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Managing ERP business intelligence and reporting FAQ

To drive ERP business intelligence (BI) and reporting software, you must first understand enterprise data usage. This is the first step to managing ERP business intelligence and reporting.

The key elements of a smart strategy for managing ERP business intelligence (BI) and reporting software include understanding your business needs, getting user input into system design, and building an integrated reporting structure that will support manufacturing needs.

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"One of the biggest challenges that people have with their ERP system is they don't get the right information out of the system. There's still a perception that if I just install an ERP system, I'll finally get all the right reports and be able to understand the business," said Gartner Research managing vice president Jeff Woods. In a recent interview, Woods addressed some frequently asked questions around ERP BI and explained why data and data usage is the foundation for building an effective BI management and reporting system.

Where does a good ERP BI management plan start?

The CFO or the business user needs to be actively involved in the design of the system and they need to understand their information requirements before the system is implemented.

What happens, though, when there's an organization that has a variety of BI and reporting systems cobbled together from business operating units or acquired along with other companies?

At that point, if you've got multiple systems, multiple integrations, you really need a strong BI strategy, and this is our number one piece of advice to users: You can't do BI ad hoc, you need a strategy. You need to know what the information needs are and then you build a strategic BI platform based on those needs."

Mapping out data usage takes a lot of time and effort -- can't you just pick out a flexible BI software platform and start converting to it?

Software is a part of a BI strategy but it isn't the first part of a BI strategy. You just can't get around the importance of understanding how businesses users need and consume data. Consequently, good BI reporting and management needs to have a component for making sure the organization is ready to embrace a smarter, more structured intelligence plan.

What about a data warehouse? If so much data is already inside an ERP system, where does a dedicated data warehouse come into play?

This will depend on how much of your business is taking place on your one ERP. We find that in some organizations that primarily use ERP for business operations the data warehouse decision is less important. But when you are in a heterogeneous environment, and you're dealing with different systems, then the data warehouse question becomes a much more important question. It's never unimportant; it just gets more important as your diversity of systems increases.

Why can't an IT department consolidate data and make disparate systems work together? Isn't everyone talking about easy integration these days?

A business user that is evaluating the integration problem needs to be very clear what the root cause (of the problem) is. If the root cause is different views of what data is, for example -- different systems having different views of the customers -- then, that's a data management issue. But the data management issue is not solved by throwing technology at it. The data management issue is first and foremost solved by mapping the creation and use of data through the enterprise -- then figuring out why there are these different views.

If you start with a technology solution to this problem, you're likely to get the answer wrong. The first step in a master data management project is business oriented, not technology.

If thoroughly understanding data needs is the most critical tip for starting an effective ERP BI and reporting plan in an ERP manufacturing environment, where's the next key strategy tip?

One of the big trends we see is the move from backward looking to predictive analytics, and CIOs can score a lot of points with the business by transforming the view of analytics from backward-looking results to predictive views of the business. Predictive analytics are much more valuable.

The reason predictive analytics are so valuable is that they are essential to detecting emerging patterns in the business world. And to us, this is the future value of IT. It's not about automating business processes, it's much more about unlocking ingenuity in the business, and one of the ways that is done is reforming KPIs from backward-looking to predictive analytics.

The role of IT is changing to enable the business to detect patterns, model responses to those patterns, and then quickly adapt business processes to those patterns.

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