Turning data into gold requires an investment of time, effort and ingenuity. Despite these challenges, savvy CIOs are reaping the financial rewards of transforming their organizations’ data systems.
For example, a leading insurance company now uses an alchemy approach to assess risks for small and medium-sized enterprises (SMEs). The algorithms analyze business outlooks rather than past credentials, which reflects the speed and magnitude of change in the current financial environment.
How to Get Started
For years, business leaders have been relying on data mining, which is like conventional gold mining. However, as the pace of change has increased dramatically during the COVID-19 pandemic, this method isn’t nearly fast enough. It is time to embrace data alchemy, which combines continually refreshed, broadly gathered information, such as social media, news stories in 67 languages, airline ticketing stats and weather data, to find correlations that traditional approaches can miss.
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Getting Started with Data Alchemy
As a program processes data, it needs to be able to save and retrieve the results. This can be done with a database or flat file. A flat file is a human-readable text file that has a structure that can be parsed by a computer program. A database is a relational model that enables your program to store, access and manipulate the data.
For example, let’s say that your program has a model of authors and books. When you run a query against this model, it will return a list of all the authors. This list will contain scalar information, such as their names and nationalities.
These are the kind of data sets that older data-collection methods excel at when they know what needle to find in a vast haystack. But as COVID-19 teaches us, the world’s haystacks are constantly changing. That’s why many companies are now shifting to data alchemy. It frees them to focus on decision-making, and gives them the tools to adapt quickly as new conditions arise.
Getting Started with Machine Learning
If you want to start experimenting with Machine Learning, you will need to learn how to program. Python is the most popular programming language for machine learning, and there are a variety of online resources available. You will also need to find some open-source data sets to work with and practice your skills. There are many options for finding datasets, but Kaggle is a good place to start.
Getting started with Machine Learning isn’t easy, but it is possible. It takes time and effort, but once you have a solid understanding of the basics, you can begin creating models and making your own predictions. But before you get too carried away, it’s important to remember that Machine Learning is not a replacement for human genius. It’s a tool to help you better understand the world around you, so you can make smarter decisions in a more informed way. This is a valuable skill for any business, but it requires an entirely new mindset.
Getting Started with Deep Learning
As you make progress with your predictive modeling projects, it’s good to have a number of frameworks and techniques at hand. This is especially true if you have to tune your models for prediction performance on a specific problem, as in classification and regression tasks.
In contrast, the data alchemy approach aims to create an algorithm that can make decisions about data automatically without the need for human intervention. It’s the method that China’s Ant Financial uses to provide loans to small and midsize enterprises, relying on an algorithm that continuously refines itself by gathering new information: news media reports in 67 languages, CDC data, animal and plant disease statistics, blog posts, airline-ticketing data, and more.
Companies that face a high level of uncertainty in their business operations—such as those involved in remote working, employee engagement, shortening supply chains, and changing health care norms for their employees—are now embracing data alchemy. Using continually refreshed, broadly gathered information to find correlations, these algorithms recognize and act on risks and opportunities that humans may have missed.