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Introduction
Customers want to realize the value held in the data their organization generates. Common use cases include helping them expedite decision making, publishing data externally to foster innovation, or creating new revenue streams by monetizing the data.
Organizations that successfully generate business value from their data, will outperform their peers. An Aberdeen survey saw organizations who implemented a Data Lake outperforming similar companies by 9% in organic revenue growth. These leaders were able to do new types of analytics like machine learning over new sources like log files, data from click-streams, social media, and internet connected devices stored in the data lake. This helped them to identify, and act upon opportunities for business growth faster by attracting and retaining customers, boosting productivity, proactively maintaining devices, and making informed decisions.
To best realize this value using data lakes, customers need a technology cost breakdown for their budget and realize the best value of their solution. But without building the solution, they don’t know how much it will cost. This is a common paradox that delays many customers from starting their data lake projects.
A data lake is a common way to realize these goals. There are many considerations along this journey, such as team structure, data culture, technology stack, governance risk, and compliance.
Costing data lakes requires a different approach than delivering them. Customers must focus on identifying and measuring business value early on so that they can start their projects quickly and demonstrate the value back to the business quickly and incrementally.
What should the business team focus on?
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Measure business value – Without measuring business value, it’s hard to justify any expenditure or drive any value from your testing early on in the project.
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Prototype rapidly – Focus your energy on driving business outcomes with any experiments you run.
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Understand what influences costs – Analytics projects generally have similar stages, ingestion, processing, analytics, and visualization. Each of these stages has key factors that influence the cost.
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Cost model a small set of experiments – Your analytics project is a journey. As you expand your knowledge, your capabilities will change. The sooner you can start experimenting, the sooner your knowledge will grow. Build a cost model that covers to smallest amount of work to impact your business outcomes and iterate.
