5 Helpful Tips for Big Data Efficiency

man looking over analytics for big data efficiency

With billions of IoT devices in use, now is an excellent time to increase your big data efficiency in the data your business is generating.

The Internet of Things is here to stay. With billions of connecting devices in use, now is an excellent time to increase your big data efficiency in the data your business is generating. These technologies — which include wearable health monitors, smart shop signs, city energy meters, and other similar devices — rely entirely on highly tuned big data to function properly.

Data that is not organized results in unreliable datasets, insights, and gadgets. As a result, incorrect business decisions are made. In addition, users and consumers suffer as a result of this situation.

The IoT, for example, is being used by modern medical practices to extend in-home healthcare. However, the monitors in these homes must be 100 percent dependable in order to give appropriate care to patients. Furthermore, in order to precisely report usage and transfer resources, IoT meters in smart cities require totally trustworthy data from their sensors.

When it comes to big data efficiency — whether for smart city applications or for your everyday business decisions — the process is as difficult as it is vital. One of the challenges of managing big data is its complexity. Other challenges include limits on access to data lakes and the requirement to extract value as rapidly as possible. In addition, there is the challenge of the inability to convey information quickly enough.

1. Eliminate Process Latency.

Latency in processing occurs when you retrieve data from traditional storage models. These storage models, however, move slowly when retrieving data. Therefore, it is possible for organizations to save processing time by switching away from slow hard disks and relational databases. Instead, they can implement in-memory computing software. Apache Spark is a common example of an in-memory storage model. Making this switch limits process latency and allows for greater big data efficiency.

2. Make use of data in real-time.

The purpose of real-time data is to reduce the amount of time that elapses between an event and the actionable knowledge that may be gained as a result of it. Therefore, organizations should attempt to reduce the delay between insight and benefit as much as they can. They should do this in order to make better decisions. Companies can execute real-time data analysis with the help of software packages. When they do this, they are able to take the best advantage of the real-time data. This is a powerful, modern tool for businesses.  It allows them to make up-to-the-minute business decisions that can impact sales and growth in more ways than ever before.

3. Perform an analysis of the data before taking action.

It is preferable to examine data before taking action on it. You can do this through the use of a combination of batch and real-time data processing technologies. For years, companies have been relying on historical data to identify and analyze trends. However, the availability of current data — both in batch form and in streaming — now allows them to identify and analyze changes in trends as they occur.

This is a giant leap in business-enhancing technology! A comprehensive set of up-to-date data provides businesses with a larger and more accurate perspective. In addition, it gives them a competitive edge in a world where the competition is more fierce than ever.

4. Transform Information into Decisions

Innovators are constantly developing new methods of data prediction for the benefit of businesses. Much of this happens through the use of machine learning. The fact of the matter is that managing massive amounts of big data that any enterprise must deal with would be impossible using old methods. Without the use of big data software and service platforms, this job would never get done.

Using machine learning, you can transform large amounts of data into trends. You can then examine those trends and use that insight to make high-quality decisions. Therefore, organizations should take advantage of this technology. They have the opportunity to use it to the best extent possible in order to increase their big data efficiency.

5. Make Use of the Most Up-to-Date Technology.

The technology of big data is continually changing. An organization’s ability to maximize its big data efficiency to the fullest extent depends on its ability to stay up with technological advancements.

Having the ability to jump from one platform to another is dependent on minimizing the friction that can occur. As a result, data will become more versatile and adaptable to the next generation of technology.

Image credit: Karolina Grabowska; Pexels