding additional observations or variables to an existing dataset is known as appending.

Data Reduction

Reducing the quantity and complexity of the data is known as data reduction. This can be accomplished using a variety of methods, including −

Sampling − Instead of utilizing the complete dataset for analysis, sampling entails choosing a portion of the data.

Dimensionality reduction − Dimensionality reduction is the process of lowering the number of variables in a dataset while keeping the most crucial data.

Aggregation − Aggregation is the process

Advantages of Data Transformation in Data Mining

Better data quality − Unstructured and incomplete raw data is frequently inappropriate for analysis. Data transformation aids in preparing the data for analysis by cleaning and organizing it. This can raise the data’s quality and increase its dependability for making decisions.

Reduced complexity − In order to find patterns, trends, and correlations, data mining algorithms need organized data. By deleting superfluous or unnecessary information and transforming the data into the appropriate format, data transformation aids in data simplification. This may make the data less complicated and hence simpler to analyze.

Enhanced accuracy − By putting the data into a usable format, data mining algorithms can more precisely and successfully spot patterns and trends. This may result in better forecasts and decision-making.

Standardization − The formats, scales, and measuring units used by various data sources may differ. Data transformation aids in standardizing the data, allowing for better comparison and analysis. This can increase the data’s consistency and relevance for the analysis that is being planned.

Efficiency gain − Data transformation can help data mining algorithms operate more effectively by minimizing the quantity of data that has to be analyzed. Data mining algorithms can analyze the data more quickly and accurately by lowering the size and complexity of the data.

Advantages of Data Transformation in Data Mining

Information loss − Data transformation can cause information loss, especially when data reduction techniques are applied. As a result, the analysis may become less accurate and reliable.

Overfitting − When data is overly tightly fitted to the model as a result of data transformation, overfitting occurs. As a result, the model may become excessively dependent on the original set of data and become inapplicable to fresh data.

Complexity − The data mining process might become more complex as a result of data transformation. The analysis’s findings may be challenging to comprehend and interpret as a result.

Cost − Data transformation may be costly, especially when large amounts of data must be altered. This might be a serious challenge for organizations with limited resources.

Time-consuming − Data transformation might take a while, especially when there are a lot of data to be changed. The analysis and decision-making process may be delayed as a result.

Conclusion

The conversion of raw data into a more suitable format for analysis, known as data transformation, is a crucial step in the data mining process. This stage involves reducing redundancies in the data, which enhances its quality and relevance for analysis. There are different approaches to data transformation, such as data integration, data reduction, and data cleansing, which enable data mining computers to identify patterns and trends more accurately and efficiently.

Steps In Business Process Reengineering

Introduction

Business Process Reengineering (BPR) is a management approach put forward by Michael Hammer, former professor of Massachusetts Institute of Technology. According to him, reengineering is the fundamental rethinking that initiates a radical change in overall business processes with a view to achieve drastic enhancement in performance, quality, delivery time and revenue. It aims at structurally changing the work flows targeting the entire organization. The reengineering approach concentrated on business out comes than the tasks involved. Involvement of information technology is crucial in designing and developing process flows and automating tasks to make it free of human errors. As the entire reengineering effort is complex with a heavy budget allocation, structured and systematic execution of the process is highly critical for the successful completion of the project. Such organised and disciplined steps or process flow of reengineering are discussed here.

Steps Involved in BPR

Business Process Reengineering needs clarity in long term business vision, creative and determined leaders to take it ahead. It requires tremendous effort and the cost involved in its implementation is huge. Each phase needs to be accurately carried out under a reengineering activity. The steps involved in reengineering process are explained below −

STEP 1: Identify the need for change

The entire reengineering efforts are directed towards change and this makes it crucial to identify the purpose of initiating change.

Gathering information on the purpose of business, the existing process rules and systems, business vision and goals will give a clear picture of the current situation of the company. This will help to find the areas that are lacking behind the expected performance.

Determine the outcome anticipated as a result of the BPR efforts

An exhaustive identification of work rules and existing culture within the process lines will help to better understand the operational structure, requirements and the work cycle.

All these information can lead to confirming the actual need for change and this serves as a vital part prior to initiation of BPR activities.

STEP 2: BPR Team

The next step is to organize a competent team for leading and executing the BPR project. Dynamic, creative and determined people from across the functional units together with the management representatives ideally make a well balanced BPR team.

Having an efficient BPR team with the right skills to communicate, convince the need for redesigning and drive the activities towards revamping is of utmost importance.

The team does not necessarily need independent resource to cater to each roles, but the team together should have the said skills, responsibilities and accountability.

This step will help to simplify and eliminate time delays and errors during the course of BPR action plan.

STEP 3: Isolation and Selection

Once the first two steps are in place, the next task is to isolate the gaps and analyse process flaws in the existing system of operations.

The actual expected outcome of a process need to be recognized before selecting it for redesign.

Workflow bottlenecks captured, delivery time gaps, avoidable complexity in procedures, assets and resources that are not optimally utilised are recorded.

Inefficient processes that have scope of drastic improvement needs to be chosen for reengineering only after strict scrutiny.

STEP 4: Process mapping and blueprint for redesign

The business process that are found and selected for redesign needs to be precisely planned for fundamental revamping.

Activities for restructuring the operations can be productively planned through process mapping. Existing complex procedures that cause time delays and excess costs can be simplified to develop a clear process flow chart.

Blueprinting enables the aid of technology to develop workflow models and data needs of proposed business processes.

Brainstorming and benchmarking activities should be carried out with vigor, taking into consideration human resources, performance, incentives and technology infrastructure.

