Initiating a big data analytics project can vary in time from as low as a couple of week to a multi-year effort. It depends upon several factors: your understanding of your needs, your perceived information technology (IT) obstacles or, your accessibility to the data you need, the intricacy of the analytics, as well as several, a lot more. So when every big data company can have different viewpoints on how long it in fact takes to start and implement a solution, it doesn’t mean that one is right and others are wrong. It all depends upon the understanding, skill set, resources and many other factors.
Solutions should be Business Driven
One thing that all vendors and organizations should agree on is that a big data analytics solution should be a business decision, not an IT option. There are various resources providing information on the best ways to start and implement big data solutions, such as: Intel’s quick guide for big data and IBM’s 10 big data implementation best practices, both of which are really informative.
Big Data Analytics Project Process
There are different tasks to take into consideration when initiating a big data analytics project.
Problem: Determine what are the problems you want to solve. Here you need to identify what issues your organization is facing and envision what solutions are available .
Impact: Understand how these problems impact your business and then develop use case(s). Are you losing millions? Is your staff is wasting time by doing even more data entry and less analysis? How is this problem affecting your organization?
Success standards: How will you measure the success? What are the top metrics you should track throughout this process?
Value & Impact: What you need to clearly understand is if this problem is solved, what would it mean for your organization? This is typically one of the most crucial steps as it helps determine the if, how, and when you should move forward with this project. It likewise provides context for identifying the budget for your solution.
If you can not clearly define and write steps 1-4, there is no point to move forward to step 5. Additionally, note that the first 4 actions have little or nothing to do with the modern technology. This is intended as you don’t want to compel innovation to solve your business problems. You are starting with business problem and will certainly map the suitable technology to resolve it.
Cloud or On-Premise: Choose where the solution ought to implement and whether it should be a cloud or hybrid solution.
Data requirements: Assess your data need and understand what data is needed to address this problem. Is it data you currently have? Is it data you need to go out and collect? What is that data and what are the requirements that are important? What is throughput/ performance demands for the data? What are your retention and retrieval requirements?
Identify gaps: Determine if this is something that your organization can accomplish with existing or in-house resources and technology or if you need help from other vendors. Do you have enough staff to address this problem? Are they qualified of solving this problem? Will you require extra hardware or software to address? Determine those gaps and make sure you plan accordingly.
Agile or iterative approach: Start with a pre-production or a pilot implementation. Set goals and milestones and break them up into manageable chunks. Once the project is going and you see value from it, start its production and enterprise-wide use.