… this is at least what I hear often. Basically, when talking to people that are data-minded, they would argue
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
Agility is an important factor to Big Data Applications. (Rys, 2011) describes 3 different agility factors which are: model agility, operational agility and programming ability. Model agility means how easy it is to change the Data Model. Traditionally, in SQL Systems it is rather hard to change a schema. Other Systems such as non-relational Databases allow easy change to the Database. If we look at Key/Value Storages such as DynamoDB (Amazon Web Services, 2013), the change to a Model is very easy. Databases in fast changing systems such as Social Media Applications, Online Shops and other require model agility. Updates to such systems occur frequently, often weekly to daily (Paul, 2012). In distributed environments, it is often necessary to change operational aspects of a System. New Servers get added often, also with different aspects such as Operating System and Hardware. Database systems should stay tolerant to operational changes, as this is a crucial factor to growth. Database Systems should support
Whenever we talk about Big Data, one core topic is often not included: Data Quality. If we Data, all the Data doesn’t really help us if the data quality is poor. There are several key topics that data should contain in terms of quality. Relevance – Data should contain a relevant subset of the reality to support the tasks within a company. Correctness – Data should be very close to reality and correct. Completeness – There should be no gap for data sets and data should be complete as possible. Timeliness – Data should be up-to-date. Accuracy – Data should be accurant to serve the needs of the enterprise. Consistency – Data should be consistent. Understandability – Data should be easy to interpret. If it is not possible, data should be explained by metadata. Availability – Data should be available at any time.