Bleeding edge information technology developments

What are some bleeding-edge information technology developments that a forward-thinking CIO should keep an eye on?

Here are a few emerging technologies that have caught my attention. These are likely to have an increasing impact on the world of business in the future. Consider which ones you should follow a little more closely.

Quantum computing

A recent advance in quantum computing that a Google team achieved indicates that quantum computing technology is making progress out of the lab and closing in on practical business applications. Quantum computing is not likely to change routine business transaction processing or data analytics applications. However, quantum computing is likely to dramatically change computationally intense applications required for:

1. Less hackable cybersecurity.
2. Lower risk financial investment modelling.
3. Higher capacity, lower weight batteries.
4. Accelerated understanding of chemical reactions.
5. More accurate weather forecasting and climate change modelling.
6. Progress with artificial Intelligence.
7. More efficient solar heat capture.
8. Superior materials discovery.

Since most businesses can benefit from at least a few of these applications, quantum computing is worth evaluating. For a more detailed discussion of specific applications in various topic areas, please read: Applying Paradigm-Shifting Quantum Computers to Real-World Issues.

Machine learning

Machine learning is the science of computers acting without software developers writing detailed code to handle every case in the data that the software will encounter. Machine learning software develops its own algorithms that discover knowledge from specific data and the software’s prior experience. Machine learning is based on statistical concepts and computational principles.

The leading cloud computing infrastructure providers machine learning routines that are quite easy to integrate into machine learning applications. These routines greatly reduce expertise barriers that have slowed machine learning adoption at many businesses.

Selected business applications of machine learning include:

1. Reduce risk for equity investment decisions.
2. Forecast product demand and market size.
3. Retain customers.
4. Recommend and configure products.
5. Suggest truck routing for distribution.
6. Filter email spam and malware.
7. Improve customer support.
8. Unlock the value of unstructured data.
9. Optimize traditional and social media marketing programs.

For summary descriptions of specific applications, please read: 10 Companies Using Machine Learning in Cool Ways.

Distributed ledger technology

Distributed ledger technology is often called blockchain. It enables new business and trust models. A distributed ledger enables all parties in a business community to see agreed information about all transactions, not just their own. That visibility builds trust within the community.

Bitcoin, a cryptocurrency, is the mostly widely known example application of blockchain.

Distributed ledger technology has great potential to revolutionize the way governments, institutions, and corporations interact with each other and with their clients or customers.
Selected business applications of distributed ledger technology include:

1. Supply chain – to track and confirm custody transfers of products from seller to buyer.
2. Contracts – to ensure parties fulfill the obligations they committed to.
3. Banking – to ensure payments issued are received by the intended party.
4. Finance – to ensure loan disbursements and payments are completed while precluding repudiation.
5. Asset identification – to track and confirm ownership changes and prevent fraud and product knock-offs.

For descriptions of industry-specific distributed ledger applications, please read: 17 Blockchain Applications That Are Transforming Society.

IIoT/SCADA

The Industrial Internet of Things (IIoT) is a major advance on Supervisor Control and Data Acquisition (SCADA). SCADA, in many forms, has been used for decades to safely operate major industrial facilities including oil refineries, petrochemical plants, electrical power generation stations, and assembly lines of all kinds.

IIOT is a major advance over relatively expensive SCADA. IIoT relies on dramatically cheaper components including sensors, network bandwidth, storage and computing resources. As a result, IIoT is feasible in many smaller facilities and offers a huge increase in data points for larger facilities. Business examples where IIoT delivers considerable value include production plants, trucks, cars, jet engines, elevators, and weather buoys.

The aggressive implementation of IIoT can:

1. Improve manufacturing quality.
2. Lower operating costs.
3. Reduce the likelihood of expensive unscheduled outages.
4. Improve employee safety.
5. Prevent catastrophic damage to facilities.

For summary descriptions of specific IIOT applications, please read: The Top 20 Industrial IoT Applications.

RISC-V

RISC-V is an open-source hardware instruction set architecture (ISA) for CPU microprocessors that is growing in importance. It’s based on established reduced instruction set computer (RISC) principles. The open-source aspect of the RISC-V ISA is a significant change compared to the proprietary ISA designs of the dominant computer chip manufacturers Intel and Arm.

RISC-V offers a way around paying ISA royalties for CPU microprocessors to either of the monopolists. The royalties may not be significant for chips used in expensive servers or smartphones, but they are significant for the cheap chips required in large numbers to implement the IIOT applications listed above.

For an expanded discussion of RISC-V, please read: A new blueprint for microprocessors challenges the industry’s giants.

What bleeding edge information technology developments would you add to this list? Let us know in the comments below.