Informatica Data Replication has triggered a lot of interest and also a lot of questions about why replication is seeing such a resurgence in the market today. The answer is simple: the same conditions that caused its creation for mission-critical operational systems back in the early 1990′s are now happening with data warehousing as well. Read 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):
Gartner hosted a webinar on January 10, 2012: Gartner Worldwide IT Spending Forecast. One of the topics covered was industry IT spend for 2012.
In covering that topic they made a point of saying that due to severe flooding in Thailand, they expect storage to become in short supply (as much as a 29% global shortfall) through the end of 2012. It is expected that the price of storage/GB will increase as a result and supplies will fall short of demand. They recommended finding alternatives to purchasing storage to keep costs down. Read More
If you haven’t already, I think you should read The Forrester Wave™: Data Virtualization, Q1 2012. For several reasons – one, to truly understand the space, and two, to understand the critical capabilities required to be a solution that solves real data integration problems.
At the very outset, let’s clearly define Data Virtualization. Simply put, Data Virtualization is foundational to Data Integration. It enables fast and direct access to the critical data and reports that the business needs and trusts. It is not to be confused with simple, traditional Data Federation. Instead, think of it as a superset which must complement existing data architectures to support BI agility, MDM and SOA. Read More
Data warehouses are applications– so why not manage them like one? In fact, data grows at a much faster rate in data warehouses, since they integrate date from multiple applications and cater to many different groups of users who need different types of analysis. Data warehouses also keep historical data for a long time, so data grows exponentially in these systems. The infrastructure costs in data warehouses also escalate quickly since analytical processing on large amounts of data requires big beefy boxes. Not to mention the software license and maintenance costs of such a large amount of data. Imagine how many backup media is required to backup tens to hundreds of terabytes of data warehouses on a regular basis. But do you really need to keep all that historical data in production?
One of the challenges of managing data growth in data warehouses is that it’s hard to determine which data is actually used, which data is no longer being used, or even if the data was ever used at all. Unlike transactional systems where the application logic determines when records are no longer being transacted upon, the usage of analytical data in data warehouses has no definite business rules. Age or seasonality may determine data usage in data warehouses, but business users are usually loath to let go of the availability of all that data at their fingertips. The only clear cut way to prove that some data is no longer being used in data warehouses is to monitor its usage.
With his keen understanding of the role IT plays in the success of the business, Informatica CIO Tony Young shares his insight and recommendations for following best practices. Through real-world examples, he discusses the steps for using IT to realize business goals.
