Challenges
Challenges in implementing and managing a robust Data Value Chain
Despite the availability of numerous scalable and dependable technologies in the data space, the combination of these technologies often results in a fragile end solution. Only select big-tech companies have successfully mastered the processes of generating, consuming, and utilizing data reliably at any scale—examples include Google, Facebook, Netflix, Amazon, and LinkedIn. The existence of reliable data platforms facilitating a robust Data Value Chain has served as a significant distinguishing factor for these companies. And none of these companies have shared their end-to-end solutions with others, only a handful have released some of the tools they internally use, such as Facebook's contribution with Cassandra.
A pressing issue for many organizations is the substantial effort required to develop, operate or maintain an end-to-end data solution reliably.
The challenge of reliably managing the data value chain is growing for numerous companies, particularly as contemporary products generate substantial data volumes, even with a relatively modest user base.
Key Challenges
The challenges faced by most of the organizations in operating a data value chain mainly falls into one of the following four categories:
Time: Significant amount of time mis-spent in managing the solution rather than in leveraging data's full potential
Cost: High upfront CapEx and running costs
Capability: Challenges to build, manage and operate complex data technologies & systems
Risk: Business & technical risks due to propietary & fragmented solutions, rigid & less reliable systems
Listed below are some challenges that are often faced by organizations with data platforms & solutions:
Comprehensive, ready-to-use solutions for implementing the entire data value-chain are scarce. In many instances, organizations resort to employing extensive teams of Data and DevOps engineers to build these solutions.
Data Analysts consistently grapple with challenges related to data integrity, quality, and accessibility, primarily due to the dynamic and evolving nature of data.
Data Engineers spend substantial time resolving reliability issues due to the agile nature of data and interoperability challenges between components of data platforms.
Inherent complexities of data platforms, when exposed, increase timelines for new pipeline creation, increasing the lead times for generating data insights.
Organizations find themselves compelled to transition to a new data solution as they grow and have diverse data use-cases.
Majority of existing managed solutions, if not all, bind users to proprietary data tools and formats, creating a vendor lock-in.
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