The Marketers Guide to Hybrid Data Models

Hybrid data models enable enterprises to start building the automated marketing data infrastructure of the future

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    Marketers’ roles are inextricably linked with data. The big data revolution has unleashed torrents of terabytes about everything from customer behaviours, demographic consumer shifts in emerging markets and how well the last marketing campaign performed across channels. 

    When marketing teams are actively involved in decisions about data infrastructures, pipelines and processing – they stand a better chance of leveraging Martech tools to get the maximum business value. As data takes a seat at the table, technology-related debates are no longer restricted to the IT team. For example, the argument between cloud and on-premises solutions affects all. Marketing teams should know what the repercussions of such decisions are.

    Marketing priorities remain the same. For example, customer experience is king. Measures like performance, cost, security, and compliance must all be rolled up to determine how that impacts customers, including internal users. Great experiences often rely upon real-time data to impact customers in real-time. Retailers aim to present customers with personalised offers while still in a store. This not only requires instant access to data but also multi-function analytics across multiple platforms and models, including CRM, inventory, promotions and more. A hybrid data model infrastructure makes this possible through one-point access to process structured and unstructured data sources.

    A single point of service

    According to Mordor Intelligence, the market for data lakes was valued at $3.74 billion in 2020 and was expected to reach $17.60 billion by 2026. However, companies solely relying on data lake strategies eventually faced critical limitations in their agility and innovation abilities. While data lake is an easy and cost-effective way to aggregate data from multiple silos, making it accessible to analysts, the problems with such an approach include a spectrum of data quality, lineage, governance and security challenges. The cloud alone is not cost-effective for storing high-resolution, time-series data generated by machines and process equipment. So, how do we leverage the power of cloud-based analytics tools on the vast amounts of data derived through modern sources?

    A hybrid-data-management model could be the answer. Hybrid models use analytical technology near the source of the data (in a corporate data centre) and move the relevant data at the right speed to the cloud for analytics. Machine and process data are often collected at one second (or faster) intervals. Hybrid models such as hybrid clouds are becoming central to successful digital transformation efforts by defining an IT architectural approach, an IT investment strategy, and an IT staffing model that ensures the enterprise can achieve the optimal balance across multiple dimensions without sacrificing performance reliability or control.

    The hybrid advantage

    A hybrid data model makes it easy to move across data sets and workloads, enabling an easy switch between any location, whether it be multiple public and private clouds or on-premises systems; centralising the available data management. Hybrid data models let enterprises start building the automated data infrastructure of the future.

    Here are a few key benefits of a hybrid data environment:

    Security and Governance: Hybrid data models enable a write-once/run-anywhere approach for efficient data management. Public security models might vary, but data teams do not have to implement a different security model each time. Instead, a hybrid model centralises security and governance across all environments, audits and monitors user activity and access to meet compliance requirements. Hybrid models offer the flexibility to ensure GDPR compliance by locating data and workloads wherever they need to be.

    Workload Management For Cost Optimisation: According to a report, 57 per cent of organisations say hybrid is the organising principle today for their IT environments; the cost of running workloads in the cloud leads to cloud repatriation, forcing them to move workloads back on-prem. Further, a particular workload may perform better in one model environment than another, and the cost of running a workload may vary from the type of cloud being inculcated and the region. A hybrid data model makes moving workloads easy and optimising for cost.

    Establishing a hybrid data environment starts with analysing an integrated framework of requirements such as security and compliance, cost and performance issues, and types of required workloads capabilities. These requirements must be first fully understood and codified, so a supporting platform can be combined to provide assistance or automate the business model, balancing all the competing demands to ensure the delivery of the desired customer experience.

    A fully automated platform that optimises where workloads should run will be significant whenever it arrives in the near future. But, continued reliance on legacy data lakes limits abilities to innovate and accelerate business infrastructures. A hybrid data model can enable the transformation of your business by letting you move data and workloads where they need to be optimised for performance, cost, and customer experience while ensuring security and compliance on a global scale.


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