Tasks-Step 1: Gain executive-level sponsorship The projects that make use of Big Data must be proposed and rightly fleshed out. Without a dedicated team or a proper sponsorship, there are high chances...

Tasks-Step 1: Gain executive-level sponsorship
The projects that make use of Big Data must be proposed and rightly fleshed out. Without a dedicated team or a proper sponsorship, there are high chances that the project may not do well.
Step 2: Augment rather than re-build – Collect existing traffic information
It is always better to begin with the existing data rather than creating and exploring new data sets. The look-out for additional data sources is the next step in the process. Approvals would also be required in terms of the tools and techniques that would be applied.
Step 3: Make value to the customer a priority
It is important to gather and understand the requirements and the specifications of the customer. These need to be considered after the collection and prioritization of the data. Implementing strategies that do not suffice the needs of the customer would not be fruitful for the user as well as for the organization.
Step 4: Run an Agile shop and increment over time
Incremental releases and setting up of new data hubs is done once the project team and priorities are in place. It will aid in adjusting of the operations and would also help in understanding the ability of data to influence actions in the processes followed by the organizations. It is commonly seen that many of the projects do not pass because they attempt at covering everything in one go. It is suggested to take a slow start and then develop accordingly and adaptively.
Step 5: Link customer data to company process
Data driven decisions should then be implemented at the organizational level covering each and every single aspect that is involved.
Step 6: Create repeatable process and action paths
One of the hurdles to overcome when adding additional data sets is the desire to run one-off reports to answer interesting questions without connecting those answers to actions. Big Data shouldn’t mean data paralysis. Take a thoughtful approach to incorporating data sets. Ask team members what can be gained by adding the data set and what actions should be taken from the learnings. It’s crucial to clear a path for execution within the organization to prevent the data learnings from becoming just another interesting factoid devoid of connection to the customer or the product.
Step 7: Test, measure, and learn
It is necessary to test the assumptions with each data set rather than suffering from the surprises at a later stage in the cycle.
Step 8: Map data to the customer’s life cycle
It is of utmost importance to map the progress with the requirements as listed down by the customer. It will help to track the faults and lags, if any and would also help in validation and verification of the progress made.