4. Reduce Variation
Variation in BI is caused by a lack of standardization in processes, design, procedures, development and practices. Variation is introduced when work is initiated and implemented both inside and outside of the BI group. It causes waste in a number of ways including the added time to reverse engineer what others have developed, recovering ETL jobs caused by maintenance overlap, the extra time searching for scripts and reports, and the duplication of development caused by two developers working on the same file. Examples of variation that cause waste include:
- Different definitions for the same element.
- Data formatting differences.
- Different business rules across applications.
- Reports named differently in the BI portal.
- Scripts saved in different locations.
Often, variation is introduced by external consultants, especially those working offshore. Most consulting companies have their own methodologies and standards, and most consultants have their own styles of development. This can lead to different naming conventions, mapping strategies, error handling and countless other areas that produce confusion and added maintenance burden. Make sure to communicate your standards, processes and practices at the beginning of an engagement and ensure compliance during design reviews.
Standardization doesn’t mean “unchangeable” nor does it mean “stifle creativity.” Consider adjusting your standards when new ideas are introduced that make sense in your environment. Ensure that your team understands that the standards aren’t written in stone.
5. Pursue Perfection
Perfection is a critical component of Lean BI even though the key to successfully pursuing it is the understanding that you will never get there. The key to pursuing perfection is to focus on continuous improvement in an increment fashion. The difficult part is that BI teams tend to be inundated with work and don’t have time to spend working on small internal projects. The key is that the first Lean initiative should free up enough time to work on the next Lean initiative.
There are situations where compliance requirements make changes to the environment very difficult and time-consuming. For example, pharmaceutical companies are regulated by the FDA and many reporting systems are required to be validated if utilized for product safety decisions. This can add significant overhead for even simple changes. In this situation, consider choosing BI tools that have better change auditing capabilities and change traceability.
The goal of BI is to provide customer value by facilitating the process of turning data into information, communicating that information effectively and leveraging it to enable better decisions. These decisions exist at all levels of within an organization, and can be both inward facing and outward facing to IT. These decisions can also range from strategic to tactical. At the senior management level information is highly summarized, often into scorecard and trend views, so broad patterns of performance and behavior can be monitored against strategic goals. At the middle management level information is more granular but still summarized, helping to identify emerging problems and weaknesses to be addressed.
At the individual and team level, information is often highly granular and transactional, helping to keep an eye out for problem prevention rather than reaction. As you move down these levels, the data latency requirements decrease, the data volumes increase and the analytic capabilities broaden. Delivering these levels of information requires a strong business alignment and the agility to keep up with changing requirements. Lean BI principles help organizations deliver greater value across all levels of decision-makers by reducing waste, focusing on the customer and never settling for less than perfection.
Steve Dine is the managing partner and founder of Datasource Consulting, LLC. He has extensive experience delivering and managing successful, highly scalable and maintainable data integration and business intelligence solutions. Steve combines hands-on technical experience across the entire BI project lifecycle with strong business acumen. He is the former Director of Global Data Warehousing for a major durable medical equipment manufacturing company and currently works as a consultant for Fortune 500 companies. Steve is a faculty member at The Data Warehouse Institute and a judge for the Annual TDWI Best Practices Awards. He teaches courses and presents on the topics of Lean BI, BI in the Cloud and Enabling BI for the 21st Century. Steve earned his bachelor's degree from the University of Vermont and a MBA from the University of Colorado at Boulder. Contact Steve via email at email@example.com or on Twitter: @steve_dine