Category Archives: Business/IT Collaboration
Some are looking to grow their use of metadata beyond an IT productivity project into something larger such as a data stewardship or data governance initiative. Some are just getting started. In either event, here are my top ten recommendations based on conversations with customers and some of the great presentations and panel discussions at Informatica World. (more…)
Evaluating a price in the Equities or Foreign Exchange (“FX”) markets does not require much calculation, and so one of the prime limiting factors on winning those trades has been the speed of data movement, from one application to another, either within the same host or across the network. But the world of Fixed Income, commonly known as “bonds”, is different. (more…)
In 2006, Informatica announced a strategic roadmap for cloud data integration, which outlined three phases:
There’s been a lot written about the importance of customer adoption and success to the software as a service (SaaS) model – there’s even a manifesto and a Bill of Rights! Last month, Informatica Cloud received the Bronze Stevie Award for customer support. One of the drivers for this achievement was not only great front-line customer service from a world-class support organization (they even support trial accounts!), but also the establishment of a customer success team, which is run by Bryan Plaster. I sat down with Bryan to discuss his views on the importance of customer adoption to any cloud computing application, platform or infrastructure initiative, as well as some specifics on how Informatica Cloud approaches customer success. (more…)
I regularly receive questions regarding the types of skills data quality analysts should have in order to be effective. In my experience, regardless of scope, high performing data quality analysts need to possess a well-rounded, balanced skill set – one that marries technical “know how” and aptitude with a solid business understanding and acumen. But, far too often, it seems that undue importance is placed on what I call the data quality “hard skills”, which include; a firm grasp of database concepts, hands on data analysis experience using standard analytical tool sets, expertise with commercial data quality technologies, knowledge of data management best practices and an understanding of the software development life cycle. (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…)
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…)
I just came back from MicroStrategy World. There were many conversations about social, mobile, cloud and big data. There was strong interest in cloud, clear adoption of mobile, and some big data adoption. eHarmony had a great presentation about how they handle big data with Informatica, and how they’re starting to use Hadoop with Informatica HParser running on Hadoop for processing JSON.
But that wasn’t the number one conversation. The one topic that everyone was interested in – and I talked to nearly 100 customers and partners over four days – was creating new reports faster, or Agile BI. (more…)
Today, agility and timely visibility are critical to the business. No wonder CIO.com, states that business intelligence (BI) will be the top technology priority for CIOs in 2012. However, is your data architecture agile enough to handle these exacting demands?
In his blog Top 10 Business Intelligence Predictions For 2012, Boris Evelson of Forrester Research, Inc., states that traditional BI approaches often fall short for the two following reasons (among many others):
- BI hasn’t fully empowered information workers, who still largely depend on IT
- BI platforms, tools and applications aren’t agile enough (more…)