By David Loshin
Big facts Analytics will help managers in offering an outline of the drivers for introducing titanic information expertise into the association and for figuring out the kinds of commercial difficulties most fitted to special info analytics ideas, realizing the price drivers and advantages, strategic making plans, constructing a pilot, and finally making plans to combine again into construction in the enterprise.
• publications the reader in assessing the possibilities and price proposition
• assessment of huge facts and software program architectures
• provides a number of applied sciences and the way they healthy into the large info environment
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Additional info for Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph
Data streaming: Where are the datasets sourced, and will they be provided on a streaming basis? If so, that means scaling up the networking and I/O capacities to support the eventual speeds and volumes. • Computation: Many statistical analysis algorithms have computational requirements that are proportional to orders of magnitude of the data provided, and in exponential algorithms, the demand ramps up very quickly. A small sandbox may not exhibit the same computation demand as a larger environment, so understand the need for computational performance and how it needs to scale.
6 ROLES OF ORGANIZATIONAL ALIGNMENT Executing this sequence in alignment with organizational needs requires people who can champion new technologies while also retaining a critical eye to differentiate between hype and reality. Aside from the boots-on-the ground folks like big data developers and data scientists, below are some roles of organizational alignment played during the consideration, evaluation, and decisioning process for assessing the value proposition for big data: • Business evangelist: This individual understands the types of performance barriers imposed by the existing technical infrastructure and Achieving Organizational Alignment for Big Data Analytics • • • • • 27 understands that ongoing reviews of emerging technology may create efficiencies that do not currently exist within the organization.
Defining “big data governance” policies around these practice areas (managing consumer data expectations, defining critical data quality dimensions, managing the consistency of metadata and reference data, allowing for data repurposing, and data enrichment and enhancement) can help you institute a level of trust in analytical results. 6 THOUGHT EXERCISES Given the premise of data governance for big data, here are some questions and exercises to ponder: • What are the three most critical dimensions of data quality your business data consumers care about for analytics?
Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph by David Loshin