The true value of Big Data lies in the data intelligence that can be extracted from it. This revolves around the potential of your existing data and how its ROI can contribute to your business value. Conversely, data studies can in turn lead to new business insights and strategies.

Cloud delivery models offer exceptional flexibility, allowing you to offer IT on demand in all kinds of innovative models such as PaaS, SaaS and IaaS.

In order to explore Big Data and uncover connections and patterns (‘data intelligence’) we use a number of advanced technologies to process heterogeneous data. In this context, using a cloud infrastructure can be useful because:

  1. It is best not to invest in data infrastructure when still performing experimental data research.
  2. Some cloud platforms include data services, which can be a useful alternative to internally developed solutions.

Experimental data research

Limitations aren’t conducive to experimentation. Because handling a diversity of data systems with own infrastructure can often be complex and expensive, cloud models can help to accelerate and create scalable analysis solutions.

Making use of an Analytics as a Service (AaaS) solution for your experiments can quickly increase your options. Your choices will often depend on things such as:

  • Required computing power
  • Costs
  • Security of the data
  • Integration

Public Cloud services can be used to provide increased scalability.

Data services on cloud platforms

By developing a comprehensive cloud-based data strategy you can quickly optimise your company’s data experiment investments. This will allow you to start with a full range of analysis options, which you can then refine as your insights increase.

A cloud-based data analysis platform will quickly provide you with the following important capabilities:

  • capture and integrate structured and unstructured resources
  • manage and monitor data based on your company’s own governance and data policy
  • perform analyses, transformations and visualisation to deliver the right information at the right time and in the right place

Well-known examples include:

  • Amazon Elastic MapReduce
  • Google BigQuery Services
  • Windows Azure HDInsight

 Cloud platform or in-house solution?

Within our approach to data intelligence we operate at the interface between customers, business and the various technological possibilities available. Whether you should choose a cloud platform or an in-house solution will be determined by:

  1. Your requirements and the extent to which you need the whole range of analysis capabilities.
  2. The various solutions available for cloud-based analyses.
  3. Whether your frameworks and solutions are continuously adjusted. In the constantly-evolving market it is important to avoid vendor lock-in, which can get in the way of future developments.
  4. Data governance and organisation issues.