In our recent webinar, "ERP to Real-Time Analytics: Build Your Data MVP," we discussed the complexities of transforming data from traditional ERP systems like SAP and Oracle into actionable insights. Industry leaders shared their expertise, emphasising the importance of a Minimum Viable Product (MVP) approach for data transformation projects while addressing the challenges of navigating ERP data systems.
In this blog post, we’ll summarise the key insights and practical strategies discussed during the webinar, providing a roadmap for building a data MVP.
ERP Systems: Strengths and Weaknesses
Strengths
ERP systems are powerhouses when it comes to operational efficiency. They streamline day-to-day business processes, ensuring that transactions are recorded accurately. One of their standout features is a centralised database that enhances collaboration across various departments. This not only improves communication but also helps organisations maintain regulatory compliance through standardised reporting.
Weakness
However, despite these impressive capabilities, ERP systems have notable weaknesses, particularly in data analytics. Designed primarily to operate a business, they often fall short when it comes to analysing data. There’s a clear divide between their operational strengths and analytical limitations. While they capture transactions effectively, they lack the configuration for trend analysis or scenario planning, making real-time decision-making a challenge.
Furthermore, the architecture of many ERP systems is rooted in outdated hardware constraints. This complicates the extraction and utilisation of data for real-time insights, often requiring modern tools to bridge the gap.
The MVP Approach
Given these challenges, making a strategic approach essential. This is where adopting an MVP (minimum viable product) approach can help organisations enhance their data projects.
Dan Harris articulated this well: "Prove it can be done in one use case and then expand it." By focusing on quick, small-scale wins, organisations can build momentum and gain stakeholder support. Starting with a single pipeline or connector allows for manageable increments to deliver value, which is crucial given the complexities of ERP data.
Panellists emphasised prioritising high-value use cases and delivering early wins to foster greater stakeholder buy-in. Concentrating on impactful, manageable projects helps demonstrate the value of data transformation and justifies ongoing investment. This incremental approach minimises risk and encourages continuous improvement, allowing organisations to refine their strategies and scale their efforts with confidence.
Overcoming Data Extraction Challenges
Another major technical challenge with ERP data lies in its extraction. Our panellist, Dominic Orsini, stated, “When it comes to extracting data, it has to be as light touch as possible and critical in terms of security." ERP systems are often mission-critical and sensitive, making it necessary to minimise disruption during the data extraction process while maintaining security protocols.
Dominic also highlighted the need for secure, low-impact data extraction processes to ensure that critical systems continue to operate smoothly during transformations. Extracting data for analysis needs to be as seamless and secure as possible, which requires specialised tools like those provided by Prophecy and Fivetran. The use of these tools allows organisations to manage complex data pipelines and ensure that extracted data is immediately usable for real-time analytics.
Validation and Success Metrics
To drive long-term success, it's crucial to define success metrics early and validate the extracted data before moving forward. Pete Williams emphasised the importance of ensuring data accuracy and usability, asking, "Are you landing the sufficient grain of data... and is it usable or is it corrupted?" Maintaining data integrity throughout the process is vital for informed decision-making.
Rajkumar Manoharan reinforced the need to align data initiatives with business cases and set clear KPIs. By establishing success metrics that are directly tied to business outcomes, technical teams can ensure their efforts contribute to the organisation’s strategic goals.
Conclusion
In conclusion, the key takeaway from the webinar is the importance of approaching data transformation projects incrementally, using an MVP framework. Starting with small, high-impact data pipelines enables companies to demonstrate value quickly while mitigating risk. Extracting and transforming ERP data requires advanced tools and methodologies, as these systems are not built for real-time analytics.
To succeed in data transformation projects, organisations must focus on validating data, securing stakeholder buy-in through quick wins, and ensuring data integrity throughout the process. Ultimately, integrating modern ETL tools with existing ERP systems allows businesses to unlock the value of their data and drive better decision-making across the enterprise.
Actionable Steps:
- Identify high-value use cases and prioritise quick wins.
- Utilize modern ETL tools for secure, low-impact data extraction.
- Validate data quality at each step and define KPIs early.
- Focus on an MVP approach to prove value incrementally and scale.
To learn more about ERP to Real-Time Analytics, get in touch with the Cloudaeon Innovation Lab team today.
