Big Data Adoption and Planning Considerations
Big Data initiatives are strategic in nature and should be business-driven. The adoption of Big Data can be transformative but is more often innovative. Transformation activities are typically low-risk endeavors designed to deliver increased efficiency and effectiveness. Innovation requires a shift in mindset because it will fundamentally alter the structure of a business either in its products, services or organization. This is the power of Big Data adoption; it can enable this sort of change. Innovation management requires care—too many controlling forces can stifle the initiative and dampen the results, and too little oversight can turn a best intentioned project into a science experiment that never delivers promised results. It is against this backdrop that Chapter 3 addresses Big Data adoption and planning considerations.
Given the nature of Big Data and its analytic power, there are many issues that need to be considered and planned for in the beginning. For example, with the adoption of any new technology, the means to secure it in a way that conforms to existing corporate standards needs to be addressed. Issues related to tracking the provenance of a dataset from its procurement to its utilization is often a new requirement for organizations. Managing the privacy of constituents whose data is being handled or whose identity is revealed by analytic processes must be planned for. Big Data even opens up additional opportunities to consider moving beyond on-premise environments and into remotely-provisioned, scalable environments that are hosted in a cloud. In fact, all of the above considerations require an organization to recognize and establish a set of distinct governance processes and decision frameworks to ensure that responsible parties understand Big Data’s nature, implications and management requirements.
Organizationally, the adoption of Big Data changes the approach to performing business analytics. For this reason, a Big Data analytics lifecycle is introduced in this chapter. The lifecycle begins with the establishment of a business case for the Big Data project and ends with ensuring that the analytic results are deployed to the organization to generate maximal value. There are a number of stages in between that organize the steps of identifying, procuring, filtering, extracting, cleansing and aggregating of data. This is all required before the analysis even occurs. The execution of this lifecycle requires new competencies to be developed or hired into the organization.
As demonstrated, there are many things to consider and account for when adopting Big Data. This chapter explains the primary potential issues and considerations.
Big Data frameworks are not turn-key solutions. In order for data analysis and analytics to offer value, enterprises need to have data management and Big Data governance frameworks. Sound processes and sufficient skillsets for those who will be responsible for implementing, customizing, populating and using Big Data solutions are also necessary. Additionally, the quality of the data targeted for processing by Big Data solutions needs to be assessed.
Outdated, invalid, or poorly identified data will result in low-quality input which, regardless of how good the Big Data solution is, will continue to produce low-quality results. The longevity of the Big Data environment also needs to be planned for. A roadmap needs to be defined to ensure that any necessary expansion or augmentation of the environment is planned out to stay in sync with the requirements of the enterprise.