5 Tips for Big Data Startups to Achieve Massive Growth in 2021

These tips for big data startups will help them understand their clients’ needs better

Big data startups

Big data startups

Big data has evolved to be the core of every strategic decision today. It plays an important role in organizations of all sizes, starting from small to medium and big, and helps them understand the target audience and customer’s preferences better. However, at the core of all this, big data need to be in perfect form to analyze and reap out insightful business decisions. Unfortunately, that is where many organizations fall behind. With an overwhelming amount of data, they are having a hard time figuring out what can be used and what should be ignored. As a result, big data startups emerge from within. They get their hands on organizational data and help companies gain valuable insights that can lead to an advantage over their competitors. They use disruptive technologies like data analytics, data science, automation, data management, etc to acquire actionable insight from unclean data. Some big data startups go a step further to evaluate competitors’ public data so the organizations can get an outlook on what they are doing and what can be done in the counteracting to continue the growth. Running a big data startup is pretty hectic as its deals with technologies and numbers a lot. There are a few remarkable tips for big data startups that could help them understand the needs better. IndustryWired has listed the top five tips for big data startups to board on the success train. 


Using Right Data at the Right Time

Many big data companies have a huge amount of data in their hands all the time. But they don’t find the right data or the right time effectively. More than turning big data into a big benefit, big data startups must figure out their clients’ needs and the perfect time to deliver them. Many big data companies face this issue often. They might have large datasets in their hands but won’t deliver the expected result at the right time. So if you are in a big data startup, ensure that the time you get the analysis is also profitable to your clients. 


Hire the Right Big Data Professional

Although there is an overwhelming number of big data professionals out there, not all of them have similar potential. The company’s needs might completely vary from what a big data professional realizes. Therefore, hiring the right big data talent is very important while building a company. Most big data players are carefully taking a dip in applicants’ academia and previous experience while recruiting professionals. Startups can also do the same if they feel it will help their company in any way. 


Keep the Main Focus on Business Needs

The big data space is fast evolving. Every day, we see some new technology or tool making its debut in the big data sphere. Many big data companies are busily engaged in learning the new development to deliver better results for their clients. Although their motive is right, their actions might make them end up messing with the core need of their clients. Therefore, big data companies should always keep their focus on the business needs that organizations are expecting. If they can process the data and acquire accurate results with an old tool, then they should do it instead of spending more time learning about the new technologies.


Rely on Cloud Storage

As a startup, these small organizations might have a hard time figuring out where they could store the data safely. Even when data centers are a good option, they are expensive. Therefore, rather than storing the data at the right spot, companies should first learn to store them at a place where they can immediately have access to. Yes, cloud storage is the best option for data storage. A plethora of cloud platforms offers amazing services at a very cheap price. Big data startups can opt for that. 


Double Check Your Big Data Tools

Constantly keeping an eye on big data tools and running tests on them is very important for startups. Big data startups should evaluate the performance of the programming language they use, ELTs (extract, transform, and load), and KPIs (Key Performance Indicators). 


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