Data is the key to Marketing Automation


One topic every company is currently discussing on high level is the topic of marketing automation. It is a key factor to digitalisation of the marketing approach of a company. With Marketing Automation, we have the chance that marketing gets much more precise and to the point. No more unnecessary marketing spent, every cent spent wise – and no advertisement overloading. So far, this is the promise from vendors if we would all live in a perfect world. But what does it take to live in this perfect marketing world? DATA. One disclaimer upfront: I am not a marketing expert. I try to enable marketing to achieve these goals by the utilisation of our data – next to other tasks. Data is the weak point in Marketing Automation. If you have bad data, you will end up having bad Marketing Automation. Data is the engine or the oil for Marketing Automation. But why is it so crucial to get the

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Why building Hadoop on your own doesn’t make sense


There are several things people discuss when it comes to Hadoop and there are some wrong discussions. First, there is a small number of people believing that Hadoop is a hype that will end at some point in time. They often come from a strong DWH background and won’t accept (or simply ignore) the new normal. But there are also some people that basically coin two major sayings: the first group of people states that Hadoop is cheap because it is open source and the second group of people states that Hadoop is expensive because it is very complicated. (Info: by Hadoop, I also include Spark and alike) Neither the one nor the other is true. First, you can download it for free and install it on your system. This makes it basically free in terms of licenses, but not in terms of running it. When you get a vanilla Hadoop, you will have to think about hotfixes, updates, services,

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RACEing to agile Big Data Analytics


I am happy to announce the development we did over the last month within Teradata. We developed a light-weight process model for Big Data Analytic projects, which is called “RACE”. The model is agile and resembles the know-how of more than 25 consultants that worked in over 50 Big Data Analytic projects in the recent month. Teradata also developed CRISP-DM, the industry leading process for data mining. Now we invented a new process for agile projects that addresses the new challenges of Big Data Analytics. Where does the ROI comes from? This was one of the key questions we addressed when developing RACE. The economics of Big Data Discovery Analytics are different to traditional Integrated Data Warehousing economics. ROI comes from discovering insights in highly iterative projects run over very short time periods (4 to 8 weeks usually) Each meaningful insight or successful use case that can be actioned generates ROI. The total ROI is a sum of all the

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What everyone is doing wrong about Big Data


I saw so many Big Data “initiatives” in the last month in companies. And guess what? Most of them failed either completely or simply didn’t deliver the results expected. A recent Gartner study even mentioned that only 20% of Hadoop projects are put “live”. But why do these projects fail? What is everyone doing wrong? Whenever customers are coming to me, they “heard” of what Big Data can help them with. So they looked at 1-3 use cases and now want to have them put into production. However, this is where the problem starts: they are not aware of the fact that also Big Data needs a strategic approach. To get this right, it is necessary to understand the industry (e.g. TelCo, Banking, …) and associated opportunities. To achieve that, a Big Data roadmap has to be built. This is normally done in a couple of workshops with the business. This roadmap will then outline what projects are done in

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How to kill your Big Data initiative


Everyone is doing Big Data these days. If you don’t work on Big Data projects within your company, you are simply not up to date and don’t know how things work. Big Data solves all of your problems, really! Well, in reality this is different. It doesn’t solve all your problems. It actually creates more problems then you think! Most companies I saw recently working on Big Data projects failed. They started a Big Data project and successfully wasted thousands of dollars on Big Data projects. But what exactly went wrong? First of all, Big Data is often only seen as Hadoop. We live with the mis-perception that only Hadoop can solve all Big Data topics. This simply isn’t true. Hadoop can do many things – but real data science is often not done with the core of Hadoop. Ever talked to someone doing the analytics (e.g someone good in math or statistics)?. They are not ok with writing Java

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Why Big Data projects are challenging – and why I love it


During my professional carrier, I was managing several IT projects, mainly in the distributed systems environment. Initially, these projects were cloud projects, that were rather easy. I worked with IT departments in different domains/industries and we all had the same level of “vocabulary”. When talking with IT staff, it is clear that all use the same terms to describe “things”. No special explanation is needed. I soon realized that Big Data projects are VERY different to that. I wrote several posts on Big Data challenges in the last month and the requirements for data scientists and alike. What I am always coming across when managing Big Data projects is the different approach one have to select when (successfully) managing these kind of projects. Let me first start by explaining what I am doing. First of all, I don’t code, implement or create any kind of infrastructure. I work with senior (IT) staff to talk about ideas which will eventually be

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Big Data: what or who is the data scientist?


As described in an earlier post here I outlined the fact that becoming a data scientist requires a lot of knowledge. Focusing back, a data scientist needs to have knowledge in different IT domains: General understanding of distributed systems and how they work. This includes administration skills for Linux as well as hardware related skills such as networking. Knowledge in Hadoop or similar technologies. This knowledge basically builds on top of the former one but it is sort of different and requires a more software focused knowledge. Great statistical/mathematical knowledge. This is necessary to actually work on the required tasks and to figure out how they can be applied to real algorithms. Presentation skills. All is worth nothing as long as someone can’t represent the data or things found in the data. The management might not see the points if the person can’t present data in an appropriate way. In addition, there are some other skills necessary: Knowledge of the

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