This webinar covers the five critical competencies a manufacturer should develop for a successful analytics program. The conversation compares and contrasts the roles and capabilities of MI and BI and discusses the common focus on analytics-based management decision making. What can be learned from the parallel discipline of Business Analytics that can be applied to increasing the EMI role in corporate management? Comparing and contrasting Business Intelligence and EMI provide insights into the training, culture and commitment needed to realize the benefits of analytics as a key decision support strategy. On August 8, Sarah Gates, VP of Research at the International Institute for Analytics, discusses the five critical competencies businesses should develop to build a successful analytics program. Using the DELTA model framework, Sarah tells how successful executives develop and create significant business value from analytics, especially with predictive modeling and prescriptive capabilities that are essential for organizations to compete in the 21st Century. Our Guest - Sarah Gates is the VP of Research at IIA. In this role, Sarah delivers leading analytics insights and guidance to companies in the US and abroad, helping them use analytics to improve decision-making. Prior to joining IIA, Sarah worked for 20 years in a variety of financial, analytical and operational leadership positions in business and in Oregon state government. The recorded webinar is available at: http://www.nwasoft.com/resources/webinars/five-key-building-blocks-successful-analytics-program

1. Copyright © 2013 IIA All Rights Reserved

2. Copyright © 2013 IIA All Rights Reserved

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IIA is an independent research firm
that guides organizations to better
leverage the power of analytics.
Working across a breadth of
industries, IIA uncovers actionable
insights, learned directly from our
network of analytics practitioners,
industry experts and faculty. We
deliver critical information that helps
your business run smarter.
IIA co-founder and Research Director, Thomas H. Davenport

4. Manufacturing Intelligence
• Real-time connectivity
• Manufacturing analytics
• Data-source independent
• Manufacturing operations
• Manufacturing processes
• Plant floor level integration
• Data source integration
Business Intelligence
• Static connections
• Business analytics
• Proprietary
• Business operations
• Business processes
• ERP/Data warehouses
• Constrained integration
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Adapted from Competing on Analytics, Davenport and Harris, 2007

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• Conceptually they are the same – insights into the
operation to make better decisions.
• At the system level – a traditional BI system may be
different from a MI system
• As with other industries, manufacturers are having to
become more predictive and prescriptive in order to
drive better performance
HOW DOES BI/ANALYTICS RELATE TO
MANUFACTURING INTELLIGENCE?

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MATURING YOUR USE OF
ANALYTICS
It is not just having the data or the talent…..

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Adapted from Competing on Analytics, Davenport, and Harris, 2007

9. LEVELS OF ANALYTICAL MATURITY
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Adapted from Analytics at Work, Davenport, Harris and Morison, 2010

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LEVELS OF ANALYTICAL MATURITY
5. Analytical Competitors. “Analytical nirvana.” Use
analytics across the enterprise as a competitive
differentiator and in strategy.
4. Analytical Companies. “Good at analytics.” Highly
data oriented, have analytical tools, and make wide
use of analytics. Lack commitment to fully compete
or use strategically.
3. Analytical Aspirations. “See the value of analytics.”
Struggle mobilizing the organization and becoming
more analytical.
2. Localized Analytics. “Use reporting.” And analytics or
reporting is in silos.
1. Analytically Impaired. “Not data-driven.” Rely on gut
feel and plan to keep doing so. They aren’t asking
analytics questions and/or lacks the data to answer
them.

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Adapted from Competing on Analytics, Davenport, and Harris, 2007

12. BIG IDEAS
• You need to think about each of the DELTA model
competencies when evaluating analytics maturity
• Measure your progress and set goals that will push you
to continue to improve
• Analytics for offense and defense are critical
• Learn from other firms and industries
• Take advantage of the power of predictive and
prescriptive analytics.
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