Analytics firm CB Insights has released a list of 16 enterprise IT trends that it believes will dominate in 2019.

CB Insights broke the list down into the following four categories:

  • Necessary: “Trends which are seeing widespread industry and customer implementation / adoption and where market and applications are understood. For these trends, incumbents should have a clear, articulated strategy and initiatives.”
  • Experimental: “Conceptual or early-stage trends with few functional products and which have not seen widespread adoption. Experimental trends are already spurring early media interest and proof-of-concepts.”
  • Threatening: “Large addressable market forecasts and notable investment activity. The trend has been embraced by early adopters and may be on the precipice of gaining widespread industry or customer adoption.”
  • Transitory: “Trends seeing adoption but where there is uncertainty about market opportunity. As Transitory trends become more broadly understood, they may reveal additional opportunities and markets.”

Learn more about each trend by reading CB Insights’ full report here.

SEE: Digital transformation in 2019: A business leader’s guide to future challenges and opportunities (Tech Pro Research)

Here are the 16 enterprise IT trends for 2019, according to CB Insights.

Necessary technology

SD-WAN: Software-defined wide area networking is more flexible, provides better performance and bandwidth, and allows remote locations to connect to internal networks. It uses public internet with an added layer of end-to-end encryption, and is expected to grow in the coming years with the rollout of 5G networks.

Businesses with investment in the Internet of Things (IoT) should consider investment in SD-WAN technology in 2019—it’s expected to be an $8 billion industry by 2021.

Time series databases: Simply put, time series databases (TSDB) are databases of time-stamped entries used for tracking changes in data over time. They have been widely used by Wall Street and financial firms for some time, but are expected to grow in popularity in 2019 and beyond thanks to the glut of data coming from IoT devices, most of which is time stamped.

Businesses that rely on IoT data should consider investing in TSDB technology, as the data being received is likely already time stamped and can be interpreted more readily with a TSDB.

Service mesh: Microservices are everywhere in the modern enterprise world: These small blocks of code work together to form one large application, with each microservice running from its own isolated container. Service meshes are the operational layer on top of a group of microservices. They automate much of the security, monitoring, and communication between microservices to give administrators the freedom to do other work.

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Businesses that make use of microservices should consider investing in service mesh architecture to make their services and applications safer, quicker, and more reliable in 2019.

Experimental technology

Multi-cloud management: It’s increasingly common to find more than one cloud service used in a single enterprise-level business, which can make managing data and software tricky. Luckily there’s an emerging market of multi-cloud management platforms that integrate services like AWS, Google Cloud Platform, and Microsoft Azure under one control console.

Multi-cloud management can lower costs, eliminate vendor lock-in, and improve performance. If your organization uses more than one cloud platform, consider adopting a multi-cloud strategy that includes a multi-cloud management platform.

Stateless hyperconverged infrastructure: Hyperconverged infrastructure (HCI) has eliminated many bottlenecks in computing, but it comes with a major drawback: Each node exists in an independent state, meaning it’s storing its own unique set of data. Stateless HCI utilizes a shared data server so that no node is in possession of unique data. If it fails another node simply picks up where it left off.

If your organization uses HCI, take a look at new stateless platforms—they can make HCI cheaper and more reliable than ever before.

ASICS and ASSPS: Application-specific integrated circuits and application-specific standard products are creating a new wave in computing that is cheaper, performance driven, and more energy efficient. Both involve the use of chips that are built for one particular purpose, like mining cryptocurrency or building IoT devices. Because the non-application specific parts of the chipset are jettisoned in favor of only the most essential hardware, these chips do their job better than any general-purpose device ever could.

Businesses investing in IoT hardware, either as an internal tool or as a consumer product, should look into ASICS and ASSPS, sincethe IoT continues to grow and demand for these specifically-built machines will grow as well.

Quantum cloud computing: Quantum computing is still an emerging technology, but it’s one with a lot of promise for its incredibly computational power. CB Insights predicts that the growth of cloud computing in the enterprise means that quantum computers will most likely be a cloud service in the near future, instead of on-premise machines. IBM and Rigetti Computing already offer cloud-based quantum computing services, and other companies are expected to follow suit.

