Balancing Offensive and Defensive Data Management Strategies: Q&A With Fluree’s Eliud Polanco

Often, enterprise data management falls into two strategies: offensive and defensive. While defense entails mitigating risk, being offensive unlocks new opportunities. Organizations must incorporate both sides to remain competitive. To achieve this, companies can embrace data-centric architectures.

These modern architectures prioritize platforms that enable great data defense in terms of reporting, compliance, and privacy. However, they also maintain a strong data offense related to data monetization, collaboration, competitive insights, experiments, and innovation.

Eliud Polanco, the president of Fluree, a blockchain-backed data management platform that secures highly sensitive information, discussed how blockchain technology can help with embracing data-centric architectures that enable companies to capitalize on both data play styles. Polanco previously served as co-founder and CEO of ZettaLabs, which used AI and machine learning to prepare raw data for analytics use and recently merged with Fluree.

What does it mean for data strategies to be offensive or defensive?

There are different definitions for defensive, but most people describe defense as investing in data protection, information security, the keys to prevent data from leaking, and limiting data breaches. It mainly refers to the use of data for safety use cases, such as using data to produce reports, analytics of health data, or the safety control of organizations.

For example, there are banks that just collapsed, and this is the result of not being able to look at data to determine risk. They needed to be more defensive and focus on using data for regulatory, risk management, etc.

The offensive data strategy drives business value, revenue, and new products. It’s using data in customer relationships, acquisitions, or development, etc. We are seeing a tough balance of offense and defense. For business managers, being offensive is critical, but it is a balancing act.

What are the best practices for maintaining a strong defense and offense when it comes to data management?

The challenge is being able to support the vision to do both simultaneously. There is a natural need to share data to take offense, which counters defensive strategies of hoarding data and being protective. There is new technology designed to address our mission statement of sharing data more securely.

With new Web3 and decentralized tech, there are novel and interesting ways and techniques emerging to approach this that traditional databases don’t address.

How can blockchain technology help with embracing data-centric architectures that enable companies to capitalize on both data play styles?

Data-centric technology means transforming how data is created, saved, protected, and used more securely. A good way to look at this is to compare what people do today to what they need moving forward.

With app-centric architecture, data is a byproduct of this application. The decisions around how data is created and saved are made by the application.

On a practical basis, saving data using new data-centric databases takes advantage of Web3, blockchain, distributed tech, cryptography, and the trustless nature of blockchain.

Every piece of data in the chain is managed via a smart contract that outlines who owns data, permissions to access and change data, and more. All that information is in the contract visible to every party.

The idea is using the contract of the unit to transact with data. So, with a data-centric architecture, in theory, anyone in network could read, write, or own a piece of data when everyone agrees on the “rules of the road.” The use of smart contracts for control is the next generation of sharing and saving data.

By also using semantics, we can make data easy to understand for different organizations as well as making it easy for AI to interpret data. So, not only is data managed and maintained through contracts, but you can also manage data for interpretation with semantic ontologies and knowledge graphs to save information in a way that’s accessible to other teams and algorithms. It’s a combination of those two things, cryptography, contracts, plus semantic standards, RDF triples, ontology, etc. All this combined are opening new avenues to meet these requirements.

How can Fluree help with this?

We are taking these principles and building them into our product suite. Fluree offers a Web3 database that’s built on the same kind of cryptography, with smart contracts technology merged with semantics. It gives users the ability to save data being read and interpreted through natural language processing as well as the security and control policies from a trustless architecture.

It’s not easy to take legacy data and put it into triples to make it machine-readable. We have a solution called Fluree Sense that focuses on how to take legacy data and convert it into semantic forms to be understood by people and systems.

What can organizations do to respond to these issues in 2023?

I recommend companies look for specific data assets and challenges around offensive and defensive strategies within their organizations. There are four different domains where data needs to be shared but the most protected. This includes data around customers, products, accounts, and transactions. Look at how you’re currently managing data and where the biggest risks are. Exploited data is useful on offense and risks defense.

Take advantage of data-centric architecture to protect and share your data. Put together a strategy to focus on those assets. Move the assets from legacy architecture and convert them into a common data-centric, secure platform.

What does the future hold in this area?

I think we’re in a relatively new space. There is a new wave of innovation around companies producing data-centric solutions and databases around the concept of sharing data first.

There is a shift away from having a relational database tied to an application, tied to a platform with data in the middle where applications come and share from it.

When focusing on collaborating first versus just saving data, we are reframing from the ground up how databases work. As we build databases as a collaboration around data, we won’t see the need for having to have so many different databases within a stack.


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