It has been predicted by International Data Corporation (IDC) that the revenue of Big Data worldwide will surpass $200 billion by 2020. It has the potential to offer remarkable insights or completely overwhelm the users. In the end, the choice lies with the user. It is based on the decisions that are made before even one bit of big data is collected. The biggest problem is that big data is a technology solution which is used by technology professionals but the best practices are related to business processes. To combat this, the top big data practices are:
• Defining the big data business goals- Big data goals can be defined by gathering, analyzing, and understanding the requirements. More often than not, IT gets distracted by the Hadoop cluster. The most important step to take before even leveraging big data is to understand the business requirements and goals. The users have to make it clear about their desired outcome and results; otherwise, there will be no target to aim.
• Asses and strategize with partners- A big data project must include data owner, possibly an outsider which can either be a vendor providing Big data or a consultancy that will bring an outside set of eyes into the organization to evaluate the current situation.
• Determining what is worth having and needing in big data- A large number of data does not mean good data. The right kind of data might be mixed somewhere and the user might fail to find it. The more haphazardly data is collected, the more disorganized and in the varied format it is. It is important to determine what is missing before a project is kick-started. It is not always possible to know the data fields in advance; hence the user needs to make sure to be flexible as the project advances.
• Continuous communication and assessment- Effective collaboration requires on-going communication between stakeholders and IT. Necessary changes must be communicated with IT if project goals change midway.
• Leveraging big data by starting slow and gaining fast- The first big data project should be started with a proof of concept or a pilot project that is relatively small and easy to manage. There is a learning curve associated with this and also, big data solutions should not be forced upon if the problem does not need it.
• Evaluation of big data technology requirements- The fact that how much the big data is understructure is itself overwhelming. According to the reports, it is as high as 90 percent.
• Aligning with big data cloud- Big data means that a lot of data needs to be processed and the user needs to be careful with using cloud since it is metered. The public cloud can be provisioned and scaled up instantly. Using data subsets and the many tools offered by the cloud providers with the likes of Amazon and Microsoft, a user can set up a development and test environment within hours and use it as a testing platform.
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