Are you thinking BIG enough? Over the past few years, the quality of discussion regarding a ‘process’ and its interfaces between different departments has developed radically. Organizations increasingly reject guesswork, individual assessments, or blame-shifting and instead focus on objective facts: the display of throughput times, process variants, and their optimization.
But while data can hold valuable insights into business, users, customer bases, and markets, companies are sometimes unsure how best to analyze and harness their data. In fact, the problem isn’t usually a lack of data; it’s a breakdown in leveraging useful data. Being unsure how to interpret, explore, and analyze processes can paralyze any go-live, leading to a failure in the efficient interaction of processes and business operations. Without robust data analysis, your business could be losing money, talent, and even clients.
After all, analyzing processes is about letting data tell its true story for improved understanding.
The “as-is” processes
Analyzing the as-is current state helps organizations document, track, and optimize processes for better performance, greater efficiency, and improved outcomes. By contextualizing data, we gain the ability to navigate and organize processes to negate bottlenecks, set business preferences, and plan an optimized route through process mining initiatives. This focus can help across an entire organization, or on one or more specific processes or trends within a department or team.
There are several vital goals/motivations for implementing current state analysis, including:
- Saving money and improving ROI;
- Improving existing processes or creating new processes;
- Increasing customer satisfaction and journeys;
- Improving business coordination and organizational responsiveness;
- Complying with new regulatory standards;
- Adapting methods following a merger or acquisition.
The “to-be” processes
Simply put, if as-is maps where your processes are, to-be maps where you want them to… be. To-be process mapping documents what you want the process to look like, and by using the as-is diagram, you can work with stakeholders to identify developments and improvements of the current process, then outline those changes on your to-be roadmap.
This analysis can help you make optimal decisions for your business and innovative OpEx imperatives. For instance, at leading data companies like Google and Amazon, data is used in such a way that the analysis results make the decisions! Just think of the power Recommendation Engines, PageRank, and Demand Forecasting Systems have over the content we see. To achieve this, advanced techniques of machine learning and statistical modeling are applied, resulting in mechanically improved results from the data. Interestingly, because these techniques reference large-scale data sets and reflect analysis and results in real-time, they are applied to areas that extend beyond human decision-making.
Also, by analyzing and continuously monitoring qualitative and quantitative data, we gain insights across potential risks and ongoing improvement opportunities, too. The powerful combination of process discovery, process analysis, and conformance checking supports a collaborative approach to process improvement, giving you game-changing insights into your business. For example:
- Which incidents would I like to detect and act upon proactively?
- Where would task prioritization help improve overall performance?
- Where do I know that increased transparency would help the company?
- How can I utilize processes in place of gut feeling/experience?
Further, as the economic environment continues to change rapidly, and modern organizations keep adopting process-based approaches to ensure they are achieving their business goals, process analysis naturally becomes the perfect template for any company.
With this, process mining technology can help modern businesses manage process challenges beyond the boundaries of implementation. We can evaluate the proof of concept (PoC) for any proposed improvements, and extract relevant information from a homogenous data set. Of course, process modeling and business process management (BPM) are available to solve the potentially tricky integration phase.
Process mining and analysis initiatives
Process mining and discovery initiatives can also provide critical insights throughout the automation and any Robotic Process Automation (RPA) journey, from defining the strategy to continuous improvement and innovation. Data-based process mining can even extend process analysis across teams and individuals, decreasing incident resolution times, and subsequently improving working habits via the discovery and validation of automation opportunities.
A further example of where process mining and strategic process analysis/alignment is already paying dividends is IT incident management. Here, “incident” is an unplanned interruption to an IT service, which may be complete unavailability or merely a reduction in quality. The goal of the incident management process is to restore regular service operation as quickly as possible and to minimize the impact on business operations. Incident management is a critical process in Information Technology Library (ITIL).
Process mining can also further drive improvement in as-is incident management processes as well as exceptional and unwanted process steps, by increasing visibility and transparency across IT processes. Process mining will swiftly analyze the different working habits across teams and individuals, decreasing incident resolution times, and subsequently improving customer impact cases.
Positive and practical experiences with process mining across industries have also led to the further dynamic development of tools, use cases, and the end-user community. Even with very experienced process owners, the visualization of processes can skyrocket improvement via new ideas and discussion.
However, the potential performance gains are more extensive, with the benefits of using process mining for incident management, also including:
- Finding out how escalation rules are working and how the escalation is done;
- Calculating incident management KPIs, including SLA (%);
- Discovering root causes for process problems;
- Understanding the effect of the opening interface (email, web form, phone, etc.);
- Calculating the cost of the incident process;
- Aligning the incident management system with your incident management process.
Robotic Process Automation (RPA)
Robotic process automation (RPA) provides a virtual workforce to automatize manual, repetitive, and error-prone tasks. However, successful process automation requires specific knowledge about the intended (and potential) benefits, effective training of the robots, and continuous monitoring of their performance and processes.
With this, process mining supports organizations throughout the lifecycle of RPA initiatives by monitoring and benchmarking robots to ensure sustainable benefits. These insights are especially valuable for process miners and managers with a particular interest in process automation. By unlocking the experiences with process mining, a company better understands what is needed today, for tomorrow’s process initiatives.
To further upgrade the impact of robot-led automation, there is also a need for a solid understanding of legacy systems, and an overview of automation opportunities. Process mining tools provide key insights throughout the entire RPA journey, from defining the strategy to continuous improvement and innovation.
Benefits of process mining and analysis within the RPA lifecycle include:
- Overviews of processes within the company, based on specific criteria;
- Identification of processes suitable for RPA implementation during the preparation phase;
- Mining the optimal process flow/process path;
- Understanding the extent to which RPA can be implemented in legacy processes and systems;
- Monitoring and analysis of RPA performance during the transition/handover of customization;
- Monitoring and continuous improvement of RPA in the post-implementation phase.
The process of better business understanding
Every organization is different and brings with it a variety of process-related questions. Yet some patterns are usually repeated. For example, customers who introduce data supported process analysis as part of business transformation initiatives will typically face challenges in harmonizing processes from fragmented sectors and regional locations. Here it helps enormously to base actions on data and statistics from the respective processes, instead of relying on the instincts and estimations of individuals.
With this, process analysis which is supported by data, enables a fact-based discussion, and builds a bridge between employees, process experts and management. This helps avoid siloed thinking, as well as allowing the transparent design of handovers and process steps which cross departmental boundaries within an organization.
In other words, to unlock future success and transformation, we must be processing… today.
Find out more about process mining with Signavio Process Intelligence, and see how it can help your organization uncover the hidden value of process, generate fresh ideas, and save time and money.
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