Big Data and Privacy

Introduction

In 2006, Clive Humby, a British mathematician, coined the phrase “Data is the new oil.” Palmer later built upon this phrase to add that there’s value in data, but only if it is refined. Unrefined data lacks utility. Data’s importance and how it is utilised? How information is extracted from massive silos of data, these questions become important now in the age of the Digital Economy. From Governments to Corporates, in the Digital sphere, data is now key to their smooth and efficient functioning.

This digital economy is driven by data-driven organisations that have built upon their organisations and their unique selling proposition based on information extracted from Data. Thus, it is not mere storing of data in sizes that result in positive outcomes, but how the data is managed and utilised is also essential. Emphasis on Managing data rather than merely storing it is the stark difference between traditional and Big Data models, with the latter emerging in the post-internet world

Big Data

Big Data is a merger of arranged, semi-arranged and unarranged data, usually collected by large global organisations. The speciality of this data lies in the ability to mine refined data, i.e., information out of these silos of data, by using these data sets with advanced analytical applications. These applications include but are not limited to Machine learning projects and predictive modelling. Gartner, a tech-consultancy firm, was the first to respond to the question of what big data is comprised of. The composition can be characterised in a three-part analysis.

The first part involves using Doug Laney’s understanding of Big Data by Three V’s: first V is about processing Vast amounts of unarranged, unstructured data. Second V is about the Velocity, i.e., the rate at which data is stored, managed, processed, or acted upon. It is becoming increasingly crucial with developments in the field of real-time products. And third V is about the Variety or distinct types of data available.

The Second part of Gartner’s Characterization of Big Data is about the ability to link a set of unstructured or unarranged data with each other and to perform a comprehensive analysis. This results in the generation of cost-effective solutions to the problems concerned. And the Third part aims to process such silos of data, i.e., to produce novel and enhanced insights that will aid in making informed decisions. Data-driven organisation decision-making is centred on foundations of concrete and refined data.

The range in which Big Data can help address issues in a business is wide, from analytics to customer experience. It can aid in product development and develop products accustomed to customer demands that meet quality standards. Big data can also assist in producing innovative solutions based on enhanced insights extracted from these data silos. These innovations can range from solving customer and product issues to even reducing operative efficiencies & even finding deep structural faults in the ecosystem that may not be apparent.

Emerging Privacy Concerns

Despite all the advantages Big Data offers in the age of the Digital Economy. Like other innovations, it is mired by some fundamental issues and emerging challenges. One of the major challenges is the issue of privacy. Privacy concerns have increased over the past few years With the number of data breaches of big organisations, leading to the leakage of sensitive user data entrusted to these organisations. The most common data privacy concerns observed thus far are:

1. Infiltration of privacy due to breaches:

It happens due to inadequate and weak passwords, using software that is not updated, or a result of target hacking or malware attack. These breaches result in the leakage of sensitive information and reputational harm to Companies managing the data.

2. Data discrimination:

Data collected can have indicators that may reveal the demography of an individual and thus may result in certain profiling of people of certain demographics. This results in outer world biases entering the sphere of Technology and discrimination under the veil of objective technology-supported solutions. This profiling can be based on race, caste, gender, or ethnicity and can result in perpetuating biases.

3. Data brokerage:

Selling Unprotected and incorrect data is referred to as data brokerage. There has been a practice of gathering falsified information, which results in the creation of flawed and fake algorithms tending to certain categories of people. The veracity of such information and its credibility should be used before relying upon it for insights.

4. Conflict with copyrights and patents:

In setups driven by big data obtaining patents becomes a challenging task. It is because of the difficulty in establishing the uniqueness of the object at hand due to large volumes of data being considered. While w.r.t copyrights, it becomes a huge task since big data involves manipulating and handling data.

Big Data Privacy

It involves properly overseeing big data to mitigate risk and secure sensitive data. Big data comprises large sets and volumes of data, making it difficult for traditional privacy compliance mechanisms to manage. Therefore, creating a framework for managing big data privacy is important. This framework should be made capable of overseeing the Variety, value, volume, and veracity of big data and execution of necessary processes like transfer, analysis, and linkage of data.

This framework can be premised on processes such as De-identification, which involves modifying data into a generalised form to remove identification markers from such data and protects individuals from data leakage. Other recent techniques include Differential privacy, which requires access to a database containing useful information or data without publicising the identity of the individual or source.

Other techniques to ensure big data privacy include separating critical data from general data and providing specific guidance for managing both categories of data, performing risk assessments to check potential slip-ups, and using data masks to hide critical information from people without legitimate access to such information.

Conclusion

In the modern age of the Digital economy, the role of big data in driving organisations is increasing rapidly. Enhanced insights backed by data allow organisations to improve at multiple facets in lesser time and product/service to the customers. Though with emerging personalised services/products, what is also occurring is a grave threat to the privacy of individuals.

Increasing incidents of data leaks and data breaches reflect the lack of a big data privacy framework utilised by large organisations, leading to the infringement of an individual’s privacy rights and reputational loss to the entity managing the data. Thus, in an environment where data is crucial to efficient functioning, data protection must be guaranteed to ensure that an individual’s privacy and rights are not compromised.