A decade back, when the big data trend began, the mantra was to collect more and more data — then glean insights from it to better understand consumer behavior, market trends, and demand. Even today, big data is key to better decision-making and operational excellence; however, two phenomena challenge the notion of “collect as much data as possible”: data privacy and regulations.
The enforcement of major data regulations — such as GDPR and CCPA — has meant that data collection, processing, and management attract additional compliance costs, in addition to securing the data from insider threats and cybercriminals. Moreover, more and more jurisdictions are only introducing new regulatory frameworks to protect consumer data, limiting enterprises in what data they can collect and where they can store it.
On the other hand, average consumers are paying more attention to and are conscious of their online privacy and being selective about what enterprises they interact with and share information with.
While enterprises have technology solutions at their disposal to collect data about every consumer interaction on their apps and digital ecosystems, today’s data privacy concerns and regulatory landscape require enterprises to rethink:
- How much data to collect
- How to process and store the data
- Implementing a comprehensive compliance strategy that incorporates the scope of multiple data regulations
In essence, information security and data privacy are two different aspects — however, a few aspects of security and privacy are interdependent. For example, data breaches almost always affect consumer privacy governance inside an enterprise.
In this paradigm shift, collecting and storing more data can increase costs exponentially without justifying the positive impact on enterprise profitability, growth, and sustainability. The strategic thinking and decision-making around data management should essentially be turned around. Rather than collecting data at first and later embedding it into analytics, decision-making, and research and development, enterprises should map their goals and opportunities and then collect the data needed to achieve them and pursue opportunities. In analogy, this is more like building a reliable combustion engine and then focusing on extracting oil more efficiently rather than extracting oil first and then focusing on where it can be consumed.
Originally published at DataVersity