News Stay informed about the latest enterprise technology news and product updates.

IT, executive collaboration central to BI best practices

Many BI best practices hinge on collaboration between IT and executive management. A cross-departmental BI team can ensure that software deployments go smoothly and end-user training is successful.

Ensuring the success of data warehousing and business intelligence (BI) initiatives requires a stronger partnership and greater collaboration between IT and the line of business (LOB) unit than any other technology deployment.

More on business intelligence (BI) for manufacturing
Discover the benefits of open source BI

Find out what to ask when evaluating BI tools for manufacturing

Learn how one manufacturer improved data management with BI

BI applications are expected to have the highest impact on organizations such as manufacturing firms over the next two to five years, according to the Aberdeen Group's 2009 State of the Market: Mid-Year Insights Report.

As part of that uptick in adoption of BI, companies are paying serious attention to BI best practices. Of central importance are executive steering committees, program management offices, BI competency centers and other tactics for getting IT and LOB managers -- not to mention the users -- to collaborate, not compete, on building an effective BI environment. The goal: to furnish all types of users within the organization with the kind of transformative information they need for better decision-making.

IT and business team up for BI

"The big problem with IT in general, which is magnified with business intelligence, is that business and IT speak two different languages and have two different cultures," said Wayne Eckerson, director of TDWI Research, a consulting firm that focuses on data warehousing and BI. "You can't deliver an effective BI solution unless the BI team and IT are on the same page."

To get IT and the business side to work together on BI requirements involves enlisting a cross-functional team and an executive sponsor to help foster buy-in across a manufacturer's many divisions and functional areas. Data warehouse experts agree that BI projects that take root in the IT department and are pushed over the wall out to the business without proper input and oversight from key managers are destined for failure.

"This is not an IT play," said Claudia Imhoff, president and founder of Intelligent Solutions Inc., a data management consulting firm that specializes in BI and customer analytics. "This is a business environment you're creating, and it must have significant business involvement and support in order to be a success."

Identifying the specific issues for the company, understanding the information requirements and establishing a proper data model that can scale and accommodate different aspects of the business are all part of this process, she said.

Mission statements make BI goals clear

Equally important to getting BI initiatives off to the right start is striking a balance between scoping the project as an enterprise program and breaking it up into manageable, highly targeted projects that can demonstrate value in a reasonable period of time.

According to Eckerson, delivering a set of functionality every three months is a sure way to sustain funding and maintain interest in BI among top management as well as the rank and file. He suggests creating a visual project roadmap that clearly depicts each phase of the BI project as well as the bigger-picture relationship among the data sources as they come online.

At the same time, a manufacturer's BI team can't afford to lose sight of the enterprise-wide strategy behind the individual BI implementations. As part of their BI best practices, companies should create a mission statement and guiding set of principles for what they hope to achieve from BI and come up with a common definition of what BI means to the organization.

"It behooves the BI team to have an elevator speech about the BI strategy in their back pocket," Imhoff said. "Once that's done, they can work towards a sustainable BI environment, not a one-shot wonder."

BI needs high quality data

As part of the enterprise approach, manufacturers need to define a formal enterprise architecture for the data warehouse and BI applications, which should include an organization-wide effort to improve data quality and enforce data standards. Profiling the data, leveraging the executive steering committee to create and enforce an enterprise data model, and investing in tools that enhance data quality are all part of this mix.

"Lack of attention to data quality is one of the big mistakes companies make," said William McKnight, president of McKnight Consulting Group, which specializes in data warehousing, master data management, BI, data quality and operational business intelligence. "They focus so much on architecture, the different databases involved and getting the data out to end users, they neglect the content of that information -- its efficacy and usability by the end users."

Lack of attention to data quality is one of the big mistakes companies make.
William McKnight
PresidentMcKnight Consulting Group

Selecting the proper tools and creating a self-service environment in which users can design and create their own reports and custom dashboards without involvement from IT is another important ingredient of BI success. Formal training and education programs will encourage user adoption of the tools, as will appointing super users who will serve as liaisons between IT and business, ensuring that the two groups stay on the same page.

"The BI team must start to distribute development back out in somewhat of a controlled way by creating advocates and surrogates out in departments," Eckerson said. "But the real challenge is in achieving economies of scale without losing agility and flexibility."

"If corporate doesn't respond quickly," he added, "it's likely to prompt the business back to a siloed data warehouse and BI approach."

About the author: Beth Stackpole is a freelance writer who has extensively covered manufacturing techniques and manufacturing technology.

Dig Deeper on ERP software selection and implementation

Start the conversation

Send me notifications when other members comment.

Please create a username to comment.