What is process analytics?
Data powers the business world. Without data, it would be difficult to know whether our ways of working are as efficient as they could be. Best practices emerge as best practices because we measure them and discover they’re faster, generate less waste, and generally yield better results. To discover those best practices, many organizations use business process analytics tools.
Business process analytics is a term used to describe the practice of gathering, analyzing, and utilizing business data to continuously improve processes. That data (which could include financials, units of inventory, worker productivity, and more) is then used to adjust existing processes or introduce new ones. This is how business process analytics supports continuous improvement.
Why is business process analysis important?
Without business process analysis, understanding and improving key business workflows would be challenging. It would be like using a map to navigate without any idea of your current position: in order to improve, an organization needs a way to determine the current state of its processes.
Process analytics is a business tool that helps organizations understand what’s working, what’s not working, and whether the changes they introduce support their goals for improvement in real time. If understanding your processes is a map, then process analytics is the compass that shows you where you are and where you need to move. It’s a data-driven approach to success.
Organizations use process intelligence to accomplish several important business goals:
- Improve workflow efficiency by locating and resolving pain points in business processes.
- Reduce costs with more efficiency workflows that use less time and fewer resources.
- Make better decisions with access to detailed data about current business performance.
- Create better customer experiences by delivering faster, high-quality products and services.
- Gain a competitive advantage in their industry by committing to continuous improvement.
What is the difference between data analytics and process analytics?
Think of data analytics as an umbrella: process analytics falls beneath it. Data analytics is a term that refers to a wide body of strategies, methodologies, and tools used to glean insights from information – business or non-business. Process analytics is a subset of data analytics and involves close examination of individual process performance in support of a particular goal.
In business, process analytics helps organizations know whether their ways of working are efficient. It’s a data-focused, systematic approach to improvement that emphasizes improving processes. Analyzing processes can yield valuable insights into what techniques and strategies are most effective, which are least effective, where waste is generated, where communication breaks down, and where errors are most likely to occur.
How can process analytics be useful?
Process improvement
Process analytics plays an important role in process improvement: a business approach that emphasizes the continuous studying, experimenting, and improvement of existing processes.
Process improvement’s ultimate goal is process optimization: a state of being where all business processes perform optimally. However, because businesses are always changing and growing, process optimization is somewhat ephemeral; before long, ways of working will need to be adjusted again, and then businesses will look to process analytics to identify the processes, analyze them, and implement new improvements.
Process automation
In a data-driven world, process automation tools are essential supports for process analytics initiatives. Process automation can improve process efficiencies, and when coupled with process analytics, yields key insights into performance metrics, identifying trends, pain points, bottlenecks, and other useful data points.
Not only that, process automation tools can be used to create totally new processes built on the foundation of those data findings. Many platforms allow users to visualize and monitor the processes while continuously gathering new data.
Process integration
Integrating new processes into existing workflows comes with its own set of challenges. These introductions are rarely seamless, and understanding how one process impacts another – or indeed, the entire system – is one of the reasons process analytics is so important.
Process analytics supports process integration by visualizing the flow of information between workflows, processes, and systems. Complex processes are difficult to map without data, so having an analytics tool on hand to help monitor, study, and test performance is extremely useful for businesses focused on improving the way processes flow.
Technology of process analytics
Descriptive analytics
Descriptive analytics is an approach to information that focuses on reading, understanding, and extrapolating from historical data. It looks to the past to help understand the present – to uncover how prior decisions affected current outcomes. Data collected from previous years may be studied for trends, relationships, patterns, and more.
Predictive analytics
If descriptive analytics looks to the past, predictive analytics looks to the future. It uses the same historical data, as well as machine learning, to make informed guesses about a business’s performance in the future – from months to quarters to years down the road. Predictive analytics usually involves data modeling to illustrate these forecasts.
Prescriptive analytics
Now that we’ve visited the past and the future, let’s bring the focus back to the present. Prescriptive analytics is an approach to business decision-making that uses valuable insights from both descriptive and predictive analytics to weigh options and model outcomes and provide recommendations for changes to make today. The goal is to understand the best options available and what their impact may be in the future.
Big data
The world is full of data that’s growing at a faster rate all the time. When a data set becomes too large and complex to be understood by traditional data processing tools, it becomes big data. Process analytics and big data are connected in both directions: big data can only be understood by process analytics tools, and process analytics tools glean more insights from big data.
Cloud computing
Cloud computing gives businesses ready access to data, calculations, and tools without the need to store or own it themselves. Because process analytics tools often work with large datasets, cloud computing has become a useful means of accessing that data, as well as providing an environment in which to scale analytics as the business changes and grows.
Business process analytics from Nintex
What insights and efficiencies could you be gleaning from your business processes? To keep a competitive advantage in today’s data-powered world, organizations must take advantage of business process analytics tools that are user-friendly, scalable, and support continuous process improvement.
Nintex’s process platform can help you identify, map, and understand your workflows more deeply. Contact us to schedule a free demo and learn more about our analytics solutions.
