Mary Shacklett

70% of Fortune 500 companies run applications on legacy systems. .While there is good reason why many industry prognosticators predict an end to “old” systems that have run for 30 or 40 years, these systems are reliable and they work. Companies are in no hurry to move them out.

Companies interviewingl IoT stack vendors should ask them about their ability to apply uniform security policies and standards at every level of the stack—and how they do this for IoT devices that are purchased on the open market.

The sooner IT inserts itself into conversations about cloud services and systems that users might choose to fund on their own, the more IT is in a proactive position to help with what certainly will evolve into calls for help with system implementations and support.

It can take two to three hours to document a freeway traffic accident. Meanwhile, lanes are closed and oncoming traffic slows to a crawl. Drones can cut 60 percent off the down time for traffic flow following a crash.

The more effectively CIOs and CFOs collaborate throughout the lifetimes of projects, the easier it will be to get projects to market quicker, and the greater the probability of positive business returns for companies.

If analytics and data management vendors deliver agile, flexible and interoperable solutions that can upwardly scale with data and processing workloads, while also managing security/billings from a single point, IT will have investment and workload protection for the future.

Any time decision makers can't arrive or concur on decisions, it’s incumbent on IT to look at the data. Although disagreements or decision inertia may be philosophical, they can also be due to inconsistent or bad data.

CIOs and other IT leaders should develop guidelines for IT and end users about when to develop applications using low code and no code, and when it’s necessary to develop in a full code mode.

Organizations are looking for individuals who are highly skilled in working with and analyzing big data on the technical side. They also need people with IT backgrounds who are business savvy, and able to work collaboratively and effectively with end business users.

There will be pressure on organizations to show demonstrable results from the business intelligence they are deriving from their big data systems and AI.This should drive effort for identifying the most impactive business use cases to which big data and analytics are applied.

The signing of the Internet Cybersecurity Act on December 14, 2020, will promote IoT standardization . This will be a major step forward for IoT integrators and consumers. It is exactly the space that IoT should be in.

Technologies that are “hidden” in AI like machine learning (ML) are difficult for anyone to explain.That’s why it creates risks for companies and for CIOs and data scientists who are expected to explain how their AI operates.

To make self-driving cars a widespread reality, real-time data processing and analytics inside and outside of each car; and improvements to bandwidth, cloud super structures, road asset digitalization, and safety and regulatory guidance must take place first.

For every predictive analytics model that is developed, companies must continuously improve and refine them. This improves the accuracy of the analytics, and also assures that the company moves forward at the pace of business.

The goal of big data as an IT initiative should be premised on communications that come often and are easy to understand. One key to this is taking the time to explain a new process to end users in their own terms.

Proprietary code in your systems that give your company a competitive advantage, and systems that contain irreplaceable intellectual property (IP) that you don't want to risk data breaches on, benefit from systems that are under direct company control and management.

Centers of Excellence are commonly used in healthcare to focus on specific problems or disciplines. I’m going to advocate that they can be used in organizations for AI as well.