Many organizations have been eager to use new technology to make their big data dreams come true, but not all of them have been as prepared as they should be. These projects can put a strain on your network and storage. That’s why it’s not only practical but necessary to plan out and consider any requirements or limitations before deploying any sort of large-scale big data project. Here are a few tips to guide you through this:
Set a clear definition of your goals before you deploy
In all that excitement, organizations can become easily disorganized and lose sight of their aspirations for a project, slowing deployment and causing other IT-related issues. Vin Sharma, the Director of Strategy for Big Data Analytics/Data Center Group at Intel, told IT World that clearly defining the problem and the intended solution helps companies achieve a smooth deployment.
TechTarget explains that businesses must also determine other ramifications of the project, specifically the questions that must be answered, how much data is available and how much of it is needed, as well as what results need to be reported and to whom.
It’s true that these advancements in data and the Internet of Things are a revolution in the way systems operate, and that realization can cause organizations to bite off more than they can chew. These new projects often require a level of expertise and the right tools to be properly executed, and enterprises that don’t heed this advice can find themselves gathering little to no useful data, and they end up being really disappointed.
For this reason, there’s no need to dive into this technology so quickly. In fact, it’s better to take your time and really consider your needs and be as realistic as possible with them. TechTarget says that enterprises can analyze their own data to assess strengths, weaknesses and customers’ needs before embarking on a big data project.
Training is key
One of the biggest takeaways from these pieces of advice is that projects can be limited by the people who run them. For those who can afford it, data scientists are often the best people for the job, but that doesn’t mean you can’t have success in big data without them. It’s probably better if you can train existing staff with data warehouse and IT experience so more professionals understand how to operate these kinds of projects.
Human influence also means going into the project with the right mindset. You have to treat a big data project like a business asset, with proper management by IT staff and the right tools. It also means asking the right questions so you can collect useful data while still providing a service to your customers.