Theodora Fragkouli

About Theodora Fragkouli

Theodora has graduated from Computer Engineering and Informatics Department in the University of Patras. She also holds a Master degree in Economics from the National and Technical University of Athens. During her studies she has been involved with a large number of projects ranging from programming and software engineering to telecommunications, hardware design and analysis.

How to start a big data analytics project

When starting a Big Data analytics project, time is a very important issue. It may take from a few weeks to many years, and it depends on many factors, such as understanding the requirements, choosing the right technology, the complexity of the analytics and many more. An important thing to understand is that a big data analytics solution should be a business decision, not an IT decision.

An interesting approach on how to start a big data analytics project is described in 8 proven steps to starting a big data analytics project. So, according to the article, here are 8 important steps to consider when initiating a big data analytics project.

1. Problem. Determine what are the problems that you want to solve. Identify what issues your organization is facing and try to find solutions for them.

2. Impact of the problems. Try to understand what is the impact of these problems to your business. For example, do they cause the loss of millions, or the wasting time of your staff? This will help you create use cases for the problems.

3. Success criteria. Determine the metrics that will be used to measure the success in the process.

4. Value & Impact. Estimate the impact of this problem’s solution to the organization. This will help you understand whether or not you should move on with this project, and also estimate the budget that you can use. Understanding how your specific problem impacts your business is crucial in implementing the right solution.

Steps 1-4 are very important to understand before moving to step 5. They don’t have to do with technology, since first the business problem must be defined before mapping the appropriate technology to solve it.

5. Cloud or On-Premise. Decide where the solution should live and whether it should be a cloud, on premise, or hybrid solution.

6. Data requirements. Evaluate your data requirement. Try to answer the questions below? What is the data you need? Where will you find this data, do you have it or you must collect it? What is the throughput requirement for the data?

7. Identify gaps. Check if you need help from vendors, if you need more staff, or more expert staff to solve the problem. Also check if you need more hardware and software and make your plans accordingly.

8. Agile or iterative approach. Start with a pilot implementation. Set goals and milestones and break them up into manageable chunks. Once the pilot is up and running and you see value from it, roll it out into production and enterprise-wide use.

An important step is also to break down big data analytics into smaller components. The smaller and more focused implementations are easy to manage and allow the clients to see results more quickly. Another benefit is that the risk is reduced, since smaller solutions make it easy to handle problems. Flexibility is also an asset. Requirements may change, needs may change, even the data may change. So, a flexible solution can be much more effective.

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