The significance of a company’s data strategy in the context of its process mining choices cannot be emphasized enough.
Data strategy entails the blueprint for handling and leveraging data within an organization, whereas process mining employs data for uncovering, overseeing, and refining business processes.
These concepts are frequently treated as distinct entities, fostering silos between data and process teams within large organizations. Nonetheless, their collaborative synergy yields a potent instrument for business triumph.
In this piece, we will delve into the interconnection between these two realms and elucidate why joint decision-making optimally harnesses their advantages.
Understanding process mining
As your employees or software robots engage with IT systems like SAP, Salesforce, or Oracle, their actions generate a trail of data.
Process mining is a robust approach enabling organizations to harness this data, providing an unambiguous and unbiased perspective on their business processes.
The process mining journey encompasses several phases, including:
1.
Data extraction:
Collecting event logs from different sources.
2.
Preprocessing:
Cleaning and formatting the data for analysis.
3.
Process discovery:
Creating a visual representation of the process flow.
4.
Conformance checking:
Comparing the actual process flow with the expected process flow.
5.
Process enhancement:
Optimizing the process based on the insights gained from the analysis.
No need to worry, though. The majority of reputable process mining tools available today are designed to execute these steps with minimal hassle.
By harnessing the analytical insights obtained through process mining, you can pinpoint areas that could use enhancement and fine-tune your procedures. Moreover, it serves as an excellent approach to enhance your digital transformation endeavors and foster innovation throughout your entire organization.
Hence, it’s only logical that establishing a robust data strategy, especially concerning data collection and accessibility, becomes pivotal for accomplishing triumphant process mining.
The role of data in modern business strategy
The Significance of Data in Contemporary Business Strategy
In previous times, data was often an incidental outcome of business operations, holding limited value beyond the initial activity or task. While certain subsequent actions might have necessitated the utilization of this data, its significance was minimal.
In the present era, however, data has transformed into a crucial element driving numerous novel business endeavors, courtesy of advanced data collection, reporting, analytics, and the sheer volume of available data. Sharing application data across multiple systems for varying purposes has become commonplace.
Despite this shift, numerous enterprises continue to struggle in realizing their objectives pertaining to capturing, sharing, managing, and analyzing their corporate data assets.
Once an organization establishes a well-defined data strategy, it can harness the potential of process mining to optimize its operations.
How is process mining related to a company’s data strategy?
Data serves as the lifeblood of process mining – the very practice is inconceivable without it.
However, what often goes unnoticed is the alignment of the decision to invest in process mining with the overarching data strategy.
Data stands as the fundamental ingredient, the raw material, for process mining. Thus, it’s crucial to contemplate that even if a company isn’t currently prepared for process mining, its data strategy should be adaptable to accommodate such endeavors in the future.
Let’s delve into an illustration.
Consider a scenario where a company’s upper management and data team opt to implement Data Warehouse X and migrate all ERP data within a span of 2-3 years.
Typically, the selection of a data storage cloud or warehouse is a long-term commitment. Consequently, a prudent approach within the data strategy would involve evaluating the tools or vendors that could seamlessly integrate with the chosen data warehouse or cloud in the future.
In a contemporary approach, no data integration is necessary, as applications can natively operate using warehouse resources, without the need to relocate the data.
For instance, let’s assume the company intends to explore process mining once the ERP data is uploaded and streaming to the new data cloud. In this scenario, it becomes imperative to conduct research and potentially even make preliminary decisions regarding which vendor aligns fully and complies with their selected data warehouse.
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Components of data strategy and how they relate to process mining
In any organization’s strategy, data strategy is pivotal. It defines data goals, management rules, and needed resources. Privacy, security, and access policies are part of it. Successful data strategy empowers businesses with data-driven outcomes.
Data strategy involves several components, including:
Quality and availability of data
Collection and storage of data
Data sources
Data governance and security
Data visualization, sharing, and integration
In this section, we’ll look at these components, along with two others: the selection of your process mining vendor and the scalability of your chosen process mining tool.
Data quality and availability
One of the most significant ways a company’s data strategy affects process mining is by determining the quality and availability of data required for this technique.
