Digital data is one of the paradigm cases of big data.
The needs of digital marketing are one of the main drivers of new digital data management platforms at the enterprise level. Digital sources like websites, display data, and mobile applications create very large quantities of data. Some channels, like social media, go one better by churning out large quantities of fundamentally unstructured data.
At the same time, these data sources represent the new gold standard of digital marketing. Nearly all of the pertinent and observable marketing behavior of today’s customers is online. Traditional well-analyzed channels like direct mail and outbound calling are out-of-date. Consumers don’t respond to calls or direct mail at the rates they used to. Instead, they are rapidly transitioning to online communication.
The need to mine and understand digital data is critical and widely accepted among organizations, but it is a difficult process. The reasons for this include challenges around data volumes, integration, extract, transform and load (ETL), analysis and the wide array of possible technology solutions to choose from.
- Look at the reasons why analyzing digital data is challenging.
- Show why some of the attributes of a new generation of big data systems are responsive to these challenges.
- Use this to develop a set of key decision vectors for choosing an appropriate digital technology stack for your data.
- Lay out a series of the most common digital marketing data use cases and highlight the key decision factors for each.
- Discuss the decision framework as it pertains to big data
The goal of this paper is to provide today’s enterprise marketing and IT organizations with a well-defined framework for choosing a marketing digital technology stack.
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