5 things to prepare for before thinking of leveraging Big Data


When Big Data emerged as an important analysis tool a few years back, it was mostly for large enterprises. The complexity of Big Data coupled with its high cost made it out of reach for small businesses then.

Today, the picture is a lot different because Big Data has evolved to suit the needs of different sized organizations, and advancements have made it cost-effective as well. As a result, startup companies can also now tap into the power of Big Data to enhance their productivity and efficiency.

While this is good news for startups, it does not necessarily mean they should dive into it right away, because without proper guidance and without a clear sight of end-goals, it is easy to lose your way in the world of Big Data. Below are five tips that will help startups leverage the power of Big Data successfully.

Do you need Big Data at all?

This is the first and the most important question you should ask yourself. If your existing system is working fine, then you do not need anything new. You can scale up with Big Data when there is a need. Using technology simply because it is available can be detrimental to your organization. So, think wisely, understand what Big Data is and see if your organization needs it at all.

Knowledge is the key

Big Data is evolving at a rapid pace, so what was in vogue nine months back is outdated now. At such a feverish pace, it is important to stay at least one step ahead of Big Data evolution. The best way to do that is to hire people who are experts in Big Data technology. These experts come with different designations such as data scientists, data miners and chief data officers. What these people do is come up with the right Big Data technology that is best suited for your company, and they stay on top of advancements to ensure that your systems are not obsolete, even before they are implemented. However, these experts come at a price because Big Data is the happening technology today. It is important for startups to see this expense as an investment that will give them an edge over their competitors.

Bite only what you can chew

One common mistake made by many startups is they tend to bite way more than what they can chew. This puts them in a difficult situation because their resources get locked and they are unable to move forward. A smarter way is to start small and to choose the right opportunities as they come along. This strategy will allow startups to scale quickly and decisively – traits needed for success in today's competitive world. This will also help you get a better understanding on where Big Data fits in your company.

Big Data is hard, but is here to stay

You will be disappointed if you expect Big Data to give you more revenues in the short run because Big Data is hard. A study by Gartner shows that more than 73 per cent of respondents have invested or have plans to invest in Big Data over the next two years, but only 13 per cent have Big Data projects in production. Several complexities add to deploying Big Data products, so it is vital to be patient. Your rewards will surely pay off in the long run, but don't expect results within a few years.

Consider the three Vs and the C

Give equal importance to all the three Vs of Big Data, namely, volume, velocity and variety. It also helps to understand the nature and size of Big Data systems your organization needs. You need Big Data when your volume of data is huge, when you are using multiple file formats for processing, and when you are getting in data at a faster speed. The C stands for complexity, so you also need Big Data for doing complex tasks with your existing data. These four aspects will guide you to choose the best Big Data system that will match the needs of your organisation.

About the author:

Srikant Sastri is a serial entrepreneur and the cofounder of Crayon Data, A Big Data startup based in Singapore. Prior to starting Crayon Data, Srikant co founded Solutions Digitas, A CRM and Digital agency in 1995. The company was acquired by Publicis Groupe.


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