Big Data: Friend or Foe?

Today, digital technologies have penetrated almost every aspect of our everyday lives. While devices like computers and smartphones bring new levels of convenience to our lives, a fact that is too often overlooked, is that they are also excellent surveillance devices.

Computers and smartphones produce extensive personal data and information about our Internet browsing patterns (web apps), the places we visit and how frequently we visit them (GPS, Map apps), our well-being and medical history (health, psychology, and fitness apps), who we communicate with and how frequently (email, text, SMS, social media apps), our friend lists and social networks, and much more.

The type of data collected above is constantly being harvested, potentially enabling others to peer into our personal lives and decide what kinds of products and services to recommend to us, whether or not to employ us, or whether or not we pose a threat to national security.

There is little knowledge in the public domain on how tech-companies e.g. Google, document our online experiences and how the resulting data is collected, by whom is it collected, who has access to it once it is collected and how it is ultimately used.

What is Big Data?

Big Data is a term used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques.

Big Data is collected from a number of sources including emails, mobile devices, applications, databases, servers and other means.

This data, when captured, formatted, manipulated, stored and then analyzed, can help a company gain useful insight on increasing revenues, acquiring or retaining customers and improving general operations.

Data mining is the technique of discovering patterns, correlations and anomalies in large data sets, to predict outcomes.

How has Big-Data been incorporated into today’s world?


Today’s advertisers are some of the biggest players in Big Data.

Personalized and targeted advertisements: Social media and Tech-Giants such as: Google, Twitter, Facebook etc., keep a track of online user behavior, trends and transactions and sell the collection of that information to tech-oriented advertising companies, who then process and analyze said information, enabling them to run personalized and targeted advertisements.

Take Facebook and Instagram, for example, here you can target people based on buying intent, website and page visits, interests, demographics and what not. All this data is collected by algorithms that employ big-data analysis techniques.

Personalized and targeted ads are all based on massive amounts of personal data we constantly provide about what we’re doing, saying, liking and sharing on social media.

Localized advertising: The proliferation of mobile devices, primarily smartphones, has created a major opportunity for digital advertisers to deliver mobile specific ads to the right people at the right time.

Through the combination of social data and location data, stores that shoppers are in the proximity of, can send out ads offering percentage discounts or other incentives — sent to the shopper’s location in real time — to get them to walk through their doors.

Hyper-localized advertising has been shown to increase customer engagement and conversion rates.

Big data makes it possible to understand digital media consumption because an online user’s behavior can be incorporated with traditional demographic data, to provide personalized advertising.

Entertainment & Media:

In the sector of entertainment and media, big data focuses on targeting people with the right content at the right time.

Based on your online behavior and past online views, digital content providers such as Netflix and YouTube, will show you different recommendations to watch, based on certain algorithm that is able to track your digital content viewing habits.

The big-data feature described above is popularly used by Netflix and YouTube to increase user engagement and drive more revenues through advertisements on their platforms.

Cloud technology allows digital content providers such as Netflix to analyze traffic patterns across various localities and device types to help improve the reliability of video streaming and plan for growth.

Searches, viewing history, ratings, reviews are just some of the data sources that help identify audience interest.

Financial Trend Monitoring & Fraud Detection:

Financial regulators are using big data to monitor financial market trends and also in the detection of possible illegal trades and suspicious activity for example: card fraud detection is assisted by analyzing customer information collected over periods of time.

Technologies such as network analytics and natural language processors are also incorporated to identify possible frauds in the financial markets.

Threats posed by Big Data.

The threats posed by big data mainly tend to focus on the issue of privacy and can be concentrated on three specific categories: Surveillance, Discrimination and Disclosure.


Surveillance is the monitoring of behavior, activities, or information for the purpose of information gathering, influencing, or managing.

Consider behavioral advertising, for example, where the tracking technologies that make such advertising possible cause users to feel surveilled as they go about their daily activities, online or offline.

A major issue regarding targeted advertising could also be that stored tracking information might be revealed, to unauthorized third-parties or even the government.

Alternatively, an ethical issue arises whether it is right to use tracking information to determine exactly what advertisements a person sees and which ones they do not.

Wouldn’t this amount to decision making on behalf of consumers?


Discrimination, in this case, refers to treating people differently on the basis of information collected about them.

Personalized persuasion is another form of discrimination enabled by Big data that may arise as a discriminatory issue. Personalized persuasion refers to the concept of rather than changing the product on sale, the advertiser alters the sales pitch itself so as to best exploit each individual’s own psychological biases.

Personalized persuasion is made possible through big-data’s ability to identify widely shared biases over large populations.

The exploitation of individual biases raises additional concerns about an imbalance of power between advertisers and consumers, thereby undermining fair consumer protection practices.


Disclosure threats of big-data manifest themselves in the form of security threats e.g. cyber attacks.

One disclosure threat might be cyber-attacks and hacks on corporate databases that hold vast amount of people’s sensitive information.

Personal information illegally acquired from such databases may cause irreparable harm to users for example: Credit card fraud can occur as a result of identity theft.


Big-data presents a variety of benefits in the analysis of vast amounts of data, however it also creates a space for the infringement of fundamental human rights, especially in the digital domain.

It is therefore paramount that efficient legal and institutional framework be set up to govern and regulate the vastly growing world of data and how that data is collected, accessed and used.

In Kenya, the recently enacted Data Protection Act of 2019, gives Kenyans hope for a more efficiently regulated digital sector. The Act creates the Office of the Data Commissioner which has the mandate of regulating the collection, access and usage of vast amounts of citizens’ data in the country.