The enterprise resource planning (ERP) software market is set for rapid growth. Worth approximately $39 billion in 2019, it is set to reach $78.4 billion by 2026. Organizations of all sizes are increasingly looking to ERP to help drive their business. And yet, data quality remains a major challenge, undermining the value of these investments.
Automated data processing: The main benefit and a key challenge for ERP
The key benefits of ERP systems relate to their ability to facilitate and automate the delivery of information to employees and senior management, helping to drive business operations and improve decision making. These benefits include faster execution of business processes, centralized access to enterprise-wide data and increased collaboration around that data. However, the value of these benefits is based on the assumption that data in the ERP system can be trusted.
In a world of ubiquitous and infinitely diverse data, there is a vast amount of relevant information that must be captured, tracked, managed and analyzed—and new sources and types of potentially relevant data continue to emerge. According to a recent survey by IDG, 44 percent of transactional data in the ERP system originates outside the organization—in other words, it is created by customers, suppliers, banks, logistics providers and other external parties. Ensuring the quality of data under these circumstances is tricky. Unsurprisingly, surveys show that data accuracy and analytics are two of the top three areas where ERP systems regularly fall short of user expectations.
Poor data quality is costly
It can be counterproductive—or even downright dangerous—to have inaccurate, out-of-date or incomplete data in any system, but the impact is compounded within ERP systems. This is because decisions based on an organization’s ERP data have far-reaching consequences.
Some time ago, Thomas Redman outlined the Rule of Ten. This states that it costs 10 times as much to complete a unit of work when the input data is bad as it does when the data is perfect. In a similar vein, the 1-10-100 rule by George Labovitz and Yu Sang highlights the exponential cost of bad data that’s left unaddressed.
To evaluate the cost of bad data in your organization, try the simple “Friday Afternoon Measurement” (FAM) method to gain some perspective (and motivation) to tackle issues around data quality. If the results shock you, you can perhaps take some comfort in the HBR’s findings that, generally, as little as 3 percent of an organization’s data meets basic quality standards. So at least you’re not alone.
Put simply, good decisions can’t be made on bad data. Bad data can cause a plethora of issues for organizations: stocking up too much inventory, failing to fulfill orders, misreading market dynamics and so on. Issues with data quality undermine operational performance and, ultimately, can even put your relationship with customers at risk.
Responding to the data ecosystem
There are many ways in which data enters ERP systems. It can be created in the system by users, brought in via synchronization from other business applications or integrated from external business partners. Making sure that you have the right processes and solutions in place to address data quality across all scenarios is important for overall data quality, which supports operational excellence.
In addition to processing more and more data, many organizations are now moving away from the monolithic ERP implementations of the past. Instead, they are looking to integrate their new core ERP instances with complementary enterprise applications to accelerate time to value, while adding flexibility and scalability into their digital ecosystem.
Internet of Things (IoT) and artificial intelligence (AI) deployments are also growing fast. By 2022, Gartner predicts that 65 percent of organizations will have integrated AI into their ERP systems. And AI, like many other business systems, will only ever be as good as the data available to it.
The importance of modern data integration
The challenge for all organizations is ensuring that only so-called “trusted” data enters their ERP systems. In addition to a robust integration strategy that spans both internal and external systems, best practices involve using enterprise data management capabilities that can handle data from virtually any data source. This helps to both ensure the accuracy and quality of information, and to maintain that quality throughout its entire lifecycle.
Modern ERP integration solutions address the critical aspects of connecting systems, as well as managing data quality as part of integration. This can include a range of activities, from simple minimum content validations to elaborate workflows involving checks against reference and master data sets.
Applying these measures mitigates data conflicts and quality inconsistencies within the ERP and other systems. This is critical to ensure timely and secure access to consolidated, clean and accurate data.
It’s not just about technology
The key to success is having the right capabilities and deploying them in an optimal way. This requires modern integration and data management technologies, as well as the expertise to wield them. However, it is often a complex undertaking to build solutions that effectively address data quality across different systems, scenarios and data types in an organization’s specific business context. Without proper planning and management, taking on such an effort can quickly turn into an expensive mess.
That’s why it is essential to have the appropriate technology and skills available. It’s also vital to facilitate collaboration between technical and business stakeholders and to manage this engagement in a strategic way. To coordinate integration efforts and manage day-to-day operations systematically and efficiently, organizations can use integration managed services from providers like OpenText. Such an approach helps to deal with the complexities involved.
Whether your organization is looking to modernize its ERP system(s)—for example, by transitioning to SAP S/4HANA or adopting a cloud ERP like NetSuite—or simply wants to maximize the value of its current ERP investment, it’s important to focus on integrations and your ability to ensure the quality of data as it moves between systems. These actions play a key role in helping you to achieve your goals.
Read the IDG white paper “ERP Modernization and Growing Data Challenges Drive 91% of Enterprises to Modernize Integration Solutions” to find out more about ERP integration and how companies are approaching related challenges.