Three Most Practical Big Data Use Cases Of 2018
The evolution of Big Data technology touches every industry that uses big amount of information. In 2018, it grabs even more attention as a way to process data faster and stay afloat in a competitive market. Even companies that underestimated the importance of big data are now doing a research in this direction.
This technology is evolving very fast so it is hard to predict the future of Big Data for a long period, but we can notice the most practical data cases for 2018: The Internet of Things, Insurance, and E-commerce. Each of these fields is using this technology right now and going to do it more and more to get better results. Let’s take a look at each of them.
Internet of Things
Now consumers can use many voice-based services but there are still many things that don’t support this technology. Experts say that enterprise will make more efforts to provide different voice-based services to their customers.
The hardware side of IoT is also going to grow rapidly. With the need to process data faster and optimize this process, there comes a need for fast and stable hardware. Due to these changes, companies will require more and more devices to use them for Big Data projects.
The big amount of information is already commercialized, especially in the United States. It is predicted that this amount will grow so more and more enterprises will commercialize the information they gathered by different methods.
For now, most of the analysis processes occur in clouds or remote data centers. Experts say that enterprises will pay more attention to running analysis on local devices. The edge Internet of Thing devices is able to work locally based on the content they generate.
Now let’s move to the next case.
It is predicted that insurance companies will gather more information from external sources, for example, geolocation and demographic information. It helps them to make underwriting decisions more accurate and offer greater pricing granularity.
How can it be helpful? The usage of Big Data will help companies to increase the customer loyalty. How does it work? By gathering the information systematically and using AI to process it, many insurers can play different scenarios digitally before implementing them on their actual clients. The analytics help to recognize which policies are reaching maturity and are the most possible ways to make customers reinvest.
Personalization plays a huge role in the insurance field. Each customer has their own needs and they want the company to fulfill these needs. The usage of effective analysis methods helps to create a unique offer for each client, even if there is a lot of customers with different requirements.
Even those insurers who have a lot of data often don’t know what to use and where to look for necessary information. What does it mean? Such technologies help them to use the more intelligent search tool and find the needed information quickly.
Insurance always needed a personal approach to each client, analyzing many of different parameters, and making difficult calculations fast. With the modern Big Data tools and technologies, machine learning BI providers such as https://www.inetsoft.com/ – all of this become possible.
Business owners already make efforts to gather as much data about their shoppers as they can. In 2018, they are going to collect even more information from different sources and make it analysis more and more effective to predict the needs and behavior of customers.
What should we understand? The big problem for companies is that their clients don’t like to report about the bad service – it makes it harder for companies to find issues with their customer service and fix them. This is something many companies face. Big Data helps to improve customer service by getting more information on clients’ experience and how much they are satisfied.
Customers also don’t like to pay online if the available payment methods are difficult to understand or seem to be unreliable. What does it mean? That is why companies are going to use Big Data for making their payments safer and easier for clients. For example, if you need to order a big data case study or get help on essay writing, you will be able to order cheapest help with essay here and do it in a few click. This should simplify many processes. When using intelligent analytics in the E-commerce field, it will be possible to identify threats fast, detect money laundering transactions, and increase the overall security of different transactions.
The number of smartphone users keeps increasing, as well as the share of transactions made from mobile devices. It means that companies will be even more oriented on the small screens market. They have many tools to collect the information from mobile devices so companies will need platforms for processing this data fast. Many websites, such as Google, give preferences to this field because most of the search activity goes from small screen devices.
There are many other effective cases of uses of Big Data. With time, there will be more and more companies that use this technology so everyone should be ready for it. It also means that these companies will invest in their research in this field and other technologies connected with it.
The World of Big Data
It’s hard to grasp just how “big” the world of big data is becoming, but there are a few figures to put it into perspective.
In 2014, big data was just an $18.3 billion market. That number will reach well over $90 billion and continue to climb as new ways to apply big data emerge.
Data is being generated faster than we can keep up with. As a matter of fact, every human with a smartphone will generate 1.7 megabytes of new data every second by 2020. You can thank the internet of things for this explosion of data.
But for the most part, big data is still mysterious. Latest research tells us that only 3 percent of big data has been recognized – with only 0.5 percent analyzed and ready to be applied.
So, while big data will certainly unveil new opportunities in the future, it’s important to understand the data first.