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5 Tech Trends to Keep Your Eye On

Dylan Salisbury

Many factors are contributing to greater pressure on the high-tech industry, from exponential data growth, to higher-profile and more frequent data breaches, to antitrust investigations and legislative pressure.

As technology evolves at an ever-quickening pace, it’s important to keep an eye on trends to anticipate how they could affect your business.

Whether you are part of a legal team, if you serve clients at tech companies, or you use technology yourself, keep these trends top of mind in the year ahead.

An evolving relationship with distributed cloud

What it is:

Some organizations who move from legacy systems to cloud storage choose to implement a hybrid public/private cloud solution. Due to various factors, such as cost or partnership obligations, many organizations are opting for multi-cloud solutions, where they use multiple providers such as Google Cloud, Microsoft Azure, and AWS as part of a single architecture, with their data stored across several locations.

What it means:

As multi-cloud use proliferates, the nature of enterprise organizations’ relationship with the cloud will continue to evolve. As companies move to the cloud, they must be mindful that what they are moving is not necessarily cloud native. If organizations ignore problems that existed with legacy platform data as part of the migration, those problems will still exist after the migration.

Furthermore, more broadly distributed data presents security implications. If organizations don’t address fundamental security or structural issues as part of a cloud migration, those issues could follow them. For example, most cloud systems offer levels of information protection that prevent specific uses, such as opening and printing. Unfortunately, these solutions typically do not support lifting these restrictions on export, so users have to come up with new workflows to address those issues after they’ve migrated the data.

The Internet of Things and edge computing

What it is:

Edge computing allows data to be analyzed and stored near where it is collected rather than being transferred to a data center or a centralized cloud. That way, data can be analyzed in real time, and devices are more responsive. The emerging Internet of Things (IoT) will add billions of edge computing devices, each individually contributing data processing and data management effort into what is called the intelligent edge.

What it means:

With all these new devices producing a treasure trove of data, people are just starting to define how to capture and analyze it in the most meaningful way. With many new sources of data to collect and a variety of structures of these data sources, this has the potential to reshape the way e-discovery is conducted. This also presents grave security implications; a typical organization like a big bank may have a robust firewall with significant resources monitoring it, whereas many edge computing devices may have no one monitoring and be wholly vulnerable, putting entire networks at risk.

5G and the explosive growth of data

What it is:

The fifth generation of cellular networks promises an infrastructure that will allow a massive increase in the amount of data that can be carried over short distances on mobile networks. Once these infrastructures are built, it will allow for many more devices to communicate and interconnect than ever before, bringing new possibilities to areas like the IoT, smart cities, autonomous vehicles, and telemedicine.

What it means:

For the first time, your mobile device will have comparable connectivity to your hard-wired office device, which will create explosive growth in the amount of data coming from mobile devices. All that additional data will need to be collected, analyzed, and stored, which will have far-reaching implications for the nature of data storage in the future. Organizations may be responsible for collecting, holding, and storing unprecedented volumes of data. The tools to do this efficiently will need to be invented and deployed in the coming months and years.

Standardization in the preparation of data

What it is:

Data standardization is the process of aligning on a standard way to interpret and record similar types of data. The goal is to bring balance and greater operational efficiency to enterprise-wide integrated systems. A consistent format helps to streamline the ability to clean large amounts of data and make it more actionable. As more and more systems are producing disparate sources of data and becoming more interdependent on that data this need will become more pressing.

What it means:

You can’t really do any analysis if your data is in different formats. Standardization also has particularly important implications for active learning and AI systems, which become more effective as it inputs and interprets more data. We will need to be ever vigilant of the changing data standardization trends and ensure that we are adapting the ways in which we collect, process, and store data to reflect best practices.

AI active learning and security

What it is:

As the stakes involved in protecting your data and digital infrastructure continue to grow, so does the potential reward for nefarious actors. According to Accenture, globally $5.2 trillion is at risk of cybercrime over the next five years, and there has been a 72 percent increase in the actual cost of cybercrime over the last five years. More and more emphasis will need to be placed on elevating cybersecurity from a specialty owned purely by IT and instead to its own discipline. AI and active learning promise to up the game as well, by providing more sophisticated defenses, the ability to anticipate attacks, and a guard against outside AI looking for vulnerability.

What it means:

This trend presents an opportunity for organizations to be more strategic in how they apply their human resources. In fact, it will not be possible to keep up with the rising mountain of data involved in security review and investigation without active learning. Active learning could play a fundamental role in helping to review security incidents. For example, in Relativity, as an investigator starts to identify points of interest related to a security incident, active learning will serve up increasingly targeted data, upskilling that individual’s capabilities manyfold. With enough data, active learning may even be applied to anticipate locations or instances of greater vulnerability and be able to prevent security incidents rather than just respond to them.

It’s important to be aware of changing tech trends so you can ensure that you are up to date on the best solutions. We will continue to monitor these issues and more—we are always on the lookout for ways to help our users find the quickest and most reliable path to the truth.

Dylan Salisbury is a product marketing manager at Relativity, where he specializes in understanding and serving the corporate community.

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