Knowledge is power. Perhaps you have heard that data is the "new oil". They say that because data is valuable. But, like crude oil, raw data is not valuable in and of itself. Crude oil needs to be refined into fuel and petroleum products to be useful. Likewise, data also needs to be refined. One of the main ways that we refine data is data annotation.
Data needs to be collected, stored in databases and annotated to be used to power machine learning algorithms. Without that refined data, there can be no machine learning or deep learning, making data the most critical item. You should know that machine learning algorithms and AI systems are garbage-in, garbage-out systems. Kind of like how you get better performance out of an internal combustion engine with the best high-octane fuel. You want to put the best, high-quality data into your machine learning to achieve the best performance.
Just like you need to use the right fuel for your car or it can cause damage, you need to put the right data in your machine learning model. If you are using computer vision for object recognition, you need the right image data and image data annotation types, for example. Using the wrong datasets just wouldn't make sense and would provide terrible results.
We live in an information age, and the world is more interconnected than ever before through the internet. We work in a digital economy. Big data is big business, so companies like Alphabet are large and successful. You need good information to make well-informed decisions, so we have experienced the rise of big data. Data is the most valuable commodity. Enough of the right data can be a big competitive advantage. Improving your datasets' quality and size is the best way to improve your artificial intelligence.
AI is not really anything like a human mind. However, there is another good analogy. To be smart and successful, you need to learn new skills and information to train your mind and become more intelligent. Your AI also needs to learn and needs data to become smarter. That is why we sometimes call data intelligence in security.
Companies should understand that information and data is an incredibly valuable asset. Far from being a cost or liability, data centers are profit centers. Data can be used to improve your products and services. Data enables you to acquire and retain customers too. Data also sells your products and services to your customers when you share information about your company and what you offer.
Facts and data beats opinions and feelings. Pretty much everything that matters can be measured, tested and improved. Everyone has opinions and feelings about your products and services or company, and many will share them. That is especially true when you are starting up, growing, or finding success. Therefore, it is helpful to adopt a philosophy of good data beats opinions. You may notice that in the stock markets traders make decisions based on information to make money. Those who trade on their emotions lose.
The Most Critical Data Points
- Data is the most valuable asset.
- Raw data is not valuable until it is refined by data annotation.
- AI and machine learning take the best data and data annotation for the best results.
- Having the best data can provide a competitive advantage.
Big Data, Big Data Annotation
Big data really is big business. This also makes the best data one of the best ways to grow your business and improve your AI products and services. All that data requires data annotation at scale. Because as long as data is the most critical item, data annotation is also the most important. Data annotation refines and improves the data and also adds even more data.
That data can also be hard to manage, and AI is a very complicated high technology. In fact, AI is sufficiently high-tech to appear as high magic to most people.That is why you want to be able to focus on working that magic and not be caught up in doing your own data annotation.
Coming back to the idea that data is like oil, car companies don't normally refine their own gasoline. Likewise, instead of refining your own data, you should consider outsourcing data annotation services. We are passionate about data and data annotation. We can certainly provide all the data collection, data, and data annotation that you need to power your machine learning model.