Category Archives: Data Quality
Data Governance and Systemic Data Management Issues
We have been looking at how data management issues can be classified, and in my last post I provided five categories, but broken them down into two groups: Systemic and System. The systemic issues are ones in which process or management gaps allow data flaws to be introduced. A good example occurs when consumers of reports from the data warehouse insist that the data sets are incomplete, and the root cause is that the processes in which the data is initially collected or created do not comply with the downstream requirement for capturing the missing values. (more…)
Do You Have The Skills To Stand Out As The Go-To Data Quality Expert?
Regardless of your specific industry focus, there is no denying that the tsunami of big data is changing the way we do business. Our customers have admitted that along with the anticipation and excitement of being in the midst of this big technological wave, there is also some anxiety about how to manage all this data. We can practically feel the disciplines of data integration, master data management (MDM), data governance and data quality rising up in importance.
But ask yourself this: Do you have a strategy in place today to ensure that the business has confidence in your data? You may already know that Informatica’s data quality solution helps increase revenue, reduce cost and manage risk. But, do you have the knowledge and skills needed to profile, standardize, match and consolidate data as effectively and efficiently as you can? (more…)
Data Management Issue Categories
In my last post I started to talk about ideas for classifying the data management issues, with the reasoning that it will help to determine the feasibility that the expectation that acquiring a particular solution will actually address the core issues. I actually have used this categorization with some of our customers, and the process of classification does lend some clarity when considering solutions. There are five categories: (more…)
Classifying Types of Data Management Issues
Coincidentally, my company is involved with a number of different customers who are reviewing the quality criteria associated with addresses. Each scenario has different motivations for assessing address data quality. One use case focuses on administrative management – ensuring that things that need to happen at a particular location have an accurate and valid address. A different use case considers one aspect of regulatory compliance regarding protection of private information (since mail delivered to the wrong address is a potential exposure of the private information contained within the envelope). Another compliance use case looks at timely delivery of hard copy notifications as part of a legal process, requiring the correct address. (more…)
ANNOUNCING! The 2012 Data Virtualization Architect-to-Architect & Business Value Program
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…)
What it Takes to Be a Leader in Data Virtualization!
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. (more…)
Informatica Positioned in the Recent Gartner Magic Quadrant for MDM
Gartner recently published its annual Magic Quadrant for Master Data Management of Customer Data Solutions, which “positions MDM of customer data solution vendors (and their products) on the basis of their Completeness of Vision relative to the market and their Ability to Execute on that vision.” The growth of MDM market has been phenomenal – $1.6 billion in 2011, a growth of 21% from 2010, and projected to grow by the same rate to $1.9 billion in 2012. (more…)
Reading The Tea Leaves: Predictions For Data Quality In 2012
Following up from my previous post on 2011 reflections, it’s now time to take a look at the year ahead and consider what key trends will likely impact the world of data quality as we know it. As I mentioned in my previous post, we saw continued interest in data quality across all industries and I expect that trend to only continue to pick up steam in 2012. Here are three areas in particular that I foresee will rise to the surface: (more…)
Dealing with the Latest CFTC Ruling on Real-time SWAP reporting – Are you prepared?
The recent Commodities Futures Trading Commission (CFTC) ruling requiring real-time reporting for over-the-counter (OTC) swap trading was decided over the holidays to increase transparency and provide a comprehensive view of the entire swaps market to help regulators monitor and govern market activities and hedge against increased systemic risk. This ruling is a major change for many companies who have had little to no regulatory reporting requirements prior to this rulings. The deadlines for real-time swap reporting are right around the corner as the first of three deadlines being July 16, 2012 to commence real-time swap reporting.
Meeting these new reporting requirements poses significant challenges for those impacted by the new ruling that cannot be ignored. Let’s take a look at what they are and how Informatica’s solutions can help overcome these obstacles. (more…)
Scoping Failure Analysis
In adapting the six-sigma technique of failure mode and effects analysis for data quality management, we are hoping to proactively identify the potential errors that lead to the most severe business impacts and then strengthen the processes and applications to prevent errors from being introduced in the first place. In my last post, though, I noted that the approach to this analysis starts with the errors and then figures out the impacts. I think we should go the other way so as to optimize the effort and reduce the analysis time to focus on the most important potentialities. (more…)
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