Examining Complexity and Security in Building Effective Data Products

The rapid and continuous advent of new, exciting technology for cloud and data management keeps enterprises on their toes, where thorough research and change management is key. However, the adoption of the latest and greatest tech is leaving a certain “lifeblood of business” in the background: the data itself. Ensuring that data can be wholly trusted, accessed quickly, and used with reliability is a fundamental component of data architecture and management success, and cannot be ignored.

Ali Ahmadi, solutions engineer at Cinchy, and Shiva Nathan, CEO at Onymos, joined DBTA’s webinar, How to Build Effective Data Products with DataOps, to explore how DataOps, data-as-a-product thinking, and data fabric architectures are interdependent approaches that fuel data consumers with fast access to reliable, high-quality data and analytics products.

Ahmadi widened the scope for building data products by examining the context in which they serve a purpose. Typically, organizations with hundreds of applications and databases result in siloed, gatekept data, incurring high probability of error due to conflicting data points as well as expensive integrations to connect these datasets. Ultimately, this negatively impacts revenue while also disrupting the customer experience.

Complex ecosystems require simplicity, Ahmadi explained, where “data products are intended to be the bridge between your teams and your systems, to allow access—secure access—to critical information, and [to] enable your systems to have the most accurate, up-to-date data without costly integrations.”

These data products consolidate and liberate the data being produced from a variety of systems to drive greater collaboration—or collaborative intelligence—and ease for the consumer. To build effective data products, Ahmadi emphasized these three tenets:

  • Liberate: Identify core systems holding business data and mobile data engineering and operating teams to liberate this data.
  • Collaborate: Define data product use cases, model the data product as a “digital twin” for your business, test for reliability, scalability, and performance, and ensure that the data product can interact freely with other data and is readily available/easily accessible.
  • Activate: Provision access controls to IT teams, systems, and AI to deliver business capabilities.

Nathan focused their presentation on the significance of data security as it relates to building a culture of data product generation. Data security has approached a new frontier, Nathan explained, which has transformed from securing data at rest and data in transit to securing the data route itself.

Data product creators must contend with the security of the data as it travels from one endpoint to the consumer, where, for example, the security of the summoning cables and networks that transmit data from one place to the other—especially at global scale—plays a large role. In a world where data threats evolve as quickly as data innovation, those crafting data products must consider the route that the data takes and how to secure it.

Nathan offered various examples of companies whose costly data breaches were instigated by poor management of data route security. They highlighted that data product creators must think about where the data is going to be available as well as—in the wake of the proprietary AI and machine learning (ML) explosion—if other organizations are going to use your data to train their AI models.

For the full discussion about building effective data products with DataOps, you can view an archived version of the webinar here.