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Key strategies of a Big Data project implementation
Focusing on Big Data and analytics to create a real competitive advantage requires a structured and effective approach to collect, clean, correlate and analyze all of the gathered data. To facilitate the task, we’ve gathered three key strategies of this process to ensure the success of your Big Data project implementation.
Identify and collect important data for your business:
Above everything in Big Data project, it’s essential to target information to be collected in order to ensure a significant return on investment. Avoid investing in information process which cost can be higher than their potential value.
While benefits are being experienced and are expected from Big Data projects, there are complexities that organizations need to consider. The amount of data organizations have access to is expected to continue to increase. The more data there is in different forms (both structured and unstructured); the more complex processes involved to ensure that analysis of the data is comprehensive, meaningful, and useful.
An innovative approach exists and allows, from a graphical interface, easy to use, integrate data of any kind, transform and manage quality, natively in Hadoop clusters. By overcoming the complexities of programming under Hadoop, IT can respond more quickly and less costly to business demands.
Manage data quality:
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Cleaning data while using or storing it in huge repositories is hardly effective. It is best to complete this step “live”: when transactions are in progress within the systems, for example when a user clicks “OK” on your web site, or an RSS feed notifies you a new message on a blog.
Correlate information from different sources is also essential. For example, you can greatly optimize your marketing actions knowing that “tcook 1968” which visited your website is the same person as “Thomas Cook” that made a credit card payment by phone last month, and visited your physical store by presenting himself as “Thomas Cook”.
Create a data quality Firewall can help you ensure the quality of data, before the gathered data spread to other departments of your company. This firewall will improve business processes, reporting and will optimize the correlation and management analysis of Big Data.
Analyze and disseminate information:
Business users are now seeking to free themselves from complex solutions, often imposed by the IT department and poorly adapted to their daily needs. The self-service analysis tools today allow many employees to explore their data, to produce content with high added value, and share it across the organization or beyond it.
This agility, access and manipulation of data, however, must not go against the company’s governance rules. For this reason, it is essential to have a platform with security and a unique repository to handle all of the valuable data chain.
The self-service analysis, through a portal or an application, should not overcome information delivery capabilities to a large number of recipients, regardless of the medium (smartphone, tablet) a schedule or updated in real time.
Organizations that do not create Big Data projects in an organized, future-looking way may find that their solutions become more challenging and less appropriate to use over time.