By Anthony Zech, Data and AI Community of Excellence Director
For years, enterprise organizations of all sizes have pursued a data-driven approach to business operations, with goals of increased efficiency, reduced costs, and improved stakeholder experiences. However, there’s a strong argument to be made that traditional data platform architecture is centralized and monolithic – preventing organizations from realizing the full benefit of their data.
ECS’ experts provide an alternative: a distributed model of data architecture, data mesh that empowers data producers and increases the agility of your operation.
Traditional Data
Management Falls Short
Large-scale data collection and analysis has long been a major component of business operations. The prevailing architectural model has been a centralized model, which ingests data from many disparate domains to create a central repository, often referred to as a “data lake.” This model transforms structured and unstructured data into useful information for stakeholders. However, centralized architecture also has critical problems that tend to scale up with the size of the enterprise and the amount of data being processed:
The “Data Swamp” Problem
As data lakes grow more complex, meaningful analysis requires increasingly specialized personnel. Security risks and access control problems can also arise as data lakes grow.
The Slowness Problem
Centralized data architecture is slow to update, in turn slowing down artificial intelligence (AI) training and development at the local level.
The Uneven-Focus Problem
Data teams can’t give every data producer the same priority of focus all the time, meaning some will inevitably produce suboptimal data products.
Increased Data Agility
Data mesh is a distributed model of data management that bypasses the data lake concept by putting control in the hands of the data producers at the local level. There are immediate benefits to this paradigm shift:
Information is closer to the source.
Data products are timelier, more accurate, and managed by a team that understands the domain/functional area.
The data team facilitates the work of data producers.
This reduces the bottlenecks, replication delays, and schema issues that plague traditional models.
Faster AI evolution at the local level,
which creates a loop of better algorithms informing better data products.
Crucially, while distributed architecture does away with the size and speed issues of the centralized data lake, it still maintains a central data catalog for locating and understanding data. Distributing your data management doesn’t silo it – it democratizes it.
ECS – Applying A Data Mesh Approach
ECS utilizes a data mesh approach to data management, ensuring better quality data for AI development while still adhering to strict security standards. Our AI/ML solutions have helped organizations develop computer vision models for unmanned vehicles, perform sensitive intelligence and business research, and protect our nation’s critical environmental assets.
Ready to democratize your data management? Reach out to our experts today.
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