John Schmidt
Open Data to Maximize Your Return on Data … Create Your Own Goldmine
If data is an asset, why should you give it away? Open data is based on the notion that some data should be freely available to everyone to use, similar to other “Open” movements such as open source software. But open data doesn’t have to only be about exposing information publicly; the same concepts can be applied inside your firewall. Here are a few examples: (more…)
Agile Data Integration and Business Intelligence Practices
This article explores Agile Data Integration and Business Intelligence practices and contrasts leading practices and technologies. First some definitions.
Agile DI is the application of agile techniques (iterative/incremental development, cross-functional self-organizing teams, rapid/flexible response to change, etc.) to address data integration challenges such as migrating data between systems or consolidating data from multiple systems. Agile BI is the application of agile techniques to address business intelligence challenges such as identifying and analyzing data to support better business decision-making. These two disciplines sometimes overlap or support each other. For example, you might use Agile DI to move data into a data warehouse and Agile BI to get it out of the warehouse in a useful form. (more…)
Making Collaborative Learning Sustainable and Creating Best Practices
Last week I wrote about the role of collaborative learning in achieving a transformation to Lean Value Streams. To make it more challenging and take it to the next level, let’s assume that all the people involved in the learning scenario all work for the same company, but they are in different functional groups and may never work together as a team again. In other words, how can the lessons learned by the integration project team be communicated to other project teams? How can we make organizational learning sustainable? (more…)
Collaborative Learning in a Lean Transformation
Collaborative learning is essential for transforming work activities that involve a high degree of uncertainty and creativity into a lean value stream. These characteristics are common in enterprise integration initiatives due to unclear and inconsistent data definitions across multiple silos, rapidly changing requirements and lack of perfect knowledge around end-to-end processes. Traditional approaches generally end up propagating the integration hairball which is inefficient and wasteful – and certainly not Lean. You could say that these value streams are simply immature processes that lack standards and metrics, which is true, but the practitioners that are involved in the process don’t see it that way. They see themselves as highly skilled professionals solving complex unique problems and delivering customized solutions that fit like a glove. But yet, the outside observer who looks at the end-to-end process at the macro level sees patterns that are repeated over and over again and what appears to be a great deal of “reinventing the wheel.” (more…)
Lean Architecture
Lean management practices have been applied in recent years to virtually all business functions and processes, including of course Lean Integration. IT architecture is no exception. But what exactly does a Lean Architecture look like and how could you measure its “leanness”? Since there is no generally accepted definition lean architecture, and since I won’t bore you with mine, it might be easier to describe what a non-lean architecture looks like. Or to ask it differently, what are some non-lean approaches to architecture? (more…)
Lean Data Warehouse – Clean Up The Waste
Many years ago (over 30 to be precise) I can recall walking the halls of more than one fortune 500 company and seeing four-foot high stacks of boxes with computer printouts in the hallway outside of managers’ offices. In fact it was not uncommon to see pallet-loads of computer printouts in some companies. When I asked one manager what the reports were and why they had so many, he said “we don’t look at the reports any more but we don’t know how to get the data center to stop sending them.” (more…)
Data Storage Is So Cheap Its Expensive
The cost for 1GB of magnetic disk storage 20 years ago was $1,000 – now it’s eight cents. 1GB is enough to store about 20 thousand letter-size scanned documents. To store the same number of paper documents would require two four-drawer filing cabinets which would cost about $400. The cost of electronic data storage is five thousand times less than paper storage.
Costs have dropped consistently 40% per year which accounts for the more than 12,000 times reduction in cost since 1992. The cost for RAID or mainframe disk storage is somewhat greater, but the historical trend for other storage devices has been similar and the forecast for the foreseeable future is that costs will continue to decrease at the same rate. Twenty years from now we will be able to buy one tera-byte of storage for a penny. (more…)
Do You Have A Strategic Integration Vendor?
If you answered NO, then consider some recent developments which may cause you to re-think your organization’s position. Loraine Lawson in her recent blog Organizations Demanding More from Data Integration Tools writes that “customers are demanding more from their data integration tools” and, by inference, from their integration vendors. The article goes on to highlight advice from Gartner to “Seek out vendors that support a range of styles.” (more…)
Velocity Gets A Facelift
Velocity, Informatica’s methodology for implementing world class data integration and data management solutions, has just rolled out a new website. This is a key step for the Next Generation Velocity since the site is based on a flexible content management system that enables rapid publication of best practice content. (more…)
Seven Essential Best Practices For Data Center Consolidation
Data center consolidation is much more than physical movement of servers and infrastructure. In fact, the facility costs and power savings are just the tip of the opportunity. The biggest benefits come from using the consolidation initiative as a catalyst to rationalize the application portfolio, archive inactive data and establish one version of the truth for the data that is left. (more…)
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