The malls, restaurants and retail stores in Tampa Bay are booming! How do I know? I can't find parking anywhere. There are wait times to be seated at dining establishments on a weekday. And I had to wait in line to buy hand soap at the mall – seriously! It seems like we’re hearing more and more about retail store closures and bankruptcies. The reality is, these store closures aren’t new and there are retailers who are actually thriving thanks to digital transformation.
If you’re just beginning to traverse the landscape of the Internet of Things (IoT), you've likely come across a surplus of IoT platforms. To get the most out of IoT data, you'll need to employ the tools a platform provides for connectivity, analytics, triggers, alerts and more. The quest for the perfect IoT platform often begins with the following questions: 1) What is an IoT platform? 2) Which platform do I choose to get started?
The backbone of Artificial Intelligence (AI) relies on the data that it collects. For an AI system to perform tasks that typically require human intelligence such as decision-making, there are two commonly known techniques it uses: machine learning and deep learning. Both techniques require data to be analyzed almost instantaneously to make split-second decisions. This is where IoT partners use AI to create an effective solution in nearly every industry. Certain industries benefit more than others in this AI and IoT realm based on the type of data available to collect.
Many IoT projects originate as open discussions with lines of business (LOB) that aren’t related to the IT department.
Here’s a recent example that we heard from a college at a technical seminar:
“We had a theft incident last night. Our new building construction site was robbed again and we can’t have someone sit there every night to monitor the site.”
Thanks to modern communications infrastructure we can bring the compute power from the likes of Amazon, Microsoft, and Google to just about any point in the developed world. But what do your customers do when they need data-heavy analytics in remote, rural, or undeveloped locations? They can’t always afford to build a data center where they need them. The poor quality of infrastructure in these regions prevents the use of high bandwidth solutions. But there must be a solution, right?
Navigating the world of the Internet of Things (IoT) devices as a developer can be overwhelming. With thousands of unique products, hundreds of disparate communication protocols, and millions of promising use-cases, a vast universe of possibilities awaits. The missing link between all of these elements is the software. But what can the app developer build that’ll be useful and gain traction in a connected ecosystem approaching 30 billion devices1 by 2020?
The market for industrial IoT (IIoT) solutions continues to grow almost as fast as IoT solutions overall. And that growth is driven by federal regulations. If you have customers in the area of facilities management, manufacturing, and industrial development, consider offering those clients IIoT solutions focused on worker safety.
The Tech Data Smart IoT Solutions team recently showcased our capabilities as the IoT aggregator of choice at Cisco Live in Las Vegas, NV. At the conference, the Tech Data Smart team enlightened partners, customers, and vendors alike on its end-to-end solutions through their smart city model, while providing meaningful engagement to booth visitors from kickoff to close of conference.
Often in parallel with cognitive, you hear artificial intelligence (AI) referenced which seems to blur the lines of distinction between the two. In part one of the de-mystifying series, I discuss the driving factors for cognitive computing, let’s now break down this notion that AI and cognitive computing are one and the same. In fact, there is an explicit difference between AI and cognitive computing.