THE DATA DOWNLOAD

Topics: Data Warehousing, Blog, Data Architecture

This is the third in a series of articles describing foundational steps to enable agile data warehouse development. This first series of articles 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, then building a “grass roots” (at a minimum!) Data Governance capability.

The next focus for setting yourself up for a best in class agile data warehouse environment is to develop a high level data flow architecture that is inherently flexible and leverages repeatable design patterns.

Topics: Agile, Data Integration, Blog

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.

Topics: Agile, Blog

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.

Topics: Agile, Data Warehousing, Blog

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.

Topics: Program Management, Blog

Self-managing teams are vital in order to be truly successful in agile development. Here's how to encourage self-managing teams, and the benefits that can be achieved.

Earlier blogs describe the foundational steps that enable agile data warehouse development.

The next focus for setting yourself up for a best in class agile data warehouse environment is to develop and support self-managing teams.

Topics: Tool Selection, Data Virtualization, Blog

Data organizations are increasingly looking to data virtualization as an efficient approach to address many of the data integration challenges they face, providing broader access to departmental data assets in a simpler way than ever.

Composite Software, Cisco’s data virtualization platform, is one of the preferred data virtualization options available in the industry today.

Topics: Informatica Data Quality, Data Quality, Blog

CONSISTENT NAMING AND CODING STANDARDS

When designing rules in Informatica Data Quality, the developers and data stewards will see the same rules. Therefore, it is important to develop consistent naming and coding standards. For example, both the data steward and the developer will understand what “rule_” means while not everyone will understand what “mplt_” means. Therefore, mapplets should be named rule_ if they are used in both the Analyst and Developer tools.

Topics: Informatica, Blog

Last year, I had the privilege of running social media for Informatica World, as well as the joy of singing for hours at an impromptu closing night dance party.

This year, I’m honored to serve as a main stage emcee. To prepare, I’m watching extra episodes of the Tonight Show, The Voice and American Idol, to borrow tricks of the trade from Jimmy Fallon, Carson Daly and Ryan Seacrest. You never know, I may just smash an egg on my forehead!