Big Data explosion is on the go and is said to continue with same vigor for the next few years. And in order to tap the current explosion trend, with a healthy business perspective, many companies, both large and small, are attempting to find ways to leverage value from their silos of unstructured data. This is being done through applied analytics techniques such as Natural Language Processing and Sentiment Analysis.
However, due to very nature of big data, the science of extracting insights from repositories is giving only vague results. This is because, unlike structured data, where we know exactly what info we have and in what format, big data is like a pool with unfathomable depths. Thus, the only way to find out the depth is to jump and find it out for ourselves.
For this reason, many companies are deterred from investing extracting insights through analyzing big data. But there is a plenty of proof to support the effectiveness of these data mining techniques. This enthusiasm is encouraging many companies take up big data analytics projects. Some are starting with their own company generated data, while some are trying their hand in helping others.
Conversely, when starting a big data project time is very important issue, as sometimes the project can take a few weeks or sometimes many years, and depends on factors such as understanding the requirements, choosing the right technology, the complexity of the analytics and many more.
Here are some interesting tips to note while starting a big data analytics project-
- Problem detection- First track down what problems you are trying to solve but big data analytics. Identify what issues your organization is facing and try to find solutions for them.
- Impact of problems- Try to understand how these issues will show an impact on your business. For example, do they cause loss in millions, or just wasting time of your staff? This will help you create use cases for the problems
- Success Criteria – determine the metrics that will be used to measure the success in the process.
- Value & Impact- It is better to estimate the impact of this problem’s solution to the organization. This will help 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.
- Cloud or on-premise- choose where the solution should reside. That is, whether it should breathe on a cloud, on-premise, or hybrid solution.
- Data Requirements- Evaluate your data requirement, by answering questions such as what type of data you need for analysis; where will you find this data; are you going for your own enterprise data; or looking to help a company with big data analytics; what is the throughput requirement for your data and so on…
- Track down the gaps- It is better to learn early on whether you will need help from vendors, or will need staff in the project in the initial or final stages; or will need more expertise to solve the problem. Also check if you need more hardware and software and make your plans accordingly, because all these factors can add can add extra financial burden on your project.
- Wise to start with a pilot project- Start with a pilot implementation. Set goals and milestones and break them up into manageable chunks. Once the pilot is up and running and when you start seeing value from it, roll it out into production and enterprise-wide use.
Technology is evolving rapidly, and Big Data analytics is fast becoming one of the most viable ways for companies to gain the business insights required to start operating in a more consumer aligned way. So, start following the trend and reap the benefits.
Still confused on where to start from
Well, Dynamic Network Factory can help you out in big data analytics. It can help you get the expertise in this field, who will help evaluate your Big Data Analytics project needs on financial and resource front. Based on your business objective of your project will provide you the necessary resources like IT Infrastructure related expertise required in this project.