Another activity that needs to be carried out in this step is to develop performance indicators to measure the reengineering tasks on implementation. Ensuring performance measurements help improve the process efficiency.

This will in turn help to set realistic organizational goals

While drafting the plan for redesigning the focus should be on the resultant outcome and not on the functional unit tasks

Clarity in ownership of operational functions will facilitate easy decision making and reduce turn around time.

Activities isolated as redundant or non-essential should be discarded though it may lead to cancellation of a job role.

Collection of information should be at the source point and should be easily accessible in real time with the help of communication networks.

STEP 5: Implementation and Change management

This is the stage where all the prior efforts towards reengineering are put to action.

Implementation of planned activities requires careful and diligent execution.

As a radical change is practically introduced in this stage, techniques for change management too become a necessity. Change management can be viewed as the first step of transformation of a business.

People are the basic resources of any organization that is responsible for driving tasks towards business outcome. Change in work culture and process flows can bring in resistance. Innovative ways to tackle this issue including effective communication, reallocation of work force and providing proper training on the new processes should be carried out in parallel to implementation. Employees should be informed and convinced of the benefits of carrying out an overhaul.

Harnessing dedicated technology and systems to discard irrelevant task cycles and automations can make the business more productive.

Information technology can be leveraged to incorporate systems including software for managing business process, project management and time tracking.

STEP 6: Feedback and Monitoring

Reengineering activities does not end with implementation. Continuous monitoring, assessment, identification of errors and prompting quick action to rectify is needed to make business revamping a success.

Proper governance should be in place and performance indicators need to be modified based on customer preferences and dynamic market conditions.

Collecting feedback both from the employees and end customers is crucial to analyse the result of the reengineering process critically. This helps to improve processes as well as add customer value.

Constant improvement activities will help to sustain the benefits of reengineering and reap maximum revenue.

Conclusion

Business process reengineering brings a radical change to the entire business process and improves business performance substantially by discarding redundant procedures. It motivates employees by incorporating tools and technology to simplify and make task easier. BPR also significantly improves quality and customer value by reducing costs and turn over times.

Data Warehousing Vs Data Mining

Difference between Data Warehousing vs Data Mining

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

Let us understand the Difference between Data Warehousing and Data Mining in detail.

Key Features Data Warehouse

Subject-Oriented: A data warehouse is subject-oriented as it provides knowledge around a subject rather than the organization’s ongoing operations. These subjects include a product, customers, suppliers, sales, revenue, etc. A data warehouse focuses on modeling and analysis of data for decision-making.

Integrated: A data warehouse is constructed by combining data from heterogeneous sources such as relational databases, flat files, etc.

Time-Variant: The data in the data warehouse provides information concerning a particular period.

Non-volatile: Non-volatile means data, once entered into the warehouse, should not change.

Benefits of Data Warehouse

Consistent and quality data

Cost reduction

More timely data access

Improved performance and productivity

Data Mining

Automatic discovery of patterns

Prediction of likely outcomes

Creation of actionable information

Focus on large data sets and databases

Direct marketing: The ability to predict who is most likely to be interested in what products

Fraud detection: Data mining techniques can help discover which insurance claims, cellular phone calls, or credit card purchases are likely to be fraudulent.

Head to Head Comparison Between Data Warehousing vs Data Mining (Infographics)

Below is the Top 4 Comparison Between Data Warehousing and Data Mining:

Key Differences Between Data Warehousing and Data Mining

Data Warehousing is the process of extracting and storing data to allow easier reporting. Whereas Data mining is the use of pattern recognition logic to identify trends within a sample data set, a typical use of data mining is to identify fraud and to flag unusual patterns in behavior. For Example, Credit Card Companies provide you an alert when you are transacting from some other geographical location that you have not used previously. This fraud detection is possible because of data mining.

The main difference between data warehousing and data mining is that data warehousing is the process of compiling and organizing data into one common database. In contrast, data mining is the process of extracting meaningful data from that database. Data mining can only be done once data warehousing is complete.

A data warehouse is a repository to store data.

Data warehousing is merely extracting data from different sources, cleaning it, and storing it in the warehouse. At the same time, data mining aims to examine or explore the data using queries.

A data warehouse is an architecture, whereas data mining is a process that is an outcome of various activities for discovering new patterns.

The data warehouse contains integrated and processed data to perform data mining during planning and decision-making, but data discovered by data mining results in finding patterns that are useful for future predictions.

The data warehouse supports basic statistical analysis. The information retrieved from data mining is helpful in tasks like Market segmentation, customer profiling, credit risk analysis, fraud detection, etc.

Data warehousing is the process of pooling all relevant data together, whereas Data mining is the process of analyzing unknown data patterns.

Data warehouses usually store many months or years of data. This is to support historical analysis. Data mining uses pattern recognition logic to identify trends within a sample data set.

Data Warehousing and Data Mining Comparison Table

Below are the top comparison between Data Warehousing and Data Mining.

Data Warehousing Data Mining

It is a process that is used to integrate data from multiple sources and then combine it into a single database. It is the process that is used to extract useful patterns and relationships from a huge amount of data.

It provides the organization with a mechanism to store huge amounts of data. Data mining techniques are applied to data warehouses to discover useful patterns.

This process must take place before the data mining process because it compiles and organizes data into a common database. This process always takes place after the data warehousing process because it requires compiled data to extract useful patterns.

Engineers solely carry out this process. Business users carry out this process with the help of engineers.

Conclusion

Data warehousing is a process that must occur before any data mining can take place. A data warehouse is the “environment” where a data mining process might take place.