Difference Between Big Data And Deep Data

Big data has become an important topic for nearly every industry. The ability to study and analyze large sections of information to find patterns and trends is an invaluable tool in medicine, business and everything in between.

Employing analytics, the root of big data, in your business can lead to advances and discoveries that you might not see otherwise.

When it comes down to it, though, Big data hadoop Training in Madrid really a new concept. It’s simply taking data already available and looking at it in a different way. Deep data, on the other hand, may be the real tool that you need to change the world, or at least your industry.

What is big Data?

Big data is an amalgamation of all of the data collected by a business. The specifics will vary by industry, but generally, its information like customer or client names and contact information and other data collected over a business day.

Depending on the side of the business, this can be mind-boggling amounts of information, much more than it would be possible for a regular human to go through. And most commonly organizations use archiving software to push all this data in archives.

Businesses can employ predictive analytics to help sift through the data to find patterns and trends, but much of the information is often useless or redundant.

What is Deep Data?

Deep data is, in essence, taking the data gathered on a daily basis and pairing it with industry experts who have in-depth knowledge of the area. We’re talking exabytes or petabytes of data — much more than what could fit on a standard computer or external storage drive. Deep data pares down that massive amount of information into useful sections, excluding information that might be redundant or otherwise unusable.

What’s the Difference?

Big data and deep data are inherently similar, in that they both utilize the mass of information that’s collected every single day by businesses around the world. Companies can pair this data with analytics and use it to help predict industry trends or changes, or to decide what departments need to be investments or reductions in the coming year. So how are the two types of data gathering different?

The key is in the data analyzed.

Big data collects everything, down to the last insignificant zip code or middle initial. Trends can be found this mass of data, but it’s much harder to determine what is useful and what is just junk code. Deep data, on the other hand, looks for specific information to help predict trends or make other calculations.

If you want to predict which products are going to sell the best during the next calendar year, for example, you wouldn’t necessarily be looking at your customer’s location, especially if you sell online. Instead, you look at data like sales numbers and products information to make predictions. That’s the essence of deep data.

Deep data analysis applies to medicine and other similar fields as well. Focusing on one specific demographic, such as age, weight, gender or race, can help make trail participant searches much more streamlined and increase the accuracy and efficacy of drug or treatment trials.

Which one do you need?

Of the two options, is big data or deep data the best option for your business? That will depend on the kind of business that you run, the industry that you’re in, and the type of data you’re collecting. This is where you also need to set your priority right as well and understand where you want to plan your focus.

In general, though, when searching for specific trends or targeting individual pieces of information, deep data is going to be your best option. It allows you to eliminate useless or redundant pieces of data while retaining the important information that will benefit you and your company.

Big data and deep data are still both very useful techniques for any type of business. A data consulting firm can help you determine the best techniques to gather and process your data.

We are entering the age of big data, and it won’t be long before big data or deep data becomes a necessity rather than an option.

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