Tag Archives: ICC
In a recent visit to a client, three people asked me to autograph their copies of Integration Competency Center: An Implementation Guidebook. David Lyle and I published the book in 2005, but it was clear from the dog-eared corners and book-mark tabs that it is still relevant and actively being used today. Much has changed in the last seven years including the emergence of Big Data, Data Virtualization, Cloud Integration, Self-Service Business Intelligence, Lean and Agile practices, Data Privacy, Data Archiving (the “death” part of the information life-cycle), and Data Governance. These areas were not mainstream concerns in 2005 like they are today. The original ICC (Integration Competency Center) book concepts and advice are still valid in this new context, but the question I’d like readers to comment on is should we write a new book that explicitly provides guidance for these new capabilities in a shared services environment? (more…)
In my last blog article, I talked about the challenges associated with changing an organization to establish a sustainable integration strategy, and I outlined the first two change management principles. Here are seven more of the original nine. (more…)
They say people are resistant to change. I disagree. People are resistant to uncertainty. Once people are certain that a change is to their benefit, they will change so fast it will make your head spin. It would be a mistake however to underestimate the challenges of changing an organization from one where integration is a collaboration between two project silos to one where integration is a sustainable strategy with a common infrastructure based on strict standards and shared by everyone. (more…)
A CIO told me “After five years with an integration Center of Excellence, I expect them to be excellent. They aren’t.” But so what? The IT organization has lots of things to focus on. Is integration excellence really essential? (more…)
Data integrity is closely linked to the concept of trust which, in the world of human interactions, is based on a tight coupling between words and actions (do what you say and say what you do). In the IT world, this translates into first having a clear definition of data as well as how it is treated in the context of various business processes. If we have a clear definition of data, including policies such as access, privacy, change controls, etc. (the words), and if we have systems that consistently enforce the definition (the actions) then we have high trust and high data integrity. We know exactly what to expect, and the data always exactly matches our expectations. (more…)
Continuing the tour of our Data Governance Framework, it’s time to discuss the corporate policies that must be documented to form the foundation of your data governance efforts. When defined, approved, evangelized and enforced appropriately, these policies have the power to accomplish a feat that grassroots data governance efforts fail at repeatedly: Evolving your corporate culture to one that actually does manage data as an asset.
I told the head of the Enterprise Data Warehouse at a large bank, “you don’t have a data warehouse, you have 50,000 tables.” The issue is that the bank built the EDW without the necessary fundamentals in place. It wasn’t for lack of money; in fact the EDW was one of the biggest “money sinks” in the bank. The problem is that it was sitting on a sinking foundation.
One version of the truth isn’t achieved by putting all your data in one big system or one big database – that’s impossible. An enterprise data warehouse is indeed part of the solution, but it needs to be built on a solid foundation. What does a solid foundation look like? Here are five pillars for one version of the truth. (more…)
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…)
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…)
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…)