Today whole economy is changing into Digital economy and disrupting all the respective markets and industries. Businesses are shifting from selling physical things to digital things.
Just to take an example of Music industry – in old days we had to go to store to buy LP records, then came cassettes and CDs which were then disrupted due to introduction of MP3 format in 1990s. Later came IPod/IPhone and people could carry their music with them. Spotify further changed this to streaming music, so now you don’t have to download music at all.
The song collection on Spotify is so large that you will need a lifetime to listen to all of them. That is where Spotify started using machine learning, to identify and predict what a person would like to listen next.
The main driver for this change was “Customer behavior”. With flexibility of anytime, anywhere any information, customers became more aware of the products than manufacturers. Companies need to understand this and recognize the trends, to predict customer behavior.
With exponential growth in data from people and things, a key to survival is to use machine learning & make that data more meaningful, more relevant to enrich customer experience.
It took a while for phones to become smart phones. But now machines can become smarter machines faster, by combining the Internet of Things, machine learning, and data insights, with addition of people and devices.
In order to understand all the data coming our way, machine learning algorithms will be required. These are various techniques that allowed machine to learn on its own. Algorithms will also significantly influence the Internet of Things. Without them, the IoT will not even become possible! Gartner predicts that 25 billion connected devices will be in use by 2020. All these devices will be connected to business processes as well as billions of smartphones. This will generate massive amounts of data that need to be analyzed to understand what’s going on. This can only be achieved with algorithms, which can take automatic action at the right moment.
Here are some of the examples how machine learning is helping businesses in Digital Transformation:
- PayPal fights fraud with machine learning consuming more than 1.1 petabytes of data for 169 million customer accounts at any given moment.
- Airbnb uses Aerosolve Machine Learning package to help owners set a price for rental based on features of home, time of year, demand etc.
- Amazon's recommendation engine is one example where machine learning drives a lot of economic value
- Microsoft Azure Machine Learning, Google Prediction APIs, Amazon ML and IBM Watson Developer Servicescomes with ready made algorithms that allows business to extract patterns from data, predict trends, identify language translations, understand social media sentiments, just to name a few.
- Apple Siri, Google Now, Microsoft Cortana like digital personal assistants are making use of Machine Learning for speech recognition to become smarter & creative, knowing more about you and your needs
- By connecting the sensors and systems in each of their elevators to the cloud, ThyssenKrupp, a Garman Elevator Manufacturer, has been able to move beyond preventative maintenance to offer predictive and preemptive services, a service that has not been possible before in the elevator industry.
- Today after 56 years even Barbie doll is going to become interactive and internet connected, that can talk to children and respond to their questions.