foreAction data analytics: Optimization of SQL and NoSQL hybrid architectures

总部位于渥太华的一家软件开发和技术咨询公司为政治和非营利空间开发了一种软件产品，用于收集、存储和分析联系人数据。根据数据中存储的信息，该软件用于“微观目标”接触特定的营销和信息材料。该软件正在开发中，以便能够沿沿着三个特定轴创建联系人列表：客户对信息作出响应的可能性（倾向分数）、联系人的价值/优先级（价值分数）和所需的联系方法（中等分数）。这三个轴的分析将确定在任何性质的活动期间是否必须与联系人启动特定接洽。通过与联系人的持续互动、通过社交媒体民意调查、通过第三方统计数据（人口统计、民意调查）、通过调查/请愿、在活动等不断收集数据。 分析，以及时有效的方式创建有价值的、有针对性的联系人列表和商业智能仪表板（关键性能指标），以满足真实的时间访问信息更新的需求。特别是在政治领域，在选举前的日子里，新数据的数量可能很大，需要进行解析并纳入分析，作为选民外联战略的一部分。拟议的研究着眼于优化技术，算法（特别是）和架构，这些技术，算法和架构将允许真实的时间更新数据，并仍然对数据进行大规模分析。将为咨询公司实施研究期间开发的解决方案。

A software development and technology consulting firm based in Ottawa has developed a software product for the political and non-profit space that collects, stores and analyzes contact data. Based on information stored within the data, the software is used to "micro-target" contacts for specific marketing and informational materials. The software is being developed to allow for the creation of contact lists along three specific axis: the likelihood of a customer to be responsive to a message (propensity score), the value/priority of the contact (value score), and the desired method of contact (medium score). The analytics of these three axis will determine whether a specific engagement must be initiated with the contact during a campaign of any nature. Data is collected continually via ongoing interactions with contacts, via social media polling, via third party statistics (demographics, polling), via surveys/petitions, at events, etc. There is a pressing need to balance the analytics to create valuable, targeted contact lists and a Business Intelligence dashboard (Key Performance Indicators) in a timely and effective way against the need for real time access to updates to the information. In the political space in particular, in the days leading up to an election, the volume of new data can be significant, and needs to be parsed and included in analytics as a part of the outreach strategy for voters. The proposed research looks into optimization techniques, algorithms (especially) and architectures that will allow to update data in real time, and still perform large-scale analytics on the data. An implementation of the solution developed during the research will be done for the consulting firm.