Smoothing business processes with data flows

Markus Müller at Boomi explores how businesses can avoid data silos and opt for data flows instead

 

Many companies struggle with delays and error messages caused by siloed data systems. Customer data, transaction records, or operational metrics are often located on disconnected platforms, forcing teams to integrate data manually. This is not only error-prone but also extremely time-consuming.

 

 

What is data liquidity?

Effective data liquidity eliminates these bottlenecks by enabling seamless data integration and ensuring the company has access to consistent and reliable data. Liquid data refers to how easily data can be used without additional preparation. Fully liquid data is ready to use, for example, inventory data in a connected e-commerce system that can automatically update stock levels.

 

In contrast, sales data stored in spreadsheets, which must be manually cleaned and formatted before analysis, is not considered liquid because it requires additional work before it is useful.

 

When data is accessible and available as ready-to-use datasets, companies can change their strategies or solve challenges without spending additional time on preparation or processing. They can also respond more quickly by avoiding delays caused by incompatible formats or incomplete datasets. Finally, collaboration improves because teams work with consistent, reliable information across all tools, reducing errors in analysis or operations.

 

A complete overview of data leads to more precise and informed decisions. It also simplifies data sharing by enabling seamless movement of data between systems without manual adjustments. Data liquidity also supports regulatory compliance by providing clear, traceable data flows to meet all regulatory standards.

 

 

How is data liquidity measured?

Assessing how fluidly data moves within a company is crucial for understanding how that data should be stored and used. This index provides a holistic overview of the data ecosystem. The following key metrics are used to calculate the Data Liquidity Index:

• Data Availability Index: Measures how easily data is available. It is calculated by surveying users about the ease of accessing data and measuring the average time required to retrieve specific data sets.
• System Interoperability Score: This score indicates whether different systems can exchange and use information. It is determined by counting the percentage of systems that can exchange data without manual intervention.
• Data latency: Reflects the time required for data exchange between systems. Latency is measured by capturing the delay between the creation/update of data and its new availability.
• Data quality assessment: This involves verifying whether the data has been correctly formatted and classified. This assessment is conducted using data profiling tools and user feedback, taking into account factors such as accuracy, completeness, consistency, and timeliness.
• Data usage rate: Provides information about the extent to which employees are using available data. It is calculated by monitoring data access logs and analysing the frequency and extent of data usage across departments.
• Cross-functional data flow: Evaluates the smooth transfer of data between teams to support collaboration and is determined by tracking the number of successful data exchanges between different departments and the time required for cross-departmental data requests.
• API response time: Determines how quickly systems retrieve data. This number is measured by timing API calls and calculating the average response time across different endpoints and data sets.
• Data integration cycle time: Specifies the time required to integrate new information sources. The cycle is calculated by averaging the time required to fully integrate new data sources from initial connection to production-ready status.

 

Keeping the data flowing

Integration Platform-as-a-Service (iPaaS) solutions have become essential for keeping data flowing smoothly within companies. These platforms address the everyday challenges companies face in achieving high data liquidity. They improve integration processes through centralised management and by simplifying workflows for teams.

 

The key benefits of iPaaS for businesses include:

• Centralised data management: iPaaS consolidates integration processes through a single point of control, simplifying monitoring and reducing complexity.
• Real-time data synchronisation: The platform ensures immediate updates across all connected systems, providing employees with consistent, up-to-date information for timely decision-making.
• Scalable architecture: An iPaaS adapts to growing data volumes and new sources without compromising performance or accessibility, and grows with the needs of your business.
• Automated data quality checks: Integrated validation and cleansing functions ensure high data quality standards and guarantee reliable insights for critical decisions.
• Advanced security features: Robust encryption, access controls, and compliance tools protect sensitive data during integration and ensure information security.
• Self-service integration tools: iPaaS enables business users to set up data flows independently, reducing dependence on IT and accelerating access to insights.

 

Achieving data flow

Companies rely on data. They strive to make informed decisions and respond quickly to market changes. The ability to understand and improve data liquidity is crucial for them. Data liquidity provides a unified view of operations, enabling timely and informed decision-making. Implementing the right tools, such as an iPaaS, and strategies to optimise fluid data flow is therefore crucial for a data-intensive company.

 

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Markus Müller is Field CTO APIM at Boomi 

 

Main image courtesy of iStockPhoto.com and nadla