The Future of Connectivity – McKinsey

As the world experiences a quantum leap in the speed and scope of digital connections, industries are gaining new and enhanced tools to boost productivity and spur innovation.

Over the next decade, existing technologies like fiber, low- to mid-band 5G networks, low-power wide-area networks (LPWANs), and Wi-Fi 6—as well as short-range connections like radio-frequency identification (RFID)—will expand their reach as networks are built out and adoption grows. At the same time, new generations of these technologies will appear with upgraded standards. In addition, new types of more revolutionary and more capital-intensive frontier connectivity like high-band 5G and low-Earth orbit (LEO) satellites will begin to come online. Together, these technological developments will unlock powerful new capabilities across industries. Near global coverage will allow the expansion of use cases even to remote areas and enable constant connectivity universally. Massive IoT advances will be enabled as new technologies allow high device densities, and mission critical services will take advantage of ultra-low latency, highly reliable, and highly secure connections.

From healthcare and manufacturing to mobility and retail, there are hundreds of promising use cases for the emerging generation of enhanced connectivity. Together, advanced and frontier connectivity could help seven sectors add a total of $2 trillion to $3 trillion in additional value to global GDP.

Data Mining vs. Machine Learning vs. Deep Learning

Data Mining

Data mining can be considered a superset of many different methods to extract insights from data. It might involve traditional statistical methods and machine learning. Data mining applies methods from many different areas to identify previously unknown patterns from data. This can include statistical algorithms, machine learning, text analytics, time series analysis and other areas of analytics. Data mining also includes the study and practice of data storage and data manipulation.

Machine Learning

The main difference with machine learning is that just like statistical models, the goal is to understand the structure of the data – fit theoretical distributions to the data that are well understood. So, with statistical models there is a theory behind the model that is mathematically proven, but this requires that data meets certain strong assumptions too. Machine learning has developed based on the ability to use computers to probe the data for structure, even if we do not have a theory of what that structure looks like. The test for a machine learning model is a validation error on new data, not a theoretical test that proves a null hypothesis. Because machine learning often uses an iterative approach to learn from data, the learning can be easily automated. Passes are run through the data until a robust pattern is found.

Deep learning

Deep Learning combines advances in computing power and special types of neural networks to learn complicated patterns in large amounts of data. Deep learning techniques are currently state of the art for identifying objects in images and words in sounds. Researchers are now looking to apply these successes in pattern recognition to more complex tasks such as automatic language translation, medical diagnoses and numerous other important social and business problems.

Machine Learning and IoT

Machine learning has experienced a boost in popularity among industrial companies thanks to the hype surrounding the Internet of Things (IoT). Many companies are already designating IoT as a strategically significant area, while others have kicked off pilot projects to map the potential of IoT in business operations.

As a result, nearly every IT vendor is suddenly announcing IoT platforms and consulting services.

But achieving financial benefits through IoT is not easy. The lack of concrete objectives is disconcerting. The advancement of digitization and IoT places new prerequisites on both buyers and sellers. Many businesses have failed to clearly determine what areas will change with the implementation of an IoT strategy.

In other words, clearly defined, concrete intermediary objectives are missing. For example, industrial companies produce a massive amount of data on a daily basis. However, by and large, companies fail to systematically collect, store, analyze and use such data to improve process efficiency or meet other goals.

Furthermore, not many vendors are able to establish, in concrete terms, to the client how to prudently create positive impact on business operations with IoT solutions. Simply the promise of a cloud-based IoT platform is not enough.