The Big-Data Boom Explained in More Straightforward Terms
Your ‘digital footprint’ is the information you share concerning yourself online. Lots of people use social media to share information or to talk with friends, family and colleagues. It’s worth checking what people can find out about you. Why not take a moment to open a new window and do a Google search of your own name? Are there any surprises?
Dealing with unstructured data and with the explosion of big data, companies have a reciprocal blast “trying to mine value from the overwhelming amount of data out there. When looking at the data that needs to be analysed, we can find two distinct types: structured and unstructured data (Crimson Hexagon, 2019, pp. 14–17).
We are all knowledgeable of structured data. These include acquisitions, electronic sign-ups, and transactions, but…what is unstructured data? How is it different from structured data? What’s the difference between structured and unstructured data?
Structured data refers to organised data and displayed in a database with rows and columns, making it straightforward to work with. Examples of this include sales figures, names, phone numbers, etc.
Unstructured data lacks organisation. Due to its variability and unidentifiable internal structure, unstructured data cannot be analysed by conventional technologies. A few examples of ‘unstructured data’ are Emails, images, Social media posts and Product Reviews (Crimson Hexagon, 2019, pp. 14).
When looking at social media posts, we can see that most of the information can’t be segmented into fixed categories due to the complexity and variability. Social media users write about different subjects, making it hard to categorise them precisely in varying forms. With the rise in popularity of social media channels, new analytical tools and processes were developed to fully understand and extract value from this unstructured data boom.
AI has been used in several different ways to facilitate capturing and structuring big data. It has been used to analyse big data for critical insights.
Artificial intelligence has appeared to make this task possible. With the fantastic opportunities it offers, the management of these large masses of data has become a straightforward thing. Now, marketing has opened new doors to reach all Internet users in a precise and immediate way. (Benhabdelouahed & Dakoun, 2020, p. 83)
However, many of us have a bizarre view of artificial intelligence shaped by American films. A robot from the future will eradicate the human race or control it outside these well-scripted stories. Artificial Intelligence has nothing to do with it, but rather to make each day’s experience more intuitive and smarter by integrating predictive intelligence with the platforms we use.
Why should we analyse this unstructured data? It can produce more in-depth insights. Businesses in numerous industries examine and finance tools to derive meaning from this data and run strategic business arrangements, something hard to get from restricted structured data. The usefulness of unstructured data comes from the patterns and the meanings that can be derived from it. Examples include cataloguing issues, business trends, or overall consumer sensibility towards a brand.
Two possible solutions for unstructured data analysis are machine automated NLP (natural language processing) and Machine-Learning Natural Language Processing. They are a branch of artificial intelligence that allows a device to understand the human ‘natural’ speech.
Consequently, the machine-automated solution tries to make sense of the data by devising statements and systematically classifying them. NLP applications on social data can identify general attitudes about a subject, each positive, neutral or harmful, or even examine the common emotions through emotion analysis. Machine-automated solutions aren’t sufficient. Most analytics firms offer machine-automated characteristics. However, the problem is that the results can be inexact and not relevant to the subject matter. Although machine automation entails less setup time, it risks providing unnecessary information to the user when analysing conversations in various industries with distinct accents and slangs.
On the other hand, for additional, extensive text analytics, there’s the machine-learning approach. Think of online recommendations from Amazon. Depending on your acquisitions, search records, reviews, Wish List, other alike customers’ interests, it will find items more relevant to your search. Or even better, think of machine-learning as a Gmail filter. Gmail filtering is adding a label or tag to emails to count and group emails of the same classification. With machine learning, Gmail’s Inbox classifies emails into Topics like Social/Promotions/Updates etc. This model studies patterns in every email content, such as keywords, phrases, and authors. Assigns it to the most relevant category. This doesn’t follow predefined parameters.
Machine-learning enables tools to analyse numerous variables concurrently and exhibit how they interconnect to develop patterns. This option varies from machine-automated answers in many ways. Considering the outcome will be more related to the user’s question, machine-learning needs external information and in-depth comprehension of the conversation’s subject interest to train the tools appropriately.
References
Crimson Hexagon. (2019). The Fundamentals of Social Media Analytics.
M. Benhabdelouahed & C.Dakoun, C. (2020). The Use of Artificial Intelligence in Social Media: Opportunities and Perspectives. The Use of Artificial Intelligence in Social Media: Opportunities and Perspectives, 82–86. http://marketing.expertjournals.com/23446773-806/
Psychological Targeting: What Your Digital Footprints Reveal About You | Sandra Matz | TEDxChicago. (2019, June 5). [Video]. YouTube. https://www.youtube.com/watch?v=MkI_TrPmKgA
This blog is a project for Study Unit MCS5460, University of Malta.
