Tag Archives: CRM
As the founder of Dynamic Data Masking, I have the opportunity to meet many organizations worldwide, who are willing to openly discuss their security challenges.
These conversations have brought me to foresee the birth of yet another category in the fast growing application security market – Jurisdiction-based Access Control. (more…)
Over the last few years most enterprises have implemented several (if not more) large ERP and CRM suites. Although these applications were meant to have self-contained data models, it turns out that many enterprises still need to manage “master data” between the various applications. So the traditional IT role of hardware administration and custom programming has evolved to packaged application implementation and large scale data management. According to Wikipedia: “MDM has the objective of providing processes for collecting, aggregating, matching, consolidating, quality-assuring, persisting and distributing such data throughout an organization to ensure consistency and control in the ongoing maintenance and application use of this information.” Instead of designing large data warehouses to maintain the master data, many organizations turn to packaged Master Data Management (MDM) packages (such as Informatica MDM). With these tools at hand, IT shops can then build true Customer Master, Product Master (Product Information Management – PIM), Employee, or Supplier Master solutions. (more…)
Government organizations continue to face increasing pressure to improve customer service and operational efficiency. To date, almost every organization has embarked on some type of program ranging from 311 call centers to CRM projects in an effort to be more responsive. However, these initiatives may be only scratching the surface of what is needed to achieve real improvements across government. (more…)
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…)
Remember the last time you were home in the evening, there was little in your kitchen to eat but you didn’t want to go out? Then you had an idea – that you could concoct a delicious meal made from a variety of completely unrelated and forgotten frozen and semi-fresh food coupled with rarely used spices and other odd ingredients. That’s a lot like predicting the future. If you stay safe and conservative, you’re going to get close to what you expect. But, if you get all crazy (think stir fry Top Ramen and turkey jerky), your prediction will sound cool, but has a low probability of working out (unless you are on Top Chef). (more…)
If you’re reading this article, you’re probably interested in big data, but don’t really know what you’re looking for with big data or how you’ll find it. Don’t feel confused. It’s not like traditional analytics, where you know the structure of the data – the relations across sources, the dimensions to build, the calculations to perform – and the reports you need. Big data can be completely unstructured, with no clear relationships. And you don’t know what you’re looking for until you find patterns. A complaint might come in about an online shopping cart being wiped out, which is what happened to my wife with a big toy retailer during some online Christmas shopping. I’ll tell you right now they ended up losing a lot of money. If they’re using big data, they might find a pattern of the steps that made her hit that bug. Then they might search for all customers that had the same problem, get their e-mail addresses or names, and do a recovery campaign. I hope the retailer is using big data properly. My wife would receive a call, and get that order. They’d be happy, and I’d be happy. (more…)
Let’s say you’re a Fortune 500 manufacturer and a supplier informs you that a part it sold you last year is faulty and needs to be replaced. What’s the first thing you do—and how do you do it?
You need answers fast to critical questions: In which products did we use the faulty part? Which customers bought those products and where are they located? Do we have substitute parts in stock? Do we have an alternate supplier? (more…)
Given a list of data domains that were critical to the success operation of a set of business processes, we start to get a picture of the interdependence of many applications on the same conceptual data. In our last discussion, we came to the conclusion that a top-down consideration of the value of quality data to specific activities would result in a list of dependent data domains for each activity.