Globally, stored data is predicted to reach around 175 zettabytes (ZB) by the end of 2025 from the current estimate of 40ZB. When we look at statistics like this, we can see that the data’s growth rate is exploding faster than ever. Some recent research on about data statistics, sheds some interesting facts:
By quickly looking at all of these stats, one thing that is for sure is that there is a massive amount of data produced every day, and this data will increase exponentially in the next few years.
We can classify this data into three different types:
In simple terms, this data, as the name indicates, is well organized (structured). The information is clear, formatted, and has been transformed into a data model. The perfect example of this type of data is SQL relational database tables, which consist of rows and columns.
Unstructured data is information that doesn’t follow any particular format and is presented in raw form (not organized). Examples of this type of data include documents, recorded audio, video, unstructured system logs, posts or tweets from social media, email messages, etc.
Data that doesn’t fit into one of the above types may be partially structured and contain tags or other markers that give it a self-describing structure. Examples of semi-structured data include XML, OEM, and JSON formats.
For now, we know that there is a huge amount of data out there, and we know how that data is structured or otherwise. Now, the question is, What can we do with all of that data?
Wouldn’t it be perfect if we were able to understand and analyze actual phenomena within that data?
A significant portion of today’s critical data is structured data generated from business applications and held in company databases. However, most businesses don’t use this data as decision-making input to boost and optimize its performance within the organization. In fact, between 60% and 73% of all data within an enterprise goes unused for analytics. The critical thing to appreciate here is that it is not about how much data a business has, but what they do with it. So, how can we improve this and shift from data-generation to data-powered business intelligence?
Databases are the source of clean business data, which is a fundamental ingredient for Machine Learning (ML). AI Tables brings Machine Learning capabilities straight to the database by automating the pipeline for applying Machine Learning directly to business data sources. This reduces Machine Learning complexity and saves time by minimizing data movement between layers (shown below).
Traditional Applied ML vs. MindsDB with AI-Tables
How AI Tables work
AI Tables can generate predictions upon being queried as if the data was present in the table. It uses the same tools as your database, the same language; it is automated, explainable, and customizable and doesn’t require ML expertise. It works with most effective data management systems available on the market:PostgreSQL, MariaDB, MySQL, MongoDB, MsSQL, and data warehouses like Snowflake.
So what are the benefits for your business of bringing Machine Learning to the database by using MindsDB’s AI Tables?
If you want to read about how you can implement AI Tables in your database, check out our tutorials: