We work as advisors to enterprises that plan to implement or upgrade their Analytics Infrastructure. We work closely with management teams as well as their Consulting partners to chart out a milestone based roadmap that offers all stakeholders measurable progress. With our commitment to optimising TCO of such infrastructure cost benefit analyses will be presented by us at various process entry and exit points.
Selection of platforms is one of the most important aspects of Data Analytics with near term impact and long term ramifications. There are several factors we consider before we advise our clients for a particular platform.
We start our study of platform with the selection of the Distributed Data Hosting system. It could be either the client's server farm already in operation or a cloud hosted solution based on the IT policies of the client. Even among cloud based hosting solutions there are multiple platforms - we make a detailed study of whether it could the AWS or the Azure or IBM Softlayer.
The selection process for the Data Analytics platform is much more convoluted. We work with the client evaluate whether to adopt the Open source stack or a ready solution from SAS or a boxed approach of Cloudera or Hortonworks or the sophisticated suite from IBM. These again depend on several intricate factors that need to be evaluated in detail so as to arrive at a meaningful conclusion.
Our clients have unique requirements based on the vertical they operate in, the size of the enterprise, geographical diversity and above all the enterprise objective for Data Analytics. These call for custom made development which means dedicated resourcing from the service provider as well as the client.
Many a times the costs are a constraint especially for smaller organisations and enterprise initiatives that are in POC stage. With our commitment to creating value for the buck for our clients, we sometime provide a subscription based SaaS option to our clients that while not an ideal solution but provides a pragmatic alternative till traction is achieved.
Although new Data Analytics platform would form the standard for an enterprise decision making systems, the traditional data storage, data handling and data interpretation techniques and tools remain very relevant.
It is important to include these legacy systems as these apart from being a benchmark also need to be integrated at various application layers. A modular approach to such transitions is key to a successful implementation and many times an initial integration is required till a complete migration is implemented.
While advising our clients we also take into consideration the human resource capacity as the migration happens from traditional to modern distributed data analytics. Analysts, programmers and administrators are to be trained into the new system so that the programme efficiently utilises the existing resource capacity.
Therefore we highly recommend a training programme synchronised with the migration programme supplemented with an on demand "Migration" help desk.
The efficacy or success for a Data Analytics programme rests substantially on the scale of implementation of individual layers in the stack. A typical use case is the optimum number of nodes to be implemented in HDFS cluster to achieve a pre defined SLA in terms of computation throughput and data integrity.
During such a roll out a periodic dipstick approach is highly recommended. A complete test with real data at various implementation milestones and stakeholder reporting are required so as to obtain valuable feedback from all concerned teams. It also helps in driving adoption of the new platform and ensuring efficient migration.
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