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Process mining – more than just process discovery

Six steps you can take to scale adoption of process mining and transform your business operations.

Why process mining matters?

In the ongoing quest to achieve digital transformation, enterprises are increasingly leveraging process mining to discover, monitor, and optimize processes in a data-driven, faster, cost-efficient, and scalable way. This growing technology-driven approach unleashes objective insights based on facts derived from data, and helps organizations make better decisions, replacing manual-intensive (e.g., interview-based) process discovery and analysis methods.

Enterprises have been continuously increasing their focus on digital transformation over the years. The onslaught on traditional/legacy business models by the recent global pandemic and the alarming need to build resilient operations is further driving enterprises to accelerate the shift to become digital-first with automated and intelligent processes.

The need to scale digital transformation and automation initiatives to more complex, end-to-end processes is driving increased demand for holistic Intelligent Process Automation (IPA) solutions that combine various digital levers such as robotic process automation (RPA), Intelligent Document Processing (IDP), conversational AI, machine learning, process mining, and process orchestration.

Process mining is becoming integral to the IPA solution ecosystem given the critical role it plays across enterprises’ automation and digital transformation lifecycles.

Ways to apply process mining

Process mining solutions have a wide range of applications that go far beyond just process discovery to generate fact-based insights and enable enterprises to act on these insights for transforming processes. Graphic 1 below highlights the key application areas of process mining solutions.

key application areas of process mining solutions

Among the many applications for process mining are:

  • Process discovery or digital twins for business operations – discovering and creating a digital representation of as-is business process with assets, activities, people, and IT systems to enable data-driven assessment of transformation opportunities
  • Process conformance – Highlighting deviations in the discovered process versus the input reference model. Root-cause analysis capability helps users identify the causes of detected process anomalies
  • Process standardization – comparing and standardizing processes across teams, units, and geographies. It can identify violations from enterprise-defined standards and enable comparison with industry benchmarks to implement best practices
  • Process optimization – aiding in process streamlining by identifying exceptions (e.g., process loops) and inefficiencies. It helps identify potential pain points and bottlenecks to optimize key process KPIs
  • Automation pipeline fulfillment – offering a scientific way to assess automation potential and prioritize high-value use cases by analyzing step-level information such as volume, costs, and frequency. This significantly reduces the time and effort required to create a healthy automation pipeline
  • Process simulation – helping to generate virtual simulations that empower enterprises to test various improvement scenarios without impacting their day-to-day operations and minimizing the implementation risk
  • Process monitoring – enabling continuous monitoring of processes in near-real-time to predict bottlenecks and potential KPI breaches. This helps enterprises proactively plan for remedies, in addition to creating a feedback loop for continuous improvements
  • Workforce intelligence – unearthing productivity improvement opportunities and deriving insights into employee collaboration that can be used for better resource allocation and delegation of work
  • Action triggers – helping enterprises act on insights by enabling various action triggers such as automatic alerts/notifications in cases of expected breaches, next-best-action recommendations (e.g., resource allocation) to improve process performances, and automation triggers to carry out high severity tasks.

Six key success factors for greater scale and value realization

Process mining offers significant potential for enterprises to transform their business models. However, the low maturity of the enterprise data ecosystem and lack of awareness are key issues that present challenges as enterprises navigate their process mining journey.

To successfully adopt process mining at scale and maximize the benefits of the investment, enterprises should adopt the following key success factors:

1. Secure buy-in from leadership and enterprise IT

Executive backing provides direction to process mining initiatives and helps tie them back to the organization’s overall strategic objectives. Lack of executive sponsorship and stakeholder buy-in due to limited understanding of process mining technology and its potential might lead to gaps in vision and strategic focus to drive enterprise-wide adoption.

Gaining executive support is extremely critical in the initial stages as it helps define a clear vision and mission, creates a detailed strategy and roadmap, establishes the required governance structure, acquires the right talent, and establishes the foundation to build internal capabilities. Educating senior business leaders about the benefits of process mining to get buy-in and putting proactive efforts early-on to align stakeholders from the enterprise IT security and compliance functions is key to laying the essential groundwork for successful adoption.

2. Start with a simple project

Selecting the right processes for proof of concepts (POC) is critical as they demonstrate the enterprise vision, and act as litmus tests to assess the technology’s potential. Organizations could face apprehensions about a new solution’s credibility and effectiveness, and initial results are crucial to demonstrating value to users, in addition to clear ROI to the C-suite.

