Many organizations are now recognizing the need for data governance but are still struggling with the right way to structure it. A good approach: be agile!
A Note About Teams: Team Formation & Your Role As a Leader
By: Nancy Couture
I'm writing a series of posts that discuss five tips for creating and leading agile data management development teams. My previous post focused on tip No. 1: "Hire 'A' Players and Empower Them." Before moving on to tip No. 2, I wanted to take a moment to discuss team dynamics.
Business Intelligence Industry: 10 Considerations for 2014
By: Steve Dine and David Crolene
Each year, we reflect upon the business intelligence industry and enterprise information management (EIM) industry and provide a review of the noteworthy trends that we encounter in the field. Our review emanates from five sources: our customers, industry conferences, articles, social media, and software vendors. This year has proved to be an interesting one on many fronts. Here is our business intelligence industry review and observations for 2013 and predicted trends for the remainder of 2014.
Agile Business Intelligence: Increasing the Velocity of Reporting and Analytics
By: David Crolene and Donnie Evans
One of the biggest complaints we hear from business users is that it takes too long to develop reporting and analytics when working with IT. Some BI programs have turnaround times ranging from weeks and even months from the time a business unit requests a report. By that time, the business has often found a different source for the information, or the issue or no longer relevant.
A look at best practices learned while using agile development methodologies for enterprise data management solutions, including an enterprise data warehouse, data governance, data quality, integrated analytics and reporting. It starts with building a strong foundation, and then delving into additional innovative solutions leveraging what gets learned and continuously improving and expanding on it.
A successful agile data warehouse can speed the delivery of information to organizations. Ideally, initial raw data can be delivered to analysts quickly, then integrated into a formal enterprise data warehouse (EDW) for more robust and broad reporting in ensuing development cycles.
In the previous article, The Challenge with Agile, Part 1, the point was made that many Agile initiatives perceive data modeling and architecture compliance as time-consuming activities incompatible with agile development. The jeopardy of disregarding modeling/architecture is a portfolio of dis-integrated point solutions that proliferates redundant, semantically inconsistent data. Moreover, such projects are burdened with redundant data extraction and transformation.
Modeling and architecture discipline actually accelerates agile development as will be illustrated in this Part 2, Model-Driven Agile.
The value proposition of data warehousing/business intelligence (DW/BI) is compelling. However, the business community commonly voices concerns regarding lengthy project timelines, high costs and deficient functionality of initial deliverables.
Agile development methodology has emerged as a ‘faster, cheaper and better’ alternative for delivering business value. When most people think of Agile they associate it with:
- Close collaboration between development and user communities throughout the project life cycle,
- Rapid, iterative prototypes (sprints) to drive out requirements via hands-on interaction with earlier prototypes,
- Frequent, short team meetings (scrums) to identify and mitigate project roadblocks.
Having a solid testing strategy and tool set is a foundational part of enabling agile data warehouse development. This article describes an approach that ensures solid testing that can be done efficiently and effectively in an agile development environment.
This is the first in a series of articles that describe foundational steps that enable agile data warehouse development – something that has been a challenge in enterprise data management for years. My prior articles published thus far describe how to develop a business conceptual model as a starting point, building a “grass roots” (at a minimum) data governance capability, and developing a high level data flow architecture.
The next focus for setting yourself up for a best in class agile data warehouse environment is to develop a solid testing approach and tools before actual development begins.