Integrating ERP and BI solutions – the perfect ‘next step’ for companies seeking to manage their operations more effectively…

The integration of ERP and BI in one solution eliminates “tens of thousands of dollars” in costs traditionally associated with the deployment and implementation of separate ERP and BI software.

Traditionally BI has only been made available to a few people in a business, with only senior managers and key people at higher levels of an enterprise able to access and analyse the data. Pronto has responded to market demand and the trend in many businesses today where there is a need for staff at all levels to have full access to BI, and to be able to extract and analyse information to help them make decisions which potentially give the business a competitive edge.

To reinforce the point about costs, which he says are “enormous” when non-integrated ERP and BI solutions have to be implemented, Goepfert says historically that often meant a company had to employ consultants and contractors, costing tens of thousands of dollars, to come into the business to extract the information from ERP and bring it into BI before the data could be analysed.

All that extra cost can be reduced if companies do their research. An integrated solution should make the process very simple – even without ERP expertise – to extract the information from ERP, analyse the data and apply it to your business decision-making in the form of BI.

Business Intelligence (BI) applications are increasingly prevalent in the Small and Medium-sized Enterprise (SME) sector. BI vendors are targeting SMEs but many projects fail due to poor planning, lack of resources, organization immaturity and failure to understand the complexities of integrating such applications with existing business systems. This study looks at how manufacturing Small and Medium-sized Enterprises (SMEs) using or planning on using Enterprise Resource Planning (ERP) software could integrate BI to improve the availability of operational and strategic planning information within the constraints imposed on organizations in the SME category. SMEs need BI tools integrated with their financial (and other) applications so users (not only dedicated IT resources) can access the financial and operational data residing in the ERP systems in a quick, easy, efficient and cost effective manner. The study approaches BI integration from three perspectives. Strategy process looks at how strategy is implemented in SMEs as a precursor to defining how BI applications can integrate organizational goals into Key Performance Indicators (KPIs). BI maturity models are presented as a roadmap to measure the capability and readiness of an organization to progress BI, and BI tools are discussed from the perspective of selection, integration strategies and delivery platforms suited to SMEs culminating in a best practices framework for BI integration in the SME sector.

Things to keep in mind when incorporating business intelligence into ERP implementation:

• Business intelligence is not the same as reporting. ERP systems have always provided decent levels of reporting. In the past, most of this information was fairly superficial and more focused on supporting transactions rather than analysis or decision-making. For example, a report of open purchase orders is a lot less meaningful than predictive analytics that estimate future purchasing needs. In order to effectively leverage business intelligence, companies need to look beyond fancy end-user reports. Instead, they need to understand how information is gathered in the system and transformed into meaningful decision-making tools. As a general rule of thumb, ERP vendors that provide business intelligence or business warehouse modules are more likely to provide this functionality.
• Flexibility is key. Different organizations have different ways of analyzing and using data, especially those in different industries, and these needs evolve over time. Therefore, it is critical that companies look to flexible business intelligence solutions within their ERP systems. ERP systems that provide multiple ways of analyzing and presenting data are more likely to evolve with an organization as its needs evolve. In addition, they help organizations adapt to the power of available information as it is collected over time. Organizations also need to look for flexibility in terms of how data is delivered (e.g., traditional reports, executive dashboards, mobile applications, etc.).
• Define business intelligence requirements. In order to accomplish the first two points above, organizations need to define requirements related to business intelligence. While traditional requirements focus on how transactional business processes should work, business intelligence requirements require an organizational definition from a different perspective. In addition to focusing on how work is completed, ERP project teams should also decide how information will be used and decisions will be made. For example, it is helpful to define how demand will be forecasted, how inventory decisions will be made, and other more analytical and decision-based processes. In addition to leveraging these requirements during the selection stage of a project, organizations should also use them to ensure that the ERP software incorporates these functions while being implemented.

Preparing data for using BI with ERP

Ideally, an organization will have a master data management (MDM) plan in place or, at the very least, some basic efforts to ensure consistent data definitions and quality. Still, belief in ERP as the be-all and end-all answer to data management persists, making some think they can skip this step if they have ERP in place.

Transactional master data management -- the ERP vendors went through this phase -- was that the way you solve MDM is just deploying more and more on their technology, and we all know that that's not feasible," said Jeff Woods, managing vice president of ERP and SCM for Gartner. The ERP vendors are attacking the MDM problem, but more from a transactional point of view.

That, of course, leaves out plenty of data for most organizations, hence the need for a more overarching approach to data quality and to a BI strategy in a greater context than just ERP.

One of the things we advise is to make sure you have a BI strategy. Just using whatever the ERP provides is not always the best analytics strategy -- you need to make a conscious decision about how you're going to use BI and analytics. This should also account for and clear up data quality preparation and potential pitfalls.

A lot of folks get enamored by the looks of the [BI] solutions for the end user, and they don't pay enough attention to the data," he said. "IT is responsible for the architecture to support BI over time, and data quality is almost always the first area they have to tackle. And business people don't generally recognize how complicated the data really is.

People forget that data is problematic -- even in the best-run companies.

Even data that's coming out of an ERP system might not be pristine because you might have different codes for the same thing," he said. "For instance, you may have different customer codes, so if you're not aware of that, you can't do enterprise customer data analysis.