Can AI handle all kinds of data?
AI is taking an ever-expanding role in data processing. But not all information is alike. Can AI really handle all kinds of data? Today, we’re going to discuss the challenges most AI solutions face with different data types and why the fact that our platforms have overcome these issues is such a big win.
Different AI data types
Modern AI has to contend with a number of unique data types.
Structured data
This is the kind it likes best. It’s highly organised data that follows a predefined schema, making it easily analysable by machines. This might be data in SQL databases or spreadsheets. AI loves it because it’s stored in rows and columns, has a fixed format and structure, and it’s easy to query.
Unstructured data
Next, we come to data that does not have a predefined structure, and this is much harder for AI to work with. Think text documents, images, videos, audio recordings, emails and social media posts. This data has no consistent format, and it often requires advanced tools like NLP or computer vision for analysis. Even worse, it makes up the majority of data in the world today, so systems that can’t handle unstructured data are already on the back foot.
Semi-structured data
More middle-of-the-road data is semi-structured data. It’s data that does not reside in a strict table format, but still contains tags or markers. This is your more technical stuff like JSON, XML, YAML and HTML code. It has organisational properties but a flexible schema that makes it easier to analyse than unstructured data, but still challenging
Time-series & streaming data
The last type is time-series & streaming data. A time series is a sequence of data points in time order, and streaming data is generated in real time. Here we’re talking about IoT sensor data, financial market feeds, logs from servers and applications, social media streams and video feeds. They’re both time-stamped and are often large volume and high velocity. Processing this kind of data may require specialised tools for real-time processing, like InfluxDB.
Why AI models struggle with most data formats
That lack of consistency across formats (and the difficulty of properly cleaning and labelling each data type) often leads to fragmented toolsets, where you’ve got different platforms for different data. There are also hefty computing and storage requirements for multimedia data types, and real-time streaming data adds latency and synchronisation issues to worry about. Once you process this data, it’s often in different silos, and that lack of context adds ambiguity, muddles intent and makes complex scenes even harder to understand.
Why our unified data proposition really matters
Our AI platforms are purpose-built to handle all major data types and then integrate so structured, unstructured, numerical, text, image, time-series, audio, video and IoT (streaming & instance) are all processed properly and brought into a single view. This means you don’t need to have multiple tools, and no business intelligence is lost in silos. You can pick and mix what you need for your unique use cases, too. So, for agritech, you might need IoT data with image processing and time-series for crop health and yield forecasting. But for research, you might need text notes processing with image and audio for diagnostics and decision support. It’s all available to plug and play to meet your needs in real-time.
Our tech means you don’t need to build and stitch multiple AI pipelines, and you’ll get faster insights, lower costs and broader applications as a result. Let’s talk about how it can help you.