Why We Need Machine Learning as part of Data Visualization
Data visualization is an important tool for examining data at a glance, and particularly for spotting trends and groups. By implementing Machine Learning, data visualization can adapt itself to the data as it updates.
We’ve seen how AI is making waves, and now Machine Learning (ML) is stamping its own mark in technology applications. We’re no longer creating software that needs human technicians to update it: The software itself is adapting to the conditions of its data and learning from its applications to improve independently.
While Artificial Intelligence is the broad concept of machines performing functions independent of human input, ML is more specific, whereby machines access data and learn from it. Rather than inputting or retrieving data for a human to quantify and examine, machines can do it themselves using neural networks. By utilising probability and classification algorithms, it can predict outcomes, such as the aspects we’ve come to know these past few years, like recognizing spam in emails, auto-filling search engine queries, and identifying similar objects to tag them in your phone’s photo album, up to recognizing influences between artists that even human experts have missed. The categorization of data means that it can be grouped and presented ready for human examination and application.
Immediate Data Visualization
When a human agent is updating data, there is always going to be a lag: They have to obtain the information and then update the appropriate aspects before they can even begin to interact with it and put it to any use. But when ML is involved, the data processing time is minimized, creating real-time updates. Automatic uploads, automatic grouping. Live data streams mean that you can see everything happening with a single glance at a dashboard, and you can witness trends as they develop.
Handling Massive Amounts of Data
There’s only so much data humans can deal with within a given time frame, regardless of how many of them are working on it.
Machine Learning can process gigabytes of data, organize it into the appropriate categories, and produce a visualization all in a matter of seconds. Multiple validation, exclusionary values, and cross-referencing can all be handled by a program that gets better as it progresses. Rather than being overwhelmed with too much data, ML becomes more accurate as it consumes more data and becomes more adept at identifying patterns, groups, and user preferences.
Problem-Solving – Before the Problem Arises
Since ML algorithms are much more objective in their organization of data than humans, ML can help identify data that falls outside of parameters and how to react to it – meaning less processing time and quicker reactions to data that doesn’t fit in or unexpected situations.
With so much data to look at in one go, ML can draw on a massive wealth of information to make extremely accurate predictions. There is not only data to analyze but also lessons from outcomes to learn from.
Data visualization can project better forecasts for the future, as well as prepare for marketing. Recommendations can be based across a wide range of demographics as well as personalized data for individuals, creating a plan for both individual targeting and wider marketing campaigns. And once the forecasts are confirmed, this affirms the model’s algorithms and makes it more accurate for the future. This can be used to predict investment worth, such as conversion rates for free trial customers.
Natural Language Processing
We’ve grown accustomed to adapting our online language to fit with search algorithms. Instead of asking a search engine a question as we would ask another human, we trim our words and choose the most pertinent keywords to get the most relevant results.
But Natural Language Processing applications are a subset of ML that aim to understand the more natural nuances of real human speech – and not only understand it, but to respond in kind. This isn’t just restricted to searching a website for a product or an encyclopaedia for a definition but can form the basis of an analysis across a wide variety of semantic reasoning. Instead of picking out keywords to respond to, NLP can read into the text as a human would.
NLP can identify technical aspects such as noun, verb and preposition, as well as picking out more subtle aspects such as register and tone to formulate a natural-sounding response.
Without relying on keywords, NLP can create subtler ML that produces more accurate results: It can analyse more in-depth information as a human would rather than a blunt yes/no machine. NLP has a vast range of uses in data visualization, including summarizing massive blocks of text, which needs some in-depth understanding of the language to highlight important aspects and ignore the unnecessary, and scanning social media posts to get an idea of what people are talking about in relation to the business or products, including the “sentiment” behind the text (whether it’s positive, negative, or neutral). Social media sites are already implementing NLP-based Machine Learning to filter abusive messages.
Saving Humans for More Complex Tasks
Hard limits can be extended to similarities in order to group data and present it on-screen, ready for human agents to use it. Deciding how to apply the data in its most effective form can be complex, but at least with ML the work of gathering and grouping is already taken care of.