IBM Software Whitepaper: Choosing a Big Data Technology Stack for Digital Marketing

ATS London Big Data sponsor IBM has just released an in-depth whitepaper addressing the issues surrounding Big Data. As everyone knows, Digital marketing has the potential to extract, interrogate and leverage large volumes of data. The challenges around high-cardinality in key variables, an increasing focus on open-ended analytics, structured versus unstructured data streams are becoming more and more complex.

However, they are also challenges for which there are an increasing number of interesting and applicable technologies that provide the real potential for a long term solution. Finding the right solution involves more than a simple evaluation of price/performance—and not just because measuring performance is inherently ambiguous. It involves the usual work of matching business requirements to the comparative advantages of each possible solution.

To do that matching, you need to understand in some detail the actual applications and use cases within digital marketing that are at the top of your priority list. From enterprise dashboarding to site personalisation and even to call center optimisation, the challenges to the technology stack and the decision factors are very different.

Matching the key decision factors for each type of digital marketing use case to the technology solutions you’re considering, should make it easier to understand the appropriate big data technologies for your enterprise. This process should also help you understand the appropriate mix of those technologies. In addition, the mapping of application factors to technology solutions should help highlight areas where the platform is likely to struggle or presents particular risks.

The whitepaper is certainly worth reading. It can be downloaded here. Topics covered:

Digital marketing, analytics and the big data world:
– The challenge of choosing the right technology stack
– Cardinality as a key factor in aggregation
– Lack of meaning in key metrics and aggregations
– Need for deeper analytics beyond just reporting

Challenges of digital analytics:
– Streaming nature of the data
– Difficulties in joining streams

Key decision vectors:
– Handling huge data volumes by minimising data modeling and easy data integration
– Support for integrated marketing solutions, BI tools, advanced analytics and procedural/algorithmic queries
– Minimise administrative overhead and costs
– Uptime and load without disruption

Common digital marketing use cases:
– Advanced web, loyalty programme, merchandising, advertising and social media analytics
– Customer personalization
– Attribution modeling
– Email targeting
– Site personalisation
– Enterprise dashboarding
– Call avoidance and operations

The use of the phrase ‘Big Data’ will inevitably continue to cause confusion and often be misused. However, the trend to accessing and creating lasting value through the applications of advanced analytics is going to continue. Choosing the right solution is not an easy decision and oft times not inexpensive. However, this whitepapers provides some pointers on what needs to be considered when making that decision.