Manish Gupta

Manish Gupta
Manish Gupta is a Principal Product Manager for Data Privacy and Test Data Management at Informatica driving innovation in keeping data safe and still be able to utilize it for testing and other enterprise needs. Manish has worked as product manager and incubated products at Oracle and eGain in the CRM and Enterprise Interaction Management space. Before taking up product management roles, Manish ran large projects for Telcos around the world integrating their CRM applications with backend applications. Manish has an engineering degree from IIT Bombay and an MBA from Leavey School of Management.

Data Obfuscation and Data Value – Can They Coexist?

Data Obfuscation and Data Value

Data Obfuscation and Data Value

Data is growing exponentially. New technologies are at the root of the growth. With the advent of big data and machine data, enterprises have amassed amounts of data never before seen. Consider the example of Telecommunications companies. Telco has always collected large volumes of call data and customer data. However, the advent of 4G services, combined with the explosion of the mobile internet, has created data volume Telco has never seen before.

In response to the growth, organizations seek new ways to unlock the value of their data. Traditionally, data has been analyzed for a few key reasons. First, data was analyzed in order to identify ways to improve operational efficiency. Secondly, data was analyzed to identify opportunities to increase revenue.

As data expands, companies have found new uses for these growing data sets. Of late, organizations have started providing data to partners, who then sell the ‘intelligence’ they glean from within the data. Consider a coffee shop owner whose store doesn’t open until 8 AM. This owner would be interested in learning how many target customers (Perhaps people aged 25 to 45) walk past the closed shop between 6 AM and 8 AM. If this number is high enough, it may make sense to open the store earlier.

As much as organizations prioritize the value of data, customers prioritize the privacy of data. If an organization loses a customer’s data, it results in a several costs to the organization. These costs include:

  • Damage to the company’s reputation
  • A reduction of customer trust
  • Financial costs associated with the investigation of the loss
  • Possible governmental fines
  • Possible restitution costs

To guard against these risks, data that organizations provide to their partners must be obfuscated. This protects customer privacy. However, data that has been obfuscated is often of a lower value to the partner. For example, if the date of birth of those passing the coffee shop has been obfuscated, the store owner may not be able to determine if those passing by are potential customers. When data is obfuscated without consideration of the analysis that needs to be done, analysis results may not be correct.

There is away to provide data privacy for the customer while simultaneously monetizing enterprise data. To do so, organizations must allow trusted partners to define masking generalizations. With sufficient data masking governance, it is indeed possible for data obfuscation and data value to coexist.

Currently, there is a great deal of research around ensuring that obfuscated data is both protected and useful. Techniques and algorithms like ‘k-Anonymity’ and ‘l-Diversity’ ensure that sensitive data is safe and secure. However, these techniques have have not yet become mainstream. Once they do, the value of big data will be unlocked.

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Posted in Application ILM, B2B Data Exchange, Data masking, Data Privacy, Data Security, Telecommunications | Tagged , , , | Leave a comment