A crucial part of any organisation’s plan is its data strategy. This entails outlining goals related to data, creating rules for managing data, and pinpointing the necessary resources. Data strategy also encompasses establishing policies for data access, security, and privacy. Organisations that excel in data strategy are more adept at leveraging data to achieve business results.

Data strategy involves several components, including:

  • Quality and availability of data 
  • Collection and storage of data 
  • Data sources
  • Data governance and security 
  • Data visualisation, sharing, and integration

In this part, we will explore these elements, along with two more: picking your process mining vendor and ensuring your chosen process mining tool can scale effectively.

Data quality and availability 

A company’s data strategy significantly impacts process mining by influencing the quality and accessibility of the required data.

A key challenge in implementing process mining is ensuring the quality and availability of data. It is crucial for companies to focus on collecting and storing data in a structured and accessible way, ensuring accuracy, completeness, and relevance to the analysed processes. This approach guarantees that process mining generates precise and valuable insights.

Data quality is especially vital in the realm of process mining. High-quality data makes it easier to pinpoint areas for process optimisation, leading to notable enhancements in efficiency and effectiveness.

When we mention high-quality data, we’re not referring to specific process details (e.g., the time an invoice was created), but rather to metadata or attribution issues, such as duplicate records for the same customer or multiple product numbers for the same product.

Conversely, inaccurate or incomplete data can result in flawed process mining insights, leading to suboptimal decision-making. Therefore, companies must prioritise data quality in their data strategy for accurate and meaningful insights.

Data governance and security 

A company’s data strategy plays a role in governing and securing the data used in process mining. According to a Gartner survey, data security and privacy are primary concerns for organisations adopting process mining.

To mitigate risks, companies should include data access and ownership guidelines in their data strategy, preventing unauthorised access or data breaches. Additionally, the data strategy should outline protection measures like encryption and firewalls, ensuring secure and reliable data for process mining insights.

Process mining involves critical business data, such as customer information, pricing, products, and operational details. The modern approach to process mining, where data isn’t copied to a new system, accelerates time to value by leveraging existing security measures rather than reinventing them.

Data access and sharing 

A company’s data strategy dictates how process mining findings are accessed, shared, and utilised internally.

Hence, the data strategy should include guidelines for data visualisation, sharing, and integration to seamlessly incorporate process mining insights into the company’s decision-making processes. This ensures that companies can bring about significant improvements and optimisations across their operations.

Data integration, AI and machine learning

Furthermore, a company’s data strategy should account for the integration of diverse data sources into the process mining approach. Process mining relies on data from various sources like enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and other operational data.

By integrating data from as many sources as possible, companies can obtain a more comprehensive view and deeper insights into their processes, allowing them to pinpoint the root causes of inefficiencies or bottlenecks.

Moreover, a company’s data strategy should explore the incorporation of artificial intelligence (AI) and machine learning (ML) techniques alongside process mining. These technologies enhance process mining by enabling predictive and prescriptive analytics, anticipating issues, and recommending optimal solutions. Currently, 49% of business leaders utilise AI and ML in conjunction with process mining. Leveraging these technologies enhances the accuracy and speed of process mining insights, leading to more effective process optimisation.

Scalability of the chosen process mining approach

A company’s data strategy should also consider the scalability of the process mining approach. The volume of data generated by organisations is rapidly increasing. It is projected that by 2025, the daily global data generation will reach 463 exabytes, a substantial increase from 12.5 exabytes in 2015.

Hence, companies need a scalable data strategy that accommodates rising data volumes and meets the expanding requirements of process mining techniques. The company should possess the capability to process and analyse the substantial volumes of data generated by its operations. A well-designed data architecture, with scalable infrastructure and data processing capabilities, can significantly enhance the effectiveness of process mining.

Selecting a process mining tool or vendor 

A company’s data strategy greatly shapes process mining decisions by influencing the choice of tools and techniques. The compatibility of process mining tools with the company’s data sources is the paramount factor during selection.

Hence, the company’s data strategy should offer guidelines on data security, management practices, and storage technologies that align with process mining tools. This enables companies to efficiently analyse and optimise their processes by choosing the most suitable process mining vendor for their specific needs.

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