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Enterprises invest in Big Data technology without specific and measurable business applications tied to the projects. In such cases, largely driven by IT departments, enterprises begin amassing large volumes of data in storage, which is sometimes made available to Data Scientists and business analysts for exploratory analysis, but that otherwise sits underutilized.
Enterprises successfully test Big Data pilot projects, only to find they do not have enough skilled practitioners – including administrators, developers and data scientists – to support large scale production deployments. As a result, technology performance degrades over time as stressed storage and database administrators struggle to keep systems running optimally. Data Scientists and business analysts consequently are unable to perform the quantity or quality of Big Data analysis desired
Qualities Of Big Data Production-Ready Solutions
Big data rookies make the mistake of thinking that implementing a big solution is just a matter of declaring that “the POC is now production.” Think again. IT leaders need to see the bigger picture. Implementing a big data solution requires planning, architecture, talent, flawless execution, and a supreme command of what it means to be production-ready. Production-ready big data solutions must exhibit these seven qualities:
Practitioners involved in successful projects also indicate they realistically assess the level of skilled practitioners inside the enterprise and often engage outside professional services organizations to fill talent gaps and keep systems running optimally.
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