Your team will select a big data analytics project that is introduced to an organization of your choice, in the Cyber Security industry. Please address the following items:
(a) Summarize Big data concepts that are relevant to this paper (10 – 12 lines)
(b) Provide a background of the company chosen (need to be descriptive)
(c) Determine the problems or opportunities that that this project will solve. What is the value of the project? Why is this project important to the company?
(d) Describe the impact of the problem. In other words, is the organization suffering financial losses? Are there opportunities that are not exploited?
(e) Provide a clear description regarding the metrics your team will use to measure performance of the analytics project. Please include a discussion pertaining to the key performance indicators (KPIs).
(f) Recommend a big data tool that will help you solve your problem or exploit the opportunity, such as Hadoop, Cloudera, MongoDB, or Hive. Justify the tool.
(g) Evaluate the data requirements. Here are questions to consider: What type of data is needed? Where can you find the data? How can the data be collected? How can you verify the integrity of the data? How will you reduce noise in your data?
(h) Discuss the gaps that you will need to bridge. Will you need help from vendors to do this work? Is it necessary to secure the services of other subject matter experts (SMEs)?
(i) What type of project management approach will you use this initiative? Agile? Waterfall? Hybrid? Please provide a justification for the selected approach and argue it’s suitability to a Big data implementation.
(j) Provide an introduction, summary and conclusion.
(k) Your written paper must have at least 8 to 10 reputable sources and 10-to-15-pages.
(l) Please write the paper in APA Style; please make it very structured.
(m) Use Grammarly to correct Grammatical errors.
· Friday (02/12) – Outline: Provide an outline of the work to be performed. You can submit in MS Word or PPT. Please make sure to include company name, background and big data concepts.