Category Archives: Profiling
I Can’t Describe It, “But I Know It When I See It”
So wrote Potter Stewart, Associate Justice of the Supreme Court in Jacobellis v. Ohio opinion (1964). He was talking about pornography. The same holds true for data. For example, most business users have a hard time describing exactly what data they need for a new BI report, including what source system to get the data from, in sufficiently precise terms that allow designers, modelers and developers to build the report right the first time. But if you sit down with a user in front an analyst tool and profile the potential source data, they will tell you in an instant whether it’s the right data or not. (more…)
Social, Mobile, Cloud, Big Data and … Agile BI
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…)
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…)
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…)
Data is the Answer, Now What’s the Question? Hint: It’s The Key to Optimizing Enterprise Applications
Data quality improvement isn’t really anything new; it’s been around for some time now. Fundamentally the goal of cleansing, standardizing and enriching enterprise data through data quality processes remains the same. What’s different now, however, is that in an increasingly competitive marketplace and in difficult economic times, a complete enterprise data quality management approach can separate the leaders from the laggards. With a sound approach to enterprise data quality management, organizations reap the benefits of turning enterprise data into a key strategic asset. This helps to increase revenue, eliminate costs and reduce risks. Using the right solution, organizations can leverage data in a way never possible before, holistically and proactively, by addressing data quality issues when and where they arise. Doing so ensures key IT initiatives, like business intelligence, master data management, and enterprise applications, deliver on their promises of better business results. (more…)
(Data) Champions Are Everywhere
I recently had the opportunity to meet with the board of directors for a large distribution company here in the U.S. On the table for discussion were data quality and data governance, and how a focus on both could help the organization gain competitive advantage in the market. While I was happy to see that this company had tied data quality and data governance to help meet their corporate objectives, that’s not what caught my attention. Instead, what impressed me the most was how the data quality and data governance champion had effectively helped the rest of the board see that there WAS a direct link, and that with careful focus they could drive better business outcomes than they could without a focus on data at all. As it turns out, the path to success for the champion was to focus on articulating the link between trusted data — governed effectively — and the company’s ability to excel financially, manage costs, limit its risk exposure and maintain trust with its customers. (more…)
Bad Data Is Criminal
Reading a recent central statistical office report showing that burglaries, theft and fraud have increased in the last quarter reminded me of a how Humberside police use data and the information they extract from it to help fight crime.
It is easy to underestimate how tenacious the modern criminal can be in avoiding attention and capture by authorities, they are not motivated to provide accurate information when questioned! Moreover, any inconsistency amongst police departments or personnel in the way in which the data was entered could add to data quality problems, inadvertently aiding the criminal. (more…)
Reflections On Gartner’s 2011 Magic Quadrant For Data Quality Tools
Gartner recently released their 2011 Magic Quadrant for Data Quality Tools and I’m happy to announce that Informatica is positioned in the Leaders’ quadrant. We believe our position is a testament to the fact that customers like Station Casinos and U.S. Xpress continue to turn to Informatica to solve their most critical data quality challenges.
The publishing of the Magic Quadrant is often a great opportunity to reflect on the state of the data quality market. It should come as no surprise that data quality as a business imperative isn’t going away any time soon. We are continuing to see customers looking for help and expertise in solving a wide range of data quality problems, largely associated with data governance initiatives, master data management (MDM), business intelligence and application modernization. And the association of data quality in these areas is only getting stronger. (more…)
I Don’t Like Rotten Applesauce
I had the good fortune to work in the information services department at UMass Memorial Healthcare for several years prior to joining Informatica. It was pretty clear when I was there that the investments UMass Memorial was making in information systems was the future direction of healthcare everywhere, and that the lessons being learned there had applicability across the broader healthcare market. Since joining Informatica, I have had the opportunity to meet with a wide cross section of our healthcare customers and prospects, and I can confirm that this is in-fact absolutely true. A good case in point is the recent discussion I had with Karen Marhefka, Associate CIO at UMass Memorial, about the challenges of poor data quality and the adverse impact this can have on migrating existing data to new applications. (more…)
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