Category Archives: Profiling
Informatica Data Quality 9.1: Discover, Profile And Cleanse – Oh My!
Today is an exciting day for Informatica as we go live with the launch of Informatica 9.1. With this new release we are further extending our platform vision to bring to market industry leading data integration, MDM and data quality capabilities.
With the launch of Informatica 9.1, we’ll be introducing new capabilities aimed at making the process of managing data quality that much easier. Here’s a sneak peak at what’s in store: (more…)
Why MDM and Data Quality is Such a Big Deal for Big Data
Big Data is the confluence of three major technology trends hitting the industry right now: Big Transaction Data (describing the enormous growing volumes of transactional data within the enterprise), Big Interaction Data (describing new types of data such as Social Media data that are impacting the enterprise), and Big Data Processing (describing new ways of processing data such as Hadoop). If you can imagine companies having problems with business-critical master data such as customers, products, accounts, and locations at current data volumes, now that problem is compounded many-fold with the growth into Big Data. That’s where MDM and Data Quality come in as the fundamental solutions. So, why is MDM and Data Quality such a big deal for Big Data? (more…)
Public Opinion And Data Quality: A Lesson In Sustained Data Quality Improvement
In the U.S., and perhaps around the world, the events of a couple of weeks ago are truly memorable. As is often the case, when significant politically oriented shifts occur, the discussion of public opinion towards those in charge rises to the surface. The past couple of weeks, of course, are no exception. In scanning through the various media outlets, I came across an interesting read regarding the impact of such events on presidential public opinion. Not surprisingly, history shows that immediately after a significant positively impactful event, public opinion ratings for the “Commander in Chief” surge. This is due in large part to the general public essentially offering a reward or pat on the back for a job well done. People, for the most part, lose sight of what it was that caused them to not give a glowing report card in the first place, change their tune, and come to the belief that perhaps they were in fact wrong. Over time, however, we see that this point of view doesn’t last. Again, as history shows us, a short period of time after the bump in favorable public opinion the polls slowly drop back to levels they were before the significant event occurred. Essentially a one off win isn’t enough to sustain long term improvement in public opinion so more must be done.
Customer Data Forum Off To A Great Start Featuring MDM
We launched a coast-to-coast Customer Data Forum road show with visits to Atlanta and Washington, D.C., that attracted business and IT professionals interested in using master data management (MDM) to attract and retain customers.
From the business side, our guests consisted of analysts, sales operations personnel, and business liaisons to IT, while the IT side was represented by enterprise and data architects, IT directors, and business intelligence and data warehousing professionals. In Washington, about half the audience was from public sector and government agencies. (more…)
Life Cycle Events, Customer Retention, and Data: Pulling It All Together
The last set of blog entries looked at the value of maintaining high quality data to support customer retention activities and processes in reference to life cycle events. To pull my thoughts together, we have looked at the business expectations for retention, business processes that center on life cycle events, and impacts related to data issues. The next step is to consider the underlying data requirements to maintain a high degree of information utilization. I believe we can roll this into two main sets of requirements:
- Managing high quality master data associated with the customer, key aspects of the customer’s life, and the life cycle of that customer’s purchased products and services; and
- Overseeing the observance of expectations associated with following dimensions of data quality: completeness, accuracy, currency, and timeliness.
By preventing data flaws related to master customer and master product data, the appropriate life cycle triggers will fire at the right time, leading to new business opportunities and elongated customer lifetimes. For more on this topic, read my newest white paper entitled: Increasing Confidence, and Satisfaction Through Improved Data Quality.
Navigating The Maze: Tips For Enabling Successful Data Governance
I have been talking with a lot of customers lately on the topic of data governance. Over and above the obvious question of “what is data governance”, two other common questions seem to come up, both related to helping make it a reality. Time and time again, I get questions regarding what the right approach is to data governance as well as how to effectively sell its importance to the business. Let’s explore both in a little more detail. (more…)
Introduction…
I am very honored to have been asked to become a contributing blogger to Informatica Perspectives, and I am looking forward to sharing thoughts about what I like to refer to as “information utility.” The online version of the American Heritage Dictionary defines utility as “The quality or condition of being useful,” and I would like to adapt that definition for my own purposes: establishing information utility is the process of ensuring the quality and usefulness of information.
Luckily, over the past 15 years I have been actively pursuing a number of activities and research areas that fundamental to information utility, ranging from data cleansing, data quality, identity resolution, data integration, master data management, business intelligence, data mining, all the way to data governance. Perhaps you may already be familiar with some of my monthly columns at the Business Intelligence Network, or having read one of my books on Master Data Management or Data Quality. I hope to share my experiences as well as experiences our consulting practice has had with our clients in a way that can help you improve your organization’s information utility.
And I am always looking for feedback – I hope that my entries will inspire readers to share their own thoughts and experiences as well!
Life In The Fast Lane: Using Data Quality For Accelerated Results
As Bay Area commutes go, I consider mine to be on the long side. At roughly 60 miles each way, I can expect to be in my car for a while depending on what time of day I make the journey to or from the office. As is often the case, I take the time I have in my car to think about work and the various items that consume my inbox on any given day.
During a drive home late last week, I was pondering some thoughts I had on how to articulate the ROI of good data quality. As commutes tend to go sometimes, this day was particularly frustrating so I had some extra time on my hands to think things through. As I sat at a standstill for what seemed like an eternity, it dawned on me that the carpool lane was, for the most part, completely empty. After briefly contemplating the utility of the very high fine associated with jumping into the empty carpool lane, I realized that the flow of traffic at that particular moment was a good example of how data often flows throughout an organization.
Taking Stock Of DQ Predictions For 2011
To kick off the new year, I decided to spend some time sifting through the various data management in 2011 predictions that surfaced over the past couple of weeks. One discussion in particular offers some interesting thoughts on what we can expect to see from the data management market this coming year. In an article titled Six Data Management Predictions for 2011, the following trends are predicted for next year:
- Data will become more open
- Business and IT will become blurry
- Tools will become easier to use
- Tools will do less heavy lifting
- CEOs and Government Officials will gain enlightenment
- We will become more reliant on data
These are all reasonable for the data management market as a whole, but I thought it would be interesting to take a closer look at what implications they have on data quality tools in particular. Let’s look at the role of data quality tools play in addressing each one: (more…)
Grounded By Poor Data Quality
Recently, CNN reported that the FAA is set to embark on an overhaul of the registration of all private aircraft in the United States. Of the over 350,000 private aircraft currently registered with the FAA, an alarming 1/3 is believed to have inaccurate registration records. While most aircraft are believed to be junk or inactive, there exists the possibility that several are indeed operational, however they cannot be located. This comes with not only financial impact, through having to recreate processes that should have been sufficient for tracking them in the first place, but also a potential impact to public safety, as those aircraft that are operable but unaccounted for could be used for potentially harmful acts. (more…)
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