The significance of a company’s data strategy in its process mining decisions is crucial. Data strategy involves planning for managing and using data in an organisation, while process mining uses data to discover, monitor, and improve business processes.
Although these concepts are usually seen as separate, with silos between data and process teams in large organisations, when they collaborate effectively, process mining and data strategy becomes a potent tool for business success.
This article delves into their relationship and emphasises the importance of making decisions about them together to maximize their benefits.
Understanding process mining
When your employees or software robots engage with IT systems like SAP, Salesforce, or Oracle, their activities create a trail of data.
Process mining is an effective methodology that enables organisations to gather this data, providing a clear and objective view of their business processes.
The stages of process mining include:
- Data extraction: Collecting event logs from different sources.
- Preprocessing: Cleaning and formatting the data for analysis.
- Process discovery: Creating a visual representation of the process flow.
- Conformance checking: Comparing the actual process flow with the expected process flow.
- Process enhancement: Optimising the process based on the insights gained from the analysis.
No need to worry, as reputable process mining tools in the market can typically execute these steps with minimal effort.
With analytical insights from process mining, you can pinpoint areas for improvement and streamline operations. It’s an excellent method to enhance your digital transformation initiatives and foster innovation throughout your organisation.
Therefore, a robust data strategy, especially regarding data collection and availability, is essential for successful process mining.
The role of data in modern business strategy
In the past, data was often seen as a byproduct of business activities, carrying little value beyond the initial process. While some subsequent tasks might have required the data, it wasn’t highly regarded.
In contemporary times, data has evolved into a crucial component of numerous business initiatives due to advanced data collection, reporting, analytics, and the sheer volume of data available. It’s now common for application data to be utilised by multiple systems for various purposes.
Despite this transformation, many companies struggle to meet their objectives in capturing, sharing, managing, and analysing corporate data assets. Once an organisation establishes a well-defined data strategy, it can employ process mining to harness its data for optimising its processes.
How is process mining related to a company’s data strategy?
Data serves as the fuel for process mining – it’s just not feasible to conduct process mining without it. However, what is frequently disregarded is the synchronisation of the process mining investment decision with the overarching data strategy.
Data is the fundamental element, the raw material, for process mining. Hence, it’s crucial to acknowledge that even if a company isn’t ready for process mining at present, the data strategy should be capable of accommodating it in the future.
Let’s take an example.
The company’s top management and data team have chosen to implement Data Warehouse X and migrate all ERP data within 2-3 years.
Typically, the selection of a data storage cloud or warehouse is a long-term decision. A sensible approach to the data strategy would involve considering tools or vendors that can integrate with that data warehouse or cloud in the future.
In a modern approach, no data integration is necessary, as applications can operate natively using warehouse resources without relocating the data.
For instance, if the company plans to explore process mining after the ERP data is uploaded to the new data cloud, it’s essential to research and even make preliminary decisions on a vendor that is fully aligned and compliant with their chosen data warehouse.
Real-life example: QPR ProcessAnalyzer and Snowflake
Snowflake Data Cloud’s special design allows for faster, simpler, and more flexible data storage, processing, and analytics compared to traditional data warehouses.
QPR ProcessAnalyzer works seamlessly on any data lake with its in-memory engine, but it uniquely operates natively within the Snowflake Data Cloud. This setup leverages Snowflake’s scalability and unified data policy.
By using QPR ProcessAnalyzer, you tap into Snowflake’s virtually limitless scaling capacity. This empowers you to identify process inefficiencies from billions of data rows almost instantly.
“We connected to Snowflake in 5 minutes and saw results the same day. We’ve never seen anything like it.”