Trust and Data Analytics: Protecting Privacy in Analysis

Technology gave rise to Data Analytics, but it can also become the cause of several privacy issues.

Data Analytics is progressively changing the world. Businesses, governments, and different organizations are unlocking their true potential with the help of Data Analytics. Real-time analytics provides swift solutions to complex problems, enabling faster decision-making, and accelerating investments across all industrial sectors.

In any industry, using data for decision-making is related to the customers' trust in the organization. While using Data Analytics, these data sets combine information about someone's lifestyle, habits, behavioral patterns, and so much more. Companies use this personal information to amplify their growth. The ability to leverage this information, identifying these patterns, and creating personalized communication has reached a new degree of sophistication that was never thought of before.

Risks involved in Data Analytics

In this age of cloud computing, data owners have to keep up with the recent technological advancements and the privacy threats to this information, and the regulations concerning the privacy of sensitive data and personally identifiable information (PII). With more data comes the risks of privacy breaches that can lead to financial losses for the company and loss of its clients' trust.

Data privacy is related to the clients' trust. The more data the company gathers, the more efficient it becomes to create the profile on customers' lives and preferences based on their current reactions towards a product or service, their preferences, and eventually predict their future behavior. Since organizations store different types of sensitive data for long periods, therefore they are under constant pressure to be transparent about the data they collect, how they use it, and how they retain it. To acknowledge these demands, organizations need reliable and scalable data privacy tools to encourage and help people access the stored information, correct and even remove any incorrect or sensitive information. The volume of data collected by e-commerce and OTT platforms generally involves personal financial details and requires extensive data privacy strategies and tools.

Some other examples of risks are:

• Unauthorized disclosure or data theft is a threat to privacy, and it becomes more serious when the data set contains identifiable and centralized information. Unauthorized disclosure of private information can also put public safety at risk.
• Secondary use of data can also raise trust issues between the company and its customers. An organization should only use a client's personal information to fulfill the purpose at hand. Any secondary use of the data for analytics; without the client's consent can be considered as a privacy breach.

Despite several significant investments and measures taken to improve the data and analytics tools and applications, organizations still lack the confidence to implement analytics in their business operations. Several decision-makers feel that their lack of knowledge about Data Analytics would harm their organizations and are reluctant to implement it.

How to Gain the Customers' Trust?

The process of gaining clients' trust can be intimidating but, it also helps by starting simply through trust-building and gaining their confidence. Being transparent with the customers can help ease their doubts.

Privacy by design (PbD) was developed, keeping in mind the need for robust data protection to encourage data-driven innovations. Based on this framework, several companies are taking advanced privacy measures while pursuing Data Analytics, which includes:

• Data minimization: No personal information is collected unless the cause is specified and is compelling enough, but it is gathered only after eliminating all the privacy risks.
• User access controls: It is a set of processes that grants and denies access to information. It is built based on the security policies followed by the company.

It is entirely possible to achieve privacy in this data and analytics era. Big data and AI unlock insights and innovations that help organizations to move forwards and grow. Companies should build privacy protections into their technologies that can be included in their business and operational processes.