Organizations should start with a process that is structured, contains a limited number of steps, and requires low data preparation. As enterprises look to scale up adoption, it is important to prioritize processes using a structured and repeatable evaluation framework that looks at aspects such as:

  • Impact potential – considers the cost of the process and business criticality
  • Process mining potential – takes into account factors such as process awareness, complexity, frequency, existing process health, and data availability

Enterprises could also tap into service providers’ expertise and seek best practices for conducting successful pilots.

3. Ensure data availability and quality

Limited availability of event logs data is a key hurdle to scaling adoption, especially in industries with relatively lower penetration of information systems such as enterprise resource planning (ERP) and customer relationship management (CRM). Even if systems are available, enterprises sometimes lack historical data, the data is not available in the right format, or they are reluctant to provide vendors access due to growing data security and privacy concerns. Extracting event logs in the right format especially from non-standard IT systems can be tedious, and transforming data into appropriate formats might require significant IT/coding support.

Educating key stakeholders about the benefit of logging data through information systems and saving them for future use is vital to ensuring data availability. Organizations should focus on leveraging pre-built connectors with leading enterprise applications and Extract, Transform, Load (ETL) capability to transform data into appropriate formats. Enterprises should focus on partnering with enterprise IT early to gain a better understanding of the application landscape, address data privacy concerns, and get access to skills for performing data transformation.

4. Set up a process mining center of excellence

Adoption of process mining in organizational silos and lack of proper governance and collaboration across teams could lead to lower ROI, redundant efforts, and reduced value realization, making it difficult to sustain and scale adoption. A lack of centralized governance might limit an enterprise’s ability to involve relevant process SMEs, navigate compliance requirements, identify the right use cases, define the right metrics/KPIs for tracking process performance, and optimally utilize process mining software licenses across the enterprise.

Establishing a dedicated process mining center of excellence (CoE) helps clearly define desired outcomes and combine efforts across process excellence, automation, and digital transformation initiatives to resonate with the needs of the broader organization. It provides a strong centralized structure and governance framework for developing a shared vision and strategic alignment between the IT and business stakeholders. CoEs equip the enterprise with a forum to share skills, talent, assets, and discuss challenges and best practices. This increases the agility and speed of process mining implementation in new operational areas.

5. Identify and source relevant skills/expertise

Process mining requires a multi-disciplinary team of people from various areas such as project governance, data specialists, solution architects, business analysts, and domain experts, for successful implementation. Shortage of skills, difficulty in acquiring/retaining relevant talent, and high training costs to develop in-house skills impede progress.

Enterprises could consider leveraging service providers’ expertise/experience to plug skill gaps and jump-start the program. Collaborating with process mining vendors or service providers to train employees and develop role-based courses for the key positions can fast-track the development of in-house skills. Leveraging in-house experts to help employees develop an analyst mindset also can play a significant role in scaling adoption.

6. Focus on change management

Resistance from individual employees to adopt process mining due to apprehensions around increased process/operational transparency and visibility into employees’ ways of working can pose a threat to scaling adoption across any organization.

Enterprises should focus on aligning with, and educating frontline managers, who become change agents to evangelize the technology and spread awareness about its applications and benefits. Proactively disseminating success stories and showcasing the benefits of process mining to impacted employees can help get their buy-in, create interest, and address concerns related to increased transparency. Developing an organizational culture that embraces innovation and building a community of interested employees through initiatives such as events and workshops can go a long way in effectively managing change.

Process mining’s huge potential

Process mining offers great potential for enterprises to divulge a company’s process DNA and drive continuous improvement. Enterprises need to understand process mining goes well beyond the first step of discovery and realize it can play a vital role in building future-proof operations.

With the snowballing need to shift to digital at break-neck speed, adopting process mining at scale can help enterprises accelerate their automation and transformation initiatives and realize greater ROI and business value.Download factsheet

Amardeep Modi - Vice President at Everest Group

Amardeep Modi is a Vice President at Everest Group and assists clients on topics related to technology areas across the intelligent automation ecosystem, including RPA, AI, process mining, and process orchestration. Amar helped establish Everest Group’s Service Optimization Technologies (SOT) practice, and his responsibilities include leading SOT syndicated and custom research offerings globally.

Harpreet Makan - Practice Director at Everest Group

Harpreet Makan is a Practice Director at Everest Group and a member of the Service Optimization Technologies (SOT) team. She assists clients on topics related to technologies that are disrupting the way global services are delivered, including RPA, AI, and process mining.

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