What's BPA? Business Process Analytics, Explained

Companies need to rely on connectivity and data to keep up with the competition. Organizations digitize their processes to reduce costs, enhance the customer experience, and improve efficiencies. In fact, 66% of businesses have automated at least one business process.

However, as businesses adopt more data, tracking digital processes becomes increasingly complex and challenging. How can you know if these efforts are truly saving your organization time and money?

IT can provide a valuable service by streamlining these complex IT systems to improve critical digital processes. Business process analytics can provide leaders with the knowledge and insights they need to direct business processes and improve profits without compromising service. Here is what you need to know about business process analytics, how it can improve your business, and the role technology plays in it.

Understanding business process analytics

Business processes are complex, related activities that help your business accomplish a goal. For example, when a customer places an order to an e-commerce business, there’s a series of steps to ensure the correct product gets delivered on time. Each step is critical to ensuring the correct item arrives at the right time, from order receipt to processing to delivery to follow-up.

One of the best ways to eliminate waste, enhance the customer experience and improve efficiency is by measuring, monitoring, and optimizing these processes. Business process analytics uses tools and data to do just that.

Business process analytics uses data, performance metrics, and statistical methods to monitor and analyze business processes to optimize them. It leverages systematic collection, analysis, and interpretation of process data to:

• Find patterns and trends.
• Fix bottlenecks.
• Ultimately enhance business performance.

Business process analytics vs. business process analysis: what’s the difference?

Although both terms are sometimes used interchangeably, business process analysis and business process analytics serve distinct purposes and operate in slightly different ways:

• Business process analytics uses data, statistical methods, and software tools to identify, track and optimize business processes. The main goal is to get actionable insights to improve business performance.
• Business process analysis is a method that aims to understand, examine, and review processes within a business to identify improvements.

Business process analysis is a more manual, investigative approach to understanding and improving business processes. Business process analytics is a data-driven approach that relies on statistical methods and techniques to gain insights and optimize processes.
(Related reading: business process automation.)

Why business process analytics matters

In an uncertain economic climate, many companies seek ways to continue being profitable. In fact, 91% of CEOs in one survey said they anticipate a recession in the next 12 months. Monitoring and analyzing business processes is a critical way your company can reduce costs and optimize the customer experience to remain strong in the months to come.

Business process analytics offers critical advantages, including:

Improves efficiency

Analyzing processes will enable you to find bottlenecks or inefficiencies that slow down operations. This insight allows you to make targeted improvements, which leads to quicker turnaround times and increased productivity.

Reduces costs

Time may be money in business, but it can be challenging for companies to measure when it comes to processes. However, business process analytics enables companies to estimate how much time each aspect of operations takes and can use it to calculate the costs of specific business processes or customers.

Identifying waste and redundant steps in a process allows your company to eliminate unnecessary activities, ultimately reducing costs. It also improves efficiency, which leads to reduced operational costs.

Enhances customer satisfaction

Streamlining and improving processes will lead to better product quality, faster delivery, and better customer service, all of which increase customer satisfaction.

Informs decision making

Business process analytics gives evidence-based insights to inform leadership’s strategic decisions. Your company can make data-driven decisions aligning with your business goals by understanding exactly how your processes perform.

Offers a competitive advantage

With a business that understands and continuously improves your processes, you can adapt to changes, innovate, and outperform your competitors.

The role of technology in business process analytics

Technology and IT teams are crucial in giving organizations real-time insights into their overall business processes. Some of the critical tools driving business process analytics include:

Artificial intelligence and machine learning

Artificial intelligence (AI) and machine learning (ML) are pivotal in advancing business process analytics in many ways:

• Predictive analysis allows you to forecast future outcomes based on historical data, enhancing your business’s ability to plan and adjust proactively.
• Anomaly detection identifies discrepancies within massive datasets that signal potential process issues.
• Process automation and process mining can dissect event logs to reveal true organizational processes, unveiling inefficiencies or bottlenecks you may not find through manual analysis
• Real-time analysis enables you to continuously monitor processes and respond promptly, unearthing hidden patterns and trends

Additionally, with these tools, AI can help your business customize its processes based on individual customer behavior and interaction, enhancing customer satisfaction and loyalty.

Cloud computing

Data process is more efficient and real-time, critical in today’s fast-paced, data-driven business environment. As most businesses migrate to the cloud, cloud computing has transformed into a key enabler for business process analytics. At its core, cloud computing provides your business with scalable, on-demand computational resources, facilitating the analysis of large data sets without a costly, on-premises infrastructure.

Big data

Big data plays a fundamental role in business process analytics by offering the raw material for analysis. In the digital world of business, companies generate and access large amounts of data from multiple sources, such as social media interactions, customer transactions, log files, sensor data, and more. It can be analyzed to uncover patterns, trends, and correlations that provide valuable insights into business processes.

When leveraged correctly, Big data provides deeper, more detailed insights into your key business operations, enabling your company to find bottlenecks, inefficiencies, and areas for improvements. It also enhances predictive analytics capabilities, allowing your business to forecast future trends and make data-driven decisions.

Predictive analytics

Predictive analytics offers critical insights into potential future events based on historical and current data. It can forecast future trends, outcomes, and behaviors based on statistical models and machine learning algorithms.

You can use predictive analytics to forecast customer demand, predict possible process failures, anticipate operational efficiency trends, or identify process changes' potential effects. Your business can use this information to optimize resource allocation, enhance customer satisfaction, prevent issues before they occur, and generally position your company for better outcomes.

Implementing business process analytics: a step-by-step approach

Implementing business process analytics is a multi-step process that requires careful planning and execution. The typical steps involved are:

Step 1. Identify and define the business process

Start by clearly defining your goal by implementing business process analytics. Align it with your overall business goals, such as:

• Improving operational efficiency.
• Enhancing customer satisfaction.

You don’t need to leverage analytics across all processes immediately. Identify which processes impact your business objective significantly to start. These could be resource-intensive processes or ones that directly affect your customer experience.

Step 2. Collect and prepare data

Gather all data related to your identified processes. Include data on the time taken for each step, costs involved, error rates, and output quality. Ensure you have the right tools and systems in place to collect data accurately and consistently.

Raw data typically requires preparation and cleaning for analysis. This should involve removing outliers, normalizing values, and handling missing data.

(Read about ETL & data normalization.)

Develop and implement the analytical model

Apply statistical and analytical methods to the data to glean insights. This could mean simple descriptive analysis, predictive modeling, or complex machine learning algorithms, depending on your objectives.

Present the analysis in an understandable and accessible way. Consider using visualization tools to create dashboards or reports for a clear picture of process performance and highlight areas of concern. Use these results to make informed decisions and implement necessary process changes.

Monitor and refine the process

Business process analysis is not a one-time process. Continuously monitor process performance and refine your analytics approach based on feedback and results. Regularly update your analytics to reflect changes in business objectives, process modifications, or improvements in analytics techniques.

Harness business process analytics for operational excellence

Business process analytics is a powerful tool to help your organization identify inefficiencies, make data-driven decisions, predict future outcomes, and enhance overall business performance. By leveraging key technologies like AI, ML, big data, and cloud computing, you can gain deeper insights into your company processes, optimize resources, enhance customer satisfaction, and maintain a competitive edge.