Instead, organizations around the world are turning to the intelligence of robots to give them a competitive edge. Machine learning allows your venture to step ahead of the competition, by accessing all the in-depth data about your conversions and customers that you need to make a real difference to your marketing strategies. According to some research, companies using machine learning strategies are up to twice as likely to make decisions driven by data, and five times more likely to make decisions quickly.
So, the question is, how can companies really take their business data to the next level, without overspending on their budget?
In today's business environment, where customers crave more empowering experiences from their favorite brands, and business is struggling to stay afloat in an over-saturated marketplace, the first steep of any machine learning strategy must begin with in-depth data. Data is an essential element of the new economy, and the amount of information available to access has expanded astronomically in recent years.
Back in 2013 we only had around 4.4 zettabytes of data to deal with. Now, thanks to new technology, IoT, and various innovative developments, we're looking at around 44 zettabytes of data by 2020 - too much for any business to manually assess themselves. However, if you're not taking steps to carefully store, manage, and assess your data, then you could be missing out on huge opportunities for growth.
The ability to effectively and efficiently manage data is a problem that all companies are facing, no matter which vertical they might be located in. After all, this is the information that helps you to understand the customers you're working with, and ensure you're doing your best to serve their needs. No matter whether you're a big or small company, you need to know exactly who your most competitive customers are, and how you can keep them loyal to you.
Of course, it's not just storing your data that's important, but what you do with that data too. The information you collect in real-time needs to be carefully addressed and analyzed so you can make the most of the insights that are available for you. Those insights will help you to make crucial decisions about your future in everything from marketing to sales.
So, if machine learning and data analytics are so effective when it comes to improving the experience that customers and businesses have - why isn't every enterprise starting to test the waters? The simple answer is that as with any new revolutionary tech, enterprises have challenges to face before they can fully embrace a new strategy.
For example, today's companies are facing silos in their data and legacy systems that make it harder for them to come to terms with the information available to them. They don't have the right technology to provide them with real-time information about important developments, and they struggle to predict outcomes based on the information they have.
The good news is that the Google Cloud Platform and CHT can help. For instance, the Google Cloud data warehouse service allows enterprises to break down their data silos and generate better insights at a larger scale. The Warehouse basically delivers a full foundation for building up AI and machine learning strategies, so that your company can begin to digitally transform.
At the same time, the "Streaming Data Analytics" system available from Google Cloud can help to reduce the risk involved in processing real-time data, generating valuable insights to inspire stronger decision making whenever data is available. With the Google Cloud AI solution, you can easily and effectively assess customer needs, and automate insights through your machine intelligence strategies, keeping your company one step ahead of its competitors.
Machine learning and Ai are the innovative new developments in the technology world that truly allow modern companies to take control of their competitive business advantage. The insights that Google Cloud Platform can generate are driven by real-time analytics, data management, and predictive outcomes, which help enterprises to understand their customers better, and maintain operational advantages.
Google's "Tensorflow" is the strategy delivered by the data giant to simplify the world of data science and machine learning for everyone involved. The system can quickly and effectively develop neural networks for the machine learning environment so that any enterprise can access the simple training, construction, and deployment or various neural solutions.
While Tensorflow might not automatically provide all developers in the enterprise space with the skills they need to fully harness machine learning, but it does offer both a C/C++ and Python API which can link seamlessly into the developer program. This machine learning opportunity means that companies of all shapes and sizes can begin to really take advantage of their own data, and combine their strategies with the cloud.
Google's machine learning solutions and the Tensorflow strategy makes it easier for companies around the world to tap into the self-learning elements and AI features they need to take full advantage of the world of data analytics and artificial intelligence. With the Tensorflow libraries, you can even enjoy access to everything from speech recognition, to natural language processing.
Towards the end of 2017, Google even launched their "TensorFlow Lite" product in a developer preview set, designed to help customers build machine learning solutions specifically for embedded IoT and mobile devices. This lightweight version of the Tensorflow product should provide fast performance options for a range of Android and iOS devices.
If you're ready to take advantage of the true benefits that machine learning and data analytics has to offer, including everything from bigger sales, to better customer interactions, then Google has a track designed to suit any need.