Tag Archives: customer
Matching for Managament: 20 Common Data Errors and Variation
A good friend of mine’s husband is a sergeant on the Chicago police force. Recenlty a crime was committed and a witness insisted that the perpetrator was a woman with blond hair about five nine weighing 160 pounds. She was wearing a gray pinstriped business suit with an Armani scarf and carrying a Gucci handbag. (more…)
Just Ask the Customer
I grabbed my wife’s Harvard Business Review (HBR Jan-Feb 2012) edition before a recent plane ride to a customer meeting. After diving through a bunch of case study-type narratives I ended up in a section titled “Stop Collecting Customer Data” (page 57), which was part of HBR’s “Audacious Ideas” series. This series was aimed at showcasing some proclaimed thought leaders’ very forward-thinking and, in my opinion, also some rather ill guided ideas full off naïveté. (more…)
Master Data Model Alternatives – Part 2
Last time I introduced two different approaches for master data models and thought it would be worth examining the differences in greater detail.
The first approach is to use pre-packaged core models provided by a vendor as part of an overall MDM suite of tools. Often these types of products evolved out of industry applications in which a common information model was used to support specific types of enterprise applications. For example, a vendor might have analyzed the property and casualty insurance industry and developed core data models for customer, policy, claim, service, financial products, etc. A set of application layers may have been developed on top of these models to implement common workflows (customer risk rating for establishing premium rates, or initiating a claim). However, there is a perception that aspects of those industry-oriented models can be segregated into a more universal format, which can become the starting point for a prepackaged master domain. (more…)
Data Governance and Technical Issues
In contrast to addressing the management and process issues, we might say that the technical issues are actually quite straightforward to address. In my original enumeration from a few posts back, I ordered the data issue categories in the reverse order of the complexity of their solution. Model and information architecture problems are the most challenging, because of the depth to which business applications are inherently dependent on their underlying models. Even simple changes require significant review to make sure that no expected capability is inadvertently broken. (more…)
What is Change Data Capture? Something Business and IT Both Agree on for Mainframe Data Integration
A few days ago, I got a text message from a friend telling me that my favorite company’s stock price was suddenly tanking and that I should dump my holding. So I went to the news portal to get a stock quote and see where the stock price happens to be. I found that the stock didn’t move much at all. Thinking that it might’ve been a prank text message, I ignored it. To my dismay, the stock quote I saw was delayed by 20 minutes and the decline wasn’t yet reflected in the news portal. (more…)
When Is A Customer Not A Customer?
Here are the two pieces of our current puzzle: we have multiple meanings for the term “customer” but we want a single view of whatever that term means. To address this Zen-like conundrum we have to go beyond our context and think differently about the end, not the means. Here are two ideas to drill into: entity vs. role and semantic differentiation. (more…)
Hadoop Enriches Data Science: Part 2 Of Hadoop Series
Enterprises use Hadoop in data-science applications that improve operational efficiency, grow revenues or reduce risk. Many of these data-intensive applications use Hadoop for log analysis, data mining, machine learning or image processing.
Commercial, open source or internally developed data-science applications have to tackle a lot of semi-structured, unstructured or raw data. They benefit from Hadoop’s combination of storage and processing in each data node spread across a cluster of cost-effective commodity hardware. Hadoop’s lack of fixed-schema works particularly well for answering ad-hoc queries and exploratory “what if” scenarios.
Hadoop-Enabled Data-Science Use Cases
Big Data Meets Sentiment Analysis!
So now you are interested in proposing Big Data projects, but are skeptical about getting business excited about yet another IT project? Somehow the business did not want to talk about data integration, data quality and master data management despite all the homework you did to propose a plan of action? Enter sentiment analysis. (more…)
Building A Business Case For Data Quality: Analyze Data And Identify Anomalies
Building A Business Case For Data Quality, 4 of a 7-part series
Now comes the fun part, inspecting the data. For this step, automated data profiling will help you identify actual problems with the data as they relate to business client expectations. Here are just a few possible issues:
- Are the phone numbers empty?
- Are the admission dates missing in inpatient hospital claims?
- Are there car loans with durations greater than 10 years?
- Do shipping records lack corresponding billing records?
- Do product descriptions differ only slightly?
- Are you delivering products to many different customers with the same address?
- What business rules are being violated? (more…)
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…)