Process analytics are becoming increasingly important, especially for those seeking to support data-driven organizational decision-making.

Keep reading to discover process analytics’ benefits and challenges. You will also find a simple framework with steps to help you use it for decision-making.

What is Process Analytics?

Process analytics involves using data to understand, monitor, and improve business processes. Typically, data is collected through process mining or process discovery, which documents how business processes are completed.

Then, data mining, statistical analysis, and predictive modeling can analyze how well different processes work in a company. For example, Hollard Insurance used to complete manual process mapping. This required heavy manual effort and hindered their ability to identify additional areas to streamline their processes.

They now use process mining to view their operational data in real-time. They also use process analytics to find and fix bottlenecks or inefficiencies quickly. Using KYP.ai, they could automate much of their process, boosting their overall productivity by 20%.

By leveraging these insights, businesses can make informed decisions that drive efficiency and effectiveness.

Building the Data Backbone to Support Process Analytics

Before diving into process analytics, you must collect data on process completion. Organizations can learn more about their business processes by capturing workforce, technology, and technology patterns. Most organizations want to fix their process inefficiencies. However, they often lack the time or resources for process documentation.

Deloitte shares, “Increasing digitization of processes is generating a gold mine of data. Smarter algorithms are leveraging this to provide insights. And faster computational capabilities make it possible to process the vast amounts of data, paving the way for the creation of a digital twin of your organization.”

Data capture is critical to ensuring the highest value from process analytics. Then, data is analyzed using descriptive analytics, typically powered by AI. This data helps organizations to understand how tasks are done, find where things get stuck, and see what we can do better.

For instance, capturing end-to-end invoice processing patterns allows organizations to optimize software deployment, enhance employee productivity, and reduce operational costs. Similarly, process analytics can help streamline workflows, improve resource allocation, and boost overall performance.

The Significance of Process Analytics in Decision-Making

In today’s data-driven world, making decisions based on intuition or gut feeling is no longer sufficient. Process analytics is crucial in providing objective, data-backed insights that guide decision-making. Here are some key reasons why process analytics is essential:

Enhanced Efficiency and Productivity

Analyzing data related to various processes can help organizations identify inefficiencies and bottlenecks. This allows them to make better decisions, use resources effectively, and be more productive. If something keeps causing delays, process analytics can figure out why and suggest ways to fix it.

Improved Customer Experience

Understanding customer behavior and preferences is vital for delivering exceptional customer experiences. Process analytics helps companies examine how customers interact in different ways. It helps to find problems, and make data-based decisions to improve customer sentiment. Companies can make optimized decisions to enhance their products, customer service, and employee engagement.

Cost Reduction and Resource Optimization

Process analytics helps companies find ways to spend less money and better use their resources.

Organizations can analyze data on resource use. This helps them make optimized decisions that cut waste, manage inventory, and lower costs. For instance, if specific resources are underutilized, process analytics can highlight this inefficiency and suggest ways to reallocate resources effectively.

Implementing Process Analytics: A Step-by-Step Guide

To implement process analytics, consider a framework, with seven steps that can be used for optimized decision-making processes:

1. Define Objectives and Key Metrics

Start by defining your goal and identifying the key metrics you want to measure. Solutions like KYP.ai can help you find your baseline performance. This gives you an easy way to build your business case. Clearly articulate your aim and the specific processes you want to analyze. To improve customer service, focus on response time, solving issues, and customer satisfaction scores.

2. Collect Relevant Data

To perform process analytics, you need access to relevant data. Identify what systems, teams, and processes should be in your scope. This could include transactional data, customer feedback, system logs, and employee performance data. Ensure that the data is accurate, complete, and up to date.

Tip: Tools like KYP.ai help capture data that typically goes unseen in task and processing mining tools. Learn more about how to get the data you need to improve productivity.

3. Clean and Prepare the Data

Once you have collected the data, cleaning and preparing it for analysis is crucial. This involves removing duplicates, handling missing values, and ensuring data consistency. Data preprocessing is a critical step to ensure the accuracy and reliability of your analysis.

4. Apply Analytical Techniques

With ready-to-use, structured data, you can apply various analytical techniques to gain insights. This could include descriptive analytics to understand past trends. It may also involve diagnostic analytics to find the root causes of problems. Or, it can use predictive analytics to forecast future outcomes. Decide what methods to use based on your goals and the type of information you have. There are many available solutions to support your analysis. For example, KYP.ai provides on-demand productivity intelligence with robust insights, recommendations, and GenAI.

