Hamster on the Data Wheel: The Last-Mile Gap for Data Monetization

Poornima Ramaswamy

Ever felt like a hamster endlessly spinning on a data wheel? The journey to data monetization is paved with challenges, and for many, data privacy emerges as the formidable “last-mile” hurdle. As a strategic advisor to Anonos, I’ve had the privilege to witness firsthand how advanced, future-proofed data privacy technology like Data Embassy bridges the last-mile gap from data modernization to monetization. Read on to learn more. #datamonetization #dataprivacy

How do we stop being data hamsters on a modernization wheel and get to monetization?

Most organizations are in limbo on their data journey and can’t monetize their data because of a last -mile problem. The last-mile problem is data privacy which is constantly evolving. When we have created technologies for all the other aspects of data, why don’t we have modern future-proofed technology for data privacy? Or, do we have it, but we just don’t know about it?

Let’s look into Anonos’ globally patented Data Embassy software for a single toolbox automated approach to Data privacy.

Read on to learn more…

State of Data

Most organizations today have a problem of plenty – a lot of data, in a lot of places, in lots of formats, but representing more potential that still feels unrealizable.

For years we have touted the benefits of having all the data we can grab, both internal and external, pulling it all together, looking at it holistically, then further slicing and dicing it to create new data-driven models, products, services, customer segmentation, market segmentation, pricing strategies etc etc. Yes, these are all the various possibilities. The utopia for most organizations is not just improving their customer experience and personalization, driving internal operational efficiency, strengthening their risk posture, or creating better products but it is in creating new revenue streams out of the data they are sitting on. The value of their data can exponentially multiply in value when combined with other data they have access to or can get access to. As an industry, we have spoken about this utopia for at least the last 10-12 years since the time the term Big Data was coined.

So why haven’t we been able to do this, since technologies to process, harness, clean, and access data have rapidly matured? And now, with GenAI, there is excitement and anticipation in the air, but it still feels like we are like hamsters on a wheel when it comes to data. And why do CEOs and boards have data investment fatigue and data skepticism?

Laying out the Case for Data Privacy

I believe it’s because we have a last-mile problem. All the technologies that have matured help us drive better data platform modernization and democratization i.e., easier access to available and allowable good-quality data. However, to go from data democratization to data monetization, we need to solve evolving data privacy needs not just because “regulators say so” but to earn the trust of our customers, consumers, suppliers, employees, and communities. Until we can confidently prove to all our stakeholders that we treat their identity like we will treat our personal identities, we won’t realize the full potential of data.

So what is the state of data privacy?

Today data privacy is an area of expertise in the hands of few – both too few and fragmented across roles in organizations and society. It’s everyone’s concern, and everyone’s problem to solve but no single person / role is held accountable to solve it. Today it is addressed through a combination of committees of people interpreting the needs and laws and solving for it through process changes. We are relying on people and process-driven approaches to solve the last-mile problem. What if we can add technology to this mix? What if we can transform these laws into a set of configurable, governed, and auditable controls that can be applied either at the individual use case level or at an enterprise level while being confident that data usability and usefulness are not compromised? All this while ensuring there is no way to reidentify any entity or stakeholder by anyone but the owner of the data, and even then only if and when needed.

Anonos’ globally patented Data Embassy is that platform. It’s a modern data privacy platform that allows for data to be protected at various levels of granularity to meet all the regulatory and privacy laws through a low code toolbox approach that makes available multiple privacy enhancing techniques, including award-winning synthetic data and pseudonymization as defined under increasing numbers of privacy laws.

Key philosophies that have guided the creation and perfection of this platform over the last 7 years of R&D and 3 years of MVP testing and refinement are:

  • Low code automation heavy
  • Centrally configured and governed, but locally executed controls
  • Toolbox approach to data protection – Access to multiple data protection techniques enabling for easy adoption and adaptation to comply with new privacy laws and court rulings
  • A single platform that creates a common vocabulary for effective communication across privacy experts, compliance & security officers, data owners, business stakeholders, and IT leaders
  • Without compromises or limitations on –
  1. Data processing performance
  2. Data usability, availability & data accuracy
  3. Data residency, on-prem / on cloud
  4. Data movement within and across organizations
  5. Data frequency – i.e., in batch or real-time situations

Data Embassy platform – How does it work?

Data Embassy platform creates a privacy-enabled version of data called “Variant Twins” either at a use case level or across use cases depending on the situation. The Data Embassy platform can create different Variant Twins for the same data by applying different privacy controls to a combination of attributes based on the use case. This approach allows for data privacy to be applied at rest, at use, and during processing as well.

Despite all the privacy controls being applied, the Variant Twins retain the full meaning and context of the data so the data can be used for further processing across the full spectrum of AI, ML, and Gen AI. In addition, if the owner of the data needs access to the source data in its original form (aka cleartext), the technically enforced and auditable governance capability allows for such access.

But that’s not all! In the AI and now Gen AI world, we all know the possibilities and opportunities are endless. Besides privacy & security, it’s important to ensure the data is well represented in all dimensions and scenarios to create more inclusive solutions. This is sometimes possible only with synthetic data. Anonos has this capability in-built as well. For scenarios where there is not enough training data, Anonos can create privacy-enabled synthetic data for more real-world and future-world representative models.

Anonos’ has 26 patents (and growing) to help organizations confidently pursue their goals of data monetization and creating data-driven businesses.

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Poornima Ramaswamy

Founder, Pivot X