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The air cargo industry and Big Data are made for each other. In fact, it is hard to think of any other industry that can profit as much from the business intelligence that Big Data can bring. The only question, then, is how to use that information to measurably drive profits.

With this goal in mind, here are five key actions to take as you develop or refine your big data project.


1. Define an Overall Big Data Strategy

Volume, velocity, and variety – these are the “3Vs” that are widely used to describe the inherent qualities of Big Data. There’s a lot of it, and it flows into an enterprise quickly and in a very wide variety of formats.

To avoid being overwhelmed by the 3Vs, it’s important to have a strategy that determines your end goals, your success metric milestones, and the parameters of each project. Think big. It’s far too easy to fall into the practice of warehousing data and using it only to confirm decisions after they have been made. You’ll get more value from your Big Data if you use it to drive smarter decision-making. Your projects will probably be defined by more immediate goals, but the utilization of predictive analysis to drive business intelligence and planning should be part of the parameters of any Big Data initiative — predictive is where the true value of Big Data lies.


2. Choose an Area of Focus

If you are just beginning to work with Big Data, or want to retool your current usage of analytics, start by determining a single area of focus – one segment of the business where profits can be increased through the use of data analysis.

Perhaps you’d like to improve operational efficiency by optimizing resource consumption, improving processes and performance, and enhancing scheduling. Or you might choose to enhance customer experience by optimizing customer service, segmenting to provide more precise targeting, and offering customers valuable self-service options that in turn will provide your organization with yet more useful data. A third area of focus could center on developing new business models that will increase revenue from existing services, or develop new offerings in order to create new, sustainable revenue streams.


3. Govern Your Data

If you’re just getting started with Big Data, you’ll need to locate the data that is already available in the company. This can best be done by mapping the flow of data into and through the company, along with the places where it is stored (this map will be useful for auditing your compliance efforts as well).

Depending on your data analysis solution, you may also have to structure the data, mapping its attributes across multiple databases, before you can use it for analytics. If your data analysis solution can handle unstructured data, you won’t need to worry about this.

Unstructured or not, you will need to create strong governance policies around data quality, including validation and cleansing processes. You obviously need to ensure your data is a reliable source if you’re going to use it to drive critical planning and decision making.


4. Secure Your Data

Data that is being processed must be protected at all times. Ensure that your security policies enable you to remain complainant with all applicable regulations, standards, and best practices.

Make sure your data analysis plans – particularly those involving shared assets or highly sensitive data – include protections from outsider theft and exposure to employees and third party suppliers.

IT will need to work out a security plan that provides the necessary balance of accessibility and security. Should you need to choose between robust availability and proper protections, you should always tip the scales toward the side of security.


5. Manage Change Wisely

Any Big Data project will, either immediately or eventually, draw on information collected across business divisions. The deeper and broader the pool of data, the more accurate your analyses can be. This sort of collaboration may not be part of your company’s culture, and so it may not come naturally. Along with the cultural change, you may also have technology silos that complicate the sharing of data.

While you’ll need people on staff who are skilled in drilling down and doing complex data analysis, it’s likely that the majority of staff members would benefit from data science skill training. Business and IT must work together to develop a data-driven company. You’ll want to on-board your partners, suppliers and customers as well – so consider what’s in it for them, and what benefits they will receive from participating in your data-driven initiatives.


According to the materials: Accelya

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