5. Visualize the Results

Data visualization plays a vital role in process analytics. Use charts, graphs, and dashboards to present the analysis results in a visually appealing and easy-to-understand manner. Ensure that your descriptive analytics tool can be continuously updated with real-time data. Data latency or relying on other teams to manually configure your data is inefficient. Visualizations help stakeholders grasp the insights quickly and make informed decisions.

For instance, a heat map can illuminate regions of intense activity or performance slowdowns in a procedure. It can also show process redundancies, like copying and pasting. This can lead to automation improvements, leveraging Robotic Process Automation (RPA) or Intelligent Document Processing (IDP). The latest innovations help you find the exact cost savings through process mining. This is important before you commit to an automation layer. You need to understand its impact first, which historically was a missing component in RPA adoption.

6. Interpret and Communicate Insights

Once you have identified potential impact through process optimization or streamlining, deciding what to implement is essential. Communicate more effectively with key stakeholders using a conversational GenAI solution like KYP.ai Concierge that lets you talk with your data. You can ask questions and support your decisions with data. Build extra business cases whenever you need to. You have access to all your data, which is cleaned and ready to use.

These practical suggestions stem from the analysis and identify their probable influence on future impact. GenAI also provides narrative methods along with hard data so that you have additional support to back your recommendations.

KYP.ai’s automated data capture and display features give you ongoing insights. This helps you focus on and report progress on meaningful goals. These pre-built dashboards let you monitor progress all the time. You can find areas that need improvement and see how your changes affect results in real time.

7. Monitor and Iterate

Process analytics is an ongoing process. Continuously monitor your processes’ performance and track the impact of your implemented changes. Iterate and refine your analytics approach based on new data and evolving business needs. Regularly revisit your objectives and key metrics to ensure they align with your business goals.

Real-World Examples of Process Analytics

Example 1: Banking and Finance

Process analytics aid in enhancing production and increasing cross-team collaboration and effectiveness. An international finance team deployed KYP.ai to solve its purchase-to-pay (P2P) process challenges. The P2P process was crucial for its financial operations but was lengthy and riddled with inefficiencies. Procurement then had to explain late payments and maintain strong supplier relationships.  

Using AI-powered discovery, KYP.ai identified manual interventions and non-standardized practices as the primary delay causes. Knowing this, the company deployed an automated invoice matching and approval protocol that was standardized for all P2P transactions. These changes cut the average payment processing time by half and enhanced transaction accuracy.  

Consequently, the corporation improved its supplier relationship, benefited from early payment discounts, and improved its margins. This case study shows how process analytics can significantly improve a business’s financial processes. 

Example 2: Healthcare Sector

Process analytics has significant applications in the healthcare sector. The US Surgeon General’s office reports that nurses spend 41% of their time on electronic health records and paperwork. The harsh reality from the Surgeon General’s advisory is that healthcare workout burnout is on the rise.

Hospitals can use process analytics to evaluate patient flow, pinpoint bottlenecks, and improve resource distribution. For example, hospitals can make better choices by using data on patient check-ins, discharge times, and staff availability. This can help reduce waiting times and improve patient care. Automation such as Optical Character Recognition (OCR) or Intelligent Document Processing (IDP) could drastically reduce the manual effort nurses currently pour into documentation.

Example 3: Compliance and Risk Management in Insurance

Process analytics assists institutions in ensuring compliance with regulatory requirements and managing risks effectively. Insurance companies can find compliance gaps by looking at data on regulatory changes, transaction monitoring, and risk exposure. They can also assess potential risks and take steps to reduce them. For example, insurance companies can use predictive modeling and scenario analysis. This helps them expect regulatory changes and adjust their processes. It also ensures they follow strict compliance standards. This proactive risk management approach minimizes regulatory fines, reputational damage, and operational disruptions.

Process analytics in insurance helps companies use real-time data more effectively. This gives a clearer view of risks and opportunities. It also improves customer experiences, streamlines operations, and ensures compliance with regulations. Organizations can boost innovation using data analytics, advanced technologies, and predictive modeling. They can also reduce risks and provide valuable services to customers in a fast-changing industry.

Conclusion

Process analytics is a powerful tool that can revolutionize decision-making within organizations. Using data to understand and improve business processes helps organizations enhance efficiency, improve customer experiences, and reduce costs.

Implementing process analytics involves defining objectives, collecting and preparing data, applying analytical techniques, visualizing results, interpreting insights, and continuously monitoring performance. By following these steps, organizations can use process analytics effectively and make data-backed decisions that lead to success.