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Cyber Security
Data & Cybersecurity for Robust Defense Against Cyber Attacks
Highlights
- Protecting consumer privacy and confidential data is of utmost importance while defining an enterprise data monetization strategy.
- Enterprises produce exponential data. While they assess how a consumer’s information can be monetized, they are also increasingly responsible for protecting it.
- Our strategic framework with data protection controls can help organizations secure not just their data but also their customers.
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Securing data
Focus on data privacy and security to reap optimal benefits from data monetization.
We see widespread adoption and maturing of data monetization strategies among companies in the consumer, retail, travel, hospitality, and other sectors. Data monetization is not just a supplemental revenue stream but a primary source of revenue for these sectors. According to a report by market research firm Research and Markets, the global data monetization market is estimated to grow from $2.1 billion in 2020 to $15.4 billion in 2030, which is a 22.1% increase in compounded annual growth rate (CAGR). In 2022, 39% of organizations experienced a data breach in their cloud environment. To monetize data in a strategic way, companies need to address data privacy and security concerns. A lack of transparency and confidentiality or conflict of interest can hamper the protection, privacy, and security of data. If these concerns are left unaddressed, it can put the free flow of data between data monetization players—entities such as data governance intermediaries that support the monetization workflow—at risk.
Another concern is that the absence of appropriate security measures makes large databases used for data monetization more vulnerable to attacks. Safeguarding sensitive information like personal identifiable information (PII) is critical to avoiding data breaches and cyberattacks.
Increased global and regional data privacy and protection regulations have also put pressure on companies to be compliant. If not, they can miss opportunities, damage their reputation, or open themselves up to lawsuits or fines. Hence, the privacy and security of data need to be considered when companies define, build, and operationalize their data monetization models.
Measures to address data security challenges
With heterogeneous, complex infrastructures and stringent compliance, and regulatory requirements, data security has gained utmost importance.
To address data security challenges in IT infrastructure, organizations need to put in place the following:
- Data monetization business models: Choose business models that offer a mathematical representation of real-world scenarios. These can predict the outcomes of trends—such as increased sales of certain items during festive seasons—and analyze buying patterns of customers.
- Data protection and regulatory compliance: Comply with regional and global compliance requirements – which is especially important when organizations operate in multiple geographies, are in heavily regulated industries such as healthcare, or handle PII, payment card industry (PCI) data, and protected health information (PHI).
- Data monetization strategy: Build a data monetization strategy based on the industry, data life cycle, and expected outcomes. Here, expected outcomes refer to building new data-driven product lines, scaling up existing products, and enhancing user experience. Also, organizations that choose to monetize their data directly reap more monetary benefits than if they monetized from indirect sources such as enhancing existing products and services, reducing costs, etc.
- Data management and governance: Set up standards and policies that apply to how data is gathered, stored, processed, and disposed.
- Data protection controls: Privacy-enhancing technologies (PET) such as homomorphic encryption and secure enclaves map niche security technologies with commercial readiness to ensure that sensitive data is not exposed to other entities during the data-processing life cycle. Moreover, a data-centric protection and monitoring strategy addresses data security and privacy challenges. This calls for a blend of data protection controls with unified policy sets for effective data monetization. These controls include secure multi-party computing (SMPC), confidential computing, PET, privacy preservation techniques (PPT), data anonymization for encryption, tokenization, and masking of data.
Strategic framework
Our data monetization approach offers a strategic roadmap and aligns the organization with a data handling model.
In this section, we expand on the measures mentioned above to provide a detailed perspective of an organization-specific planning tool. It details the decisions, processes, activities, systems, and metrics for the development of data monetization as a business discipline. The following are the key components:
- Data assessment: Identify internal and external data sources. Then, centralize data by developing a platform that can collect all necessary data in one location. We recommend that companies build or buy a platform where both internal and external parties can have rights to populate data. Once the data is centralized, consolidate it into as few dashboards or interfaces as possible. This makes it easier to perform analysis and deliver value to the intended stakeholders.
- Selection of an operating model: Implement an operating model that will fit the business. There is no one fit-for-all model. Companies often begin with internal data monetization before progressing to monetization with external parties. However, data may be more valuable and applicable for outside companies than within the company.
- Compliance with governance and regulatory requirements: Strictly adhere to local and global data privacy compliance and regulatory requirements throughout the data processing life cycle. When offering data monetization services, it is essential to implement data protection measures throughout the data processing life cycle, thereby imparting confidence for third-party stakeholders.
Path to value
The strategic framework, combined with adequate security controls, can secure data monetization.
No doubt, data monetization can be of significant value to organizations. But to get there, they need to assess their data security posture. With adequate security controls in place, they can make use of vast data assets for data monetization in an ethical manner. And to derive optimal benefits from data monetization, they need to adopt advanced technologies for data protection and periodically review achievement of their data security goals.
