Unlocking Business Value from Data Analytics Without Compromising on Security

No-one can dispute the outsized role cloud-based data analytics is having on modern business. There’s a reason the market is predicted to grow at over 20% CAGR over the coming years to reach more than $86bn by 2028. It empowers users with deeper customer and market insight so that they can make smarter business decisions. Yet the same data can be a security and compliance risk if not properly managed. The problem is that many data analysts find their projects torpedoed from the start by over-zealous colleagues.

So how can organizations navigate their way around these challenges, to ensure they don’t miss out on potentially

A data-driven world

Data doesn’t lie. If organizations can collect it in large enough quantities, and use it intelligently enough, they could extract tremendous value. For pharmaceuticals firms it could mean the development of new life-saving drugs. For financial services it could drive more effective fraud prevention to mitigate financial and reputational risk. Whatever the vertical, business users are putting increasing pressure on their colleagues in IT to make this kind of vision a reality. They may have already invested in powerful cloud-based tools. But then comes a familiar roadblock: data security and compliance concerns.

These might severely restrict access to the data needed to power cloud-based analytics. The result: analyst teams and users from marketing, sales, finance and other departments are unable to unlock business value, drive better predictive insight or make smarter decisions. It also means businesses can’t outsource their analytics services via third-party providers, or monetize data by it sharing with business partners to build new products and services with.

Opening the door to possibility

It doesn’t need to be like this. What if your business could:

1. Run analytics on encrypted data – thereby empowering users to generate meaningful insight without security and compliance concerns?
2. Enable access to data for any business user and analytics tools of your choice in a controlled fashion? This would help unlock value for data across the entire enterprise and be flexible enough to adapt to changing requirements without introducing additional risk
3. Share sensitive data with your partners in a compliant fashion? This would help businesses utilize external, advanced analytics services or monetize data, without incurring any extra risk of data loss or leakage

How to get there

Data-centric security is your first step on the road to running secure, compliant and high-performing data analytics programs. It means rolling out technology such as format-preserving encryption to apply the strongest protection to that data without impacting its utility. But beyond the high-level view, what will it take to achieve the benefits listed above?

1. Protection of all relevant fields to preserve privacy, while maintaining utility with state-of-the-art techniques. Preservation of core database functions such as referential integrity to ensure utility of data sets for complex analytics needs.
2. Application of zero trust at a data level—only provide access to sensitive data when policies allow and keep it protected/encrypted at all times, including when in use. Enforce centralized, fine-grained access controls via integration with Identity and Access Management (IAM) solutions.
3. Continuous data discovery and identification of sensitive elements. Protection of all relevant fields to preserve privacy while maintaining utility with state-of-the-art techniques. Utilize multiple protection technologies from pseudonymization to full anonymization, to preserve privacy and while sharing data.

comforte AG’s Data Security Platform brings these capabilities to life for global organizations. We deliver not only advanced data protection technology such as format-preserving encryption but also highly flexible deployment options. That latter ensure customers are able to protect data as early on as possible without compromising on their analytics goals.