In some work we have been doing recently we have been writing an overview of the Data Analytics / Big Data landscape in Ireland. Inspired by the Big Data Landscape diagrams by Dave Feinleib at forbes.com we have been playing with doming something similar for the analytics industry in Ireland. Rather than focus on technologies, as is the case in the Forbes diagrams, we have come up with the following categories of important stakeholders in the data analytics / big data industry:
- Analytics Consumers: companies that make extensive use Big Data / Data Analytics in order to make data driven decisions.
- Software/Hardware Vendors: Companies that develop and sell Big Data / Data Analytics and related software tools.
- Insight providers: Companies that sell packaged insight based on their own internal use of Big Data / Data Analytics.
- Consultancies: Companies that provide Big Data / Data Analytics consultancy (primarily to analytics consumers).
- Data Generators: Companies and organisations that focus on generating datasets used in analytics applications.
- Research Centres & Government Agencies: National research centres and government support agencies that are involved in the support of the Big Data / Data Analytics research and development.
Here is the model that we have come up with so far.
Rather than attempting to include a massive list of companies we have tried to include important representative companies in each category. In all cases we have focused on companies that have a significant presence in Ireland. In many cases there is a good argument to be made for a company to be placed in a number of categories – for example IBM develop analytics tools, are an extensive consumer of analytics and offer consultancy services – but we have placed each company int he category the best matches their core activities.
This is a first draft and there are lots of alternative ways in which we could have categorized the industry and different companies we could have chosen for each group. We would be very interested in talking to people about how we might modify and improve the model.