The Important Difference Between BI and Process Mining
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Business Intelligence (BI) and Process Mining have a common goal: help business managers make better, more informed, evidence-based decisions. Despite this shared objective, there’s an important distinction between BI and Process Mining that can help organizations understand how, when and why to apply a given tool to a given situation.
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The critical difference between BI and Process Mining is root cause analysis. BI can tell you something went wrong and Process Mining can tell you why it went wrong. The benefit that Process Mining holds over BI is the depth of analysis. Both tools work with KPIs and data at different depths, but where BI monitors and reports, Process Mining reveals and visualizes. In addition to answering the elusive question, ‘why?’, process mining holds sway over BI in terms of independence from expert interpretation and ability to interpret unsystematic data.
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The importance of ‘why?’
‘What’ and ‘when’ are key inputs of data reporting, but it’s the ‘why’ that delivers an even deeper understanding of business health. Take a brokerage firm, for example, which offers home mortgages. Business Intelligence will report website traffic and a number of successful mortgage applications (what) which can be grouped and sorted by time (when), but it lacks the capacity to answer why an increase in approvals occurred in Q2 or why compliance errors decreased last week. Process Mining digs a bit deeper into the data to create interactive process maps revealing risks and inefficiencies in business practices. The brokerage firm can now figure out the root causes of long delays in providing quotes; high prospect drop out paths, inaccurate credit reports or a high number of abandoned calls.
BI requires expert interpretation
To reap the real benefits of each system, professionals are needed at the front and end of each tool. However, the very nature of BI requires expert human analysis to convert reports and information into actionable decisions. The term Business Intelligence was first used by Richard Millar Devens to describe how a 19th-century banker, Sir Henry Furnese, gamed the system to make money off information.
Throughout Holland, Flanders, France, and Germany, he maintained a complete and perfect train of business intelligence. The news of the many battles fought was thus received first by him, and the fall of Namur added to his profits, owing to his early receipt of the news.
Systems have certainly changed since 1865, but whether recorded in handwritten ledgers or automated in advanced ERP systems, data must be interpreted by an expert in order to be converted into intelligence. The initial phase of data collection and reporting must be followed by appropriate action for business intelligence to be fully realized.
Getting a Process Mining initiative off the ground requires finding the right people and defining the right use case, however, a PhD in Data Science isn’t required. Process Mining can be championed by process enthusiasts and achieve business success when coupled with empowerment, proper change management and accountability rooted at the top. The secret weapon of Process Mining is an ability to reveal the cold hard data truth in the form of a comprehensive story, not just scattered bits of data. Process Mining outputs are user-friendly, visually appealing.
BI assumes processes are known
Business Intelligence is concerned with and limited by, an assumption that processes are known and transpire as intended. Murphy’s law, however, is ever lurking at the edge of business dealings, ensuring even the most controlled processes have room for flaws. BI is good at reporting and monitoring KPIs, but its weakness is in the assumption that all is going to plan.
Process Mining, on the other hand, deals with the reality of flawed processes, unexpected causality and a plethora of problems that may occur at any given time to any given pross. Process Mining makes no assumptions about the integrity or intentions of the underlying process and graduates reporting into root cause analysis. From flawed processes which perform in ways unbeknownst to creators, to entirely rouge process steps that remain hidden from user view, Process Mining thrives in revealing the true ‘as is’ process.
The right use case for the right tool
BI does, however, have its place in BPM. BI tools can transform KPI reporting and data monitoring into a competitive advantage. Marketers use BI to monitor website traffic in real time, helping to predict ROI and meet optimization targets. Sales teams use BI to close deals and identify customers who have higher conversion potential, and thus, deserve personalized attention.
Now consider the use case for optimizing a procure to the payment process. Process Mining can unravel disconnected data across multiple systems to stop leakage, breakthrough performance barriers and protect profits. Different business problems require different business solutions. BI is not Process Mining and Process Mining is not BI. Understanding the difference may be key to a thriving technology environment and digital transformation within an organization. To learn more about getting started with Process Mining, reach out to our team of process enthusiasts here at Minit.