In today’s cloud-first environment, data is generated at an unprecedented rate, rapidly increasing the scaling abilities of organizations across different industries the world over. The emergence of cloud computing, increasing adoption of BYOD and acceleration of remote work have been highly beneficial for organizational productivity, efficiency, and connectivity. However, due to a lack of efficient cybersecurity and data validation solutions, as well as limited overall visibility of generated data, organizations face an increased risk of data leakage, spoofing, and duplication. Consequently, unauthorized endpoints (devices and instances alike) can access and utilize said data, diminishing its overall usability and authenticity. This can lead those organizations to make costly decisions based on erroneous information. This issue is exacerbated by the exponential increase of complex data points generated via IoT, mobile devices and AI, which make it seemingly impossible for organizations to sort, analyze, and incorporate accurate information into actionable plans. 

Currently, inaccurate data, cybersecurity breaches and data leaks cost the US economy over $3 trillion each year, and organizations that fall victim to such circumstances suffer both financially and in terms of their reputation. Bad data leads to worse decisions, and as such organizations must establish robust data security infrastructures to avoid these potential threats. As it stands, many industries utilize traditional, antiquated data security infrastructures that lack the versatility necessary to scale and provide visibility of data generated, stored and accessed. As such, there is an emerging need for highly scalable solutions capable of tracking, validating, and securing organizational data. This need has become ever more urgent due to the proliferation of sensitive new information created daily that requires efficient data management. 

Organizations of all sizes should be able to set up their own data security infrastructures, thereby streamlining their data pool management capabilities to overcome issues around cybersecurity. This would eliminate the need for one-to-one access relationships, and instead allow automation from network-based policies and APIs. One way to do this is through blockchain technologies; however, traditional blockchains are latency-prone and proven to lack scalability, making them inefficient to secure and validate large sums of generated data. 

From a functionality perspective, distributed ledger technologies (DLT) are better able to support economically viable and accepted data management solutions that are scalable and able to manage complex and large data sets. DLT networks provide a normalized aggregation layer that allows all nested sub-networks of an ecosystem to exchange data without breakage, resulting in interoperable access and provenance. By adopting DLT to secure their open source networks, organizations are able to meet their growing need for comprehensive data security and validation while remaining ahead of the technological curve. 

Constellation’s Hypergraph provides a DLT-based data management solution that addresses issues around scalability and cybersecurity, enabling organizations to scale at ease while generating large sums of data. By deploying Constellation’s SPORE platform, which comprises infrastructure tools within the Hypergraph product suite, organizations are able to cryptographically secure sensitive information with varying levels of clearance. SPORE sits inline between data pipeline management tools such as Apache Storm and Kafka, providing users with ultimate visibility of their generated data, and offering security, validation and notarization across big data initiatives like AI, enterprise software, and edge computing for IoT and mobility. By allowing administrators to validate and notarize information across these initiatives, SPORE grants complete governance over data.

As a core aspect of Constellation’s product suite, SPORE is geared towards funneling and managing data as it is added to the network and accessed. SPORE is an application layered on principal infrastructure tools, enabling app developers to create state channels and validate and notarize data at scale within hosted networks, data science notebooks and graphical user interfaces. By integrating with existing solution stacks, SPORE is able to swiftly prevent corrupt data from infecting organizational systems and resulting in streams of bad logic. This leading DLT solution provides visibility on the fly into enterprise data pipelines, with an added layer of defense in the form of validation via easily integratable APIs. Through its single dashboard, users gain access to granular analytics and robust insights, enabling them to make actionable plans. 

Currently, Constellation is engaged with the MOBI Data Marketplace working group to define industry standard requirements regarding the exchange of mobility data. An appropriate network design with homogenized data frameworks is critical for optimizing a DLT-based mobility marketplace to enable the exchange of data of varying levels of purity (raw-to-processed datasets), and Constellation’s DLT architecture will facilitate security for exchanges of exponential data in the mobility sector between private enterprises and public agencies alike. 

Click on the link below to learn more about how Constellation is quickly becoming the standard for processing, validating, and securing enterprise data as it is being created and communicated across networks.