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Threatening technology

HTTP/3: The Google-developed HTTP-over-QUIC has been renamed HTTP/3, and promises to shorten connection establishment times, reduce bandwidth congestion, and improve latency when it is rolled out in 2019.

It may also make traffic more secure, as QUIC can connect to a server and encrypt a request in a single transmission.

Augmented business intelligence: Businesses have a growing amount of unstructured data from a variety of sources, and augmented business intelligence (ABI) technology wants to use artificial intelligence (AI) to make that data useful.

ABI products from companies like Microsoft would analyze a business” unstructured data to make it usable when an employee asks questions about high-level metrics, team performance, or other data that a business has, but isn’t necessarily usable without a trained AI model able to find, analyze, and present it.

Nanosegmentation: This emerging new security technology is being developed to solve security issues that have arisen in the way microsegmented apps communicate with each other. Nanosegmentation maps internal communications between microservices and creates a whitelist of what’s allowed. Anything outside of its map is automatically rejected.

CB Insights predicts that, if nanosegmentation improves security for even a fraction of the microserices market, its popularity could outpace that of microsegmentation.

Workflow automation: Job automation isn’t only affecting manual labor jobs—CB Insights said it’s now moving on to “the automation of human cognition,” which means it’s coming for white collar jobs as well. Employees performing tasks like data entry, document review, transaction processing, customer service, and other repetitive tasks could find themselves out of a job in coming years.

CB Insights also predicts a growth of automation in fields like procurement, insurance underwriting, and legal services as well. Look for workflow automation to be a growing trend in 2019.

Modular edge data centers: Usually the size of a shipping container, and generally placed near cell towers or industrial facilities, modular edge data centers are designed to fill the gap between cloud services and small pieces of hardware that can’t support onboard processing.

Transportation, healthcare, manufacturing, agriculture, and utilities companies will likely hear more about modular edge data centers in 2019, and should consider whether investing in this emerging technology would make their processes smoother.

Data lineage: This practice involves recording the origin, movement, and modification of sensitive data. It’s already commonly used in the healthcare industry, and the EU’s new GDPR requires it.

Adopting a data lineage practice can help improve data quality as well—having major changes time stamped and logged can help analysts understand more about changes in a dataset over time, which can be beneficial in any industry.

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Transitory technology

Graph databases: This new form of database is a new type of NoSQL database and is ideal for organizations building social networks, recommendation engines, and knowledge graphs. They don’t use SQL and are ideal for unstructured data since they can show relationships and connections between data, which SQL databases can’t do. NoSQL databases also scale horizontally, which means that instead of adding more computing power to handle a larger database (in the case of SQL), more servers are added instead.

Companies like Amazon, Google, and Facebook are already using NoSQL graph databases, and CB Insights predict that graph databases could soon become the NoSQL database of choice.

Serverless computing: Those familiar with serverless computing likely know of AWS’ Lambda, which is a serverless computing model. Lambda, and other serverless computing services, involve using a small bit of code for a specific function which is only spun up when it needs to be used. By opting not to devote the resources of an actual server (whether cloud or local) to a specific, repetitive task, businesses can save a lot of money.

Serverless computing is the highest growth public cloud service, and CB Insights expects it to continue to be popular in 2019.

Cold storage: While it isn’t a new concept, cold data storage is a growing market that CB Insights expects to reach $213 billion by 2025. IoT devices, sensors, and other forms of data collection are growing at an explosive rate, and much of that data is useful, but doesn’t need to be accessed on a regular basis.

Storing important, but infrequently used data, on physical media like CD-ROMs or magnetic tapes is becoming more popular with the growth of data collection, and will likely continue to do so in the near future. If your organization is collecting a lot of data be sure to consider a method of cold storage, which can save a lot of money in operational costs by offloading data and storing it a non-energy consuming format.

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