5 Ways to Optimize Your Big Data

The promise of IoT is becoming a reality. With 9.7 billion connected devices expected to be in use by 2020, now is the time to start optimizing your organization’s big data. These devices — including wearable health monitors, city energy meters, smart retail signage, and more — rely completely on highly optimized big data.

Unorganized data leads to unreliable datasets, insights, and devices. This leads to poor business decisions and, ultimately, causes users and consumers to suffer.

Modern medical practices, for example, are using IoT to expand in-home healthcare, but the monitors being used in homes need to be 100% reliable in order to provide accurate care. Additionally, smart city IoT meters need completely trustworthy data in order to report usage and deliver resources accurately.

The process of optimizing big data — for smart city applications or for your daily business decisions — is as tricky as it is necessary. The complexity of the technology, limited access to data lakes, the need to get value as quickly as possible, and the struggle to deliver information fast enough are just a few of the issues that make big data difficult to manage.

1. Remove Latency in Processing

Latency in processing occurs in traditional storage models that move slowly when retrieving data. Organizations can decrease processing time by moving away from those slow hard disks and relational databases, into in-memory computing software. Apache Spark is one popular example of an in-memory storage model.

2. Exploit Data in Real Time

The goal of real-time data is to decrease the time between an event and the actionable insight that could come from it. In order to make informed decisions, organizations should strive to make the time between insight and benefit as short as possible. Apache Spark Streaming helps organizations perform real-time data analysis.

3. Analyze Data Prior to Acting

It’s better to analyze data before acting on it, and this can be done through a combination of batch and real-time data processing. While historical data has been used to analyze trends for years, the availability of current data — both in batch form and streaming — now enables organizations to spot changes in those trends as they occur. A full range of up-to-date data gives companies a broader and more accurate perspective.

4. Turn Data into Decisions

Through machine learning, new methods of data prediction are constantly being born.

The fact is, the vast amount of big data that each organization has to manage would be impossible without big data software and service platforms. Machine learning turns the massive amounts of data into trends, which can be analyzed and used for high-quality decision making. Organizations should use this technology to its fullest in order to fully optimize big data.

5. Leverage the Latest Technology

Big data technology is constantly evolving. In order to continue optimizing its data to the fullest, an organization must keep up with the changing technology.

The key to being agile enough to jump from platform to platform is to minimize the friction that can occur. Doing so will make data more flexible and more adaptable to the next technology. A great way to minimize that friction is by using Talend’s Data Fabric platform.

Talend’s Data Fabric platform helps organizations bring software and service platforms, and more, together in one place.