The next Big (Data) Thing: Watch out for these Five Data Trends

The next Big (Data) Thing: Watch out for these Five Data Trends // Visual royalty-free @

Big is not enough anymore, it has to be huge. In order to keep track of the constantly increasing flood of data, new and extremely large data applications are needed. It is important to have the coming trends and opportunities on your screen now and to integrate them into a sustainable corporate strategy. We summarized the most important developments.

The amount of data generated daily increases exponentially. Current numbers from IDC predict, that the worldwide data volume in 2025 will be about 175 Zettabyte (175 billion Gigabyte). In 2018 it was “only” 33 Zettabyte. About 80 percent of the total amount is data that is stored in companies. Coping with the flood of data thus becomes the most important aspect of a holistic digitization strategy. The key: Big Data.

Big Data applications are already being used profitably in many business areas. However, the importance and requirements will increase enormously in the near future: “The size and complexity, the distributed nature of the data, the speed of action and the continuous intelligence required by digital business make rigid and centralized architectures and tools collapse. The continued survival of any business will depend on an agile, data-centric architecture that responds to constant change,” says Gartner vice president and analyst Donald Feinberg. From our point of view, five trends will be decisive for this in the future.

#1 Edge Computing and 5G Communication

The term Edge Computing refers to a mesh network of micro-data centers that processes or stores data locally and transfers it to a central data center or cloud repository. A single “Edge Computer” is not a new hardware part, but a “thing” connected to the network or Industrial Internet of Things: production machines and devices, automobiles and airplanes can all be regarded as “edges”. They provide data for ongoing operations on the one hand and for analysis and improvement, for example via digital twins, on the other.
Edge Computing is thus an infrastructure that allows enormous amounts of data to be processed “at the edge of the network” – hence the name – or as close as possible to the source and as quickly as possible in the process.

5G communication is an essential component of this process. Its higher speeds, lower latency and greater capacity make the IoT possible in the first place. And its machines, in turn, are best placed to take advantage of and optimize the features of 5G.
Bandwidth and speed of 5G will open up completely new possibilities, especially in industrial environments. For example, robots can be used more effectively and react faster in intelligent factories. Smaller and specialized on-site data centers will be able to make decisions faster and make better use of AI technologies.

#2 Big Data becomes Huge Data

The enormous amounts of data are also increasing rapidly due to new industrial sensors and Internet-capable wearables. According to the author Kevin Coleman, the terms have to be changed: “Big Data was a harmless era, the real data challenges are only now coming in the form of huge data”.

As a result, there are new challenges in data backup and storage: real-time data is imported every second by sensors and needs to be processed and stored. In the long term, we therefore need to find more efficient ways of storing this data. More and larger data memories, compressed data or machines connected for direct data exchange are possible solutions. However, new guidelines and regulations are also conceivable. For example, an “expiration date” for data could be legally agreed, from which point on it would have to be deleted.

#3 Immediate Data Processing via Persistent Memory Server

In the future, architecture capable of in-memory computing (IMC) will offer a good opportunity for “instant” data processing. New technologies for non-volatile or persistent memories will enable such architectures to be introduced cost-effectively and with a low degree of complexity.

Especially persistent storage – a new additional layer of storage between DRAM and NAND flash storage – will ensure cost-effective mass storage for high-performance workloads and have a positive impact on application performance, availability, boot times, clustering methods and security procedures. They can also help organizations reduce the complexity of their applications and data architectures by minimizing the need for data duplication.

#4 Continuous Intelligence Improves the Analysis Process

More than ever it is important for companies to carry out and evaluate analyses quickly. To do so, they need timely and uninterrupted information from all their available data. Regardless of the number of data sources or the size of the data volumes.

The key to this is Continuous Intelligence (CI). A modern and automated machine-driven approach, which enables fast and smooth access to current and historical data to accelerate required analysis and generate continuous business value.

According to Gartner, by 2022 more than half of new business systems will have continuous information that uses real-time contextual data to make decisions.

#5 Rethinking the Data Structure in the Company

Among all the listed trends, this is actually long overdue: In order to successfully meet the upcoming changed requirements, companies already need a holistic data structure. This must enable the exchange of data, which is usually available in distributed data environments and “data silos”.

To achieve this, organizations need the right data management: a unified and consistent framework for seamless data access and targeted processing across an otherwise isolated repository. This creates the decisive prerequisite for reacting as quickly as possible to changing market conditions and securing decisive competitive advantages.

We have developed a solution that provides companies with optimized data management support and exactly these functions. Our self-service Big Data platform enables departments to quickly and easily access all data pools within the organization and external data sources. Converted and stored ready to use. In addition, it has an interface to connected analysis and reporting tools in order to analyze and evaluate relevant data independently and at short notice.

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This post is also available in: German