With the aim of gaining an understanding of various facets of the world, data analysis is extensively used. From forecasting weather patterns to calculating the average weight of a populace, the practice has diverse applications. Not only academics, but also businesses harness data analysis to predict outcomes and improve their comprehension of their respective domains.
Data comes in various forms, such as qualitative or quantitative, structured or unstructured, digital or manual, and a host of formats to name a few. Data scientists and analysts often face the challenge of determining the most suitable techniques for gathering and preparing data for analysis when dealing with an unfamiliar data type.
The distinction between structured and unstructured data
Ellen and Charlie, both renowned scientists in their respective domains, have differing approaches to record keeping. Ellen diligently maintains a record of her research in spreadsheets, whereas Charlie has a habit of delaying the process and jotting down his findings on any available surface.
Let’s consider a hypothetical scenario where we ask each scientist to produce their database. Ellen would share a spreadsheet file, while Charlie would offer a folder containing chronologically arranged paper sheets containing numerical data.
Online databases resembling Ellen’s data are typically structured data that has a distinct format or can be modelled. In contrast, Charlie’s data represents unstructured data. Nevertheless, it holds equal significance as Ellen’s data, as long as it is accurate. However, the compilation and evaluation of this data demands more time and resources.
According to Computer World, around 70 to 80 percent of data is unstructured. Although it may seem like a setback, it is a widely prevalent phenomenon that can be tackled. Charlie’s record-keeping style, while not ideal in this context, aligns with this statistic.
Due to time and resource limitations, preparing and organising data can be a challenging task. But if the data is unsuitable for conversion to a structured format, alternate approaches must be contemplated.
Data Analysis with AI Aid
The advancements made in the realm of Artificial Intelligence (AI) in recent decades have created numerous prospects in data processing. Machine learning and intelligent assistants have made it possible for us to readily collect, refine and analyse vast amounts of data beyond our imagination.
Machine learning encompasses an ensemble of algorithms used to scrutinize data, acquire insights and then employ them to resolve novel situations. It might not be known to many, but is a potent tool when applied appropriately.
Machine learning is put to use by streaming services, leveraging it to recognise viewing patterns of users and provide recommendations that cater to the interests of viewers with similar preferences. Likewise, internet retailers utilise the browsing and purchasing patterns of customers to make informed speculations regarding products that may captivate their interest.
When referred to in this context, “learning” denotes the practice of fine-tuning an algorithm by increasing its exposure to data. It can be viewed as a tool that becomes progressively polished through repetition.
Basic machine learning models, such as those founded on linear regression, can be expanded to include more intricate models tailored to address complex obstacles borne out of unstructured data.
The Era of Deep Learning
Deep learning is a subfield of machine learning that emphasises the creation of models that simulate the judgement-making abilities of humans. It has been employed in numerous undertakings, including social media filtering, image recognition and speech recognition.
To get a deeper understanding of this issue, let us consider the case of our scientist, Charlie. It appears that just before submitting his database, an untoward incident occurred where a can of Coke spilled over the folder that contained his records. Subsequently, certain portions of the data got warped, making it arduous to correctly identify the figures. While a human might try to deduce the intended figures, an automated algorithm is highly likely to face significant hurdles in this regard.
The use of deep learning permits us to employ diverse algorithms across several tiers to build a decision tree, capable of delivering an answer, assessing its precision, and tweaking itself to provide more precise estimations. This technology equips us with this capability.
The Japanese board game, Go, offers a perfect demonstration of how deep learning can be leveraged. Google has heavily invested in creating an AI capable of playing Go, and while AlphaGo has been a momentous achievement, it has proven to be much more challenging to construct AIs that are adept at playing Go compared to Chess.
It is evident that the computation power required to determine all plausible outcomes in a game of Go is immense. Hence, an AI built to learn Go must be capable of making precise decisions based on the board’s present state. Furthermore, akin to human players, the computer can employ heuristics when it is unable to meticulously evaluate all alternatives. Data-driven decision making is vital to this process.
Handling Unstructured Data
The fundamental challenge encountered when dealing with unstructured data is that it often displays an uncertain pattern or could even lack any discernible pattern. Natural language is a prime illustration of this as it can be articulated in numerous ways, yet humans can comprehend it. In contrast, machines necessitate sophisticated designs and models to process such data correctly.
Deep learning has emerged as a prevalent technology, with one instance of its application being Tesseract – an optical character recognition (OCR) program that utilises deep-learning to precisely detect text in photos. Although it may seem like an easy task, this process can actually be quite challenging.
Some of the photographs that you have received may have low quality – they may be confined to just a few frames, have blurred text or be taken from a fragmented film. Since each image is distinct on its own, developing an algorithm for each case could be infeasible. Hence, we leverage deep learning to analyse all these images simultaneously, thereby saving on development time.
Given that our world is not highly ordered, and humans tend to process information in an unordered manner, deep learning has emerged as a potent AI method. Although it is still in its nascent stage, I foresee that virtual assistants such as Siri will soon evolve from simply being advanced search engines. This exemplifies the potential of deep learning.
