Our era is characterized by the production of massive amounts of data, which increasingly drives enterprises to adopt a data-driven culture i.e. to base their processes and decisions on the collection, processing and analysis of datasets and that’s why most enterprises consider Big Data. Big Data refers to the collection and processing of data that is very difficult to capture, store and analyze based on the capabilities of state-of-the-art data management systems. These systems are characterized by the famous Vs (Volume, Variety, Velocity, Veracity), which differentiate them from conventional data processing systems.
Although many organizations refer to their enterprise data deployments as “Big Data”, only few really understand the term and its implications, while even fewer have managed to deploy it successfully. The deployment of Big Data systems is a challenging task, from both the management and the technological points of view. Enterprises must therefore try to understand the complexities and take proper steps.
Big Data is a non-trivial project, which must be proactively and carefully planned. First, there is a need to justify a Big Data deployment, by identifying the Vs in enterprise data and the importance of dealing with them towards improving business results. Identification of the data sources that will be exploited is also needed, along with a strategy for their gradual integration. At the same time, the data-driven processes that will be supported by the Big Data system should be identified.
A Big Data project is never implemented overnight: it requires a phased approach, which progressively deploys data sources and applications, along with data analytics of increased sophistication. As a result, a Big Data project management plan should make provisions for gradual deployment, including smooth migration of data from existing systems. As an early step, this planning may involve a pilot project, which will use a limited number of data sources in the scope of a smaller scale deployment. However, any Big Data project should be empowered by a proper architecture, which scales in a cost-effective way in order to efficiently handle the ever increasing amount of enterprise data.
One of the major management challenges for Big Data is the assembly of a proper team. Successful Big Data deployments ask for the effective collaboration of individuals with a wide range of skills sets, including database experts, computer scientists, programmers, data scientists, statisticians, business analysts and more. Despite the enthusiasm around Big Data, there is still a talent shortage in terms of competent people who can fulfill the above roles in real-life projects.
Management challenges stem also from the fact that Big Data requires the deployment of a pool of different technologies, which have to be orchestrated based on a disciplined Big Data architecture. Hence, CIOs, project managers and the management team have to monitor a challenging technology project, which involves complex procurement, deployment and integration processes.
Big Data is typically based on a pool of leading technologies including:
With a proper technology infrastructure at hand, the focus of a Big Data deployment can be put on devising effective analytical models for extracting knowledge. This is a structured process that typically involves the following steps:
Big Data projects are indeed challenging, but based on an elaborate planning and disciplined management they can pay off, leading to measurable return on investment and improved business results. To this end, there are already solutions and good practices that a company can explore together with its technology partner. Get prepared as you cannot afford to miss the Big Data train.
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