One of the most significant challenges organizations face when implementing process mining is the quality and availability of data. Therefore, companies should prioritize collecting and storing data in a structured and accessible manner. In addition, the data should be accurate, complete, and relevant to the processes you’re analyzing. By doing so, companies can ensure that process mining yields accurate and meaningful insights.
Data quality is especially critical when it comes to process mining. When a company’s data is high quality, it becomes easier to identify areas where processes can be optimized, leading to significant improvements in efficiency and effectiveness.
When we say high-quality, however, we’re not talking about the process data that process mining data runs on (e.g. an invoice was created at 3:57 pm on Friday), but more so metadata or attribution issues, like having duplicate records for the same customer, or several product numbers listed for the same product.
Conversely, inaccurate or incomplete data can lead to incorrect process mining insights, resulting in suboptimal decision-making. Therefore, companies must ensure their data strategy prioritizes data quality for accurate and meaningful insights.
Data governance and security
A company’s data strategy also affects the governance and security measures put in place for the data used in process mining. According to a survey conducted by Gartner, data security and privacy are top concerns for organizations implementing process mining.
Companies must ensure that their data strategy also includes data access and ownership guidelines to prevent unauthorized access or data breaches. A company’s data strategy should also contain guidelines for data protection, such as encryption and firewalls, to ensure that their process mining insights are based on secure and trustworthy data.
Process mining typically consumes highly critical business data: customers, prices, products, and operational details. The modern approach to process mining (where data is not copied to a new system) significantly accelerates time to value as data security and governance don’t need to be reinvented, but the existing security can be leveraged.
Data access and sharing
A company’s data strategy determines how process mining results are accessed, shared, and used within the company.
Therefore, the company’s data strategy should provide data visualization, sharing, and integration guidelines to ensure that process mining insights are integrated into the company’s decision-making processes. By doing so, companies can drive meaningful change and optimization across their processes.
Data integration, AI, and machine learning
Moreover, a company’s data strategy should also consider integrating different data sources into the process mining approach. Process mining relies on data from various sources, including enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and other operational data.
By integrating data from these as many sources as possible, companies can gain a more holistic view and better insights into their processes, and identify the root cause of inefficiencies or bottlenecks.
Additionally, a company’s data strategy should consider the use of artificial intelligence (AI) and machine learning (ML) techniques in conjunction with process mining. AI and ML can augment process mining techniques by enabling predictive and prescriptive analytics that can anticipate issues and recommend optimal solutions.
In of business leaders currently use AI and ML in conjunction with process mining. By leveraging these technologies, companies can enhance the accuracy and speed of their process mining insights, resulting in more effective optimization of their processes.
Scalability of the chosen process mining approach
A company’s data strategy should also take into account the scalability of the process mining approach. The amount of data generated by an organization is growing at an exponential rate. By 2025, it is estimated that the total data generated daily will be 463 exabytes globally, up from 12.5 exabytes in 2015.
Therefore, companies should have a scalable data strategy that allows for increased data volumes and can support the growing needs of process mining techniques. The company should also have the capability to process and analyze the large volumes of data generated by its operations. A well-designed data architecture with scalable infrastructure and data processing capabilities can significantly increase the effectiveness of process mining.
Selecting a process mining tool or vendor
Another significant way a company’s data strategy influences process mining decisions is by determining the selection of process mining tools and techniques used. When selecting process mining tools, compatibility with the data sources used by the company is the most critical factor.
Therefore, a company’s data strategy should provide guidelines on data security, data management practices, and data storage technologies supporting process mining tools. As a result, companies can more effectively analyze and optimize their processes by selecting the most suitable process mining vendor for their needs.
Conclusion
In conclusion, a company’s data strategy is critical in determining the effectiveness of its process mining decisions.
A well-defined data strategy that prioritizes high-quality data, scalable infrastructure, data source integration, and AI and ML techniques can significantly improve the accuracy and speed of process mining insights, and the collaboration between data and process teams within your organization.
By following a sound data strategy, companies can achieve significant operational improvements and drive meaningful change through process mining.
To learn more about how process mining can help your business achieve process excellence, download the business leader’s guide to process mining today.
