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7 Big Data Mistakes

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  • Last Updated : 17 Aug, 2021

Prerequisite : Big data

Introduction :
Time is passing, technology is improving, new tools are coming with that usage of data is increasing and side by side amount of data is also increasing. From only data now we saw big data. Also we trained ourselves to deal with this big data. So many technology and tools came to the market and trained people started working on it.

  • With the influence of technology, gathering customers’ data is way easier for companies than it has ever been. By spending some time and money, they can obtain an enormous amount of data about their customers digitally. And those data can be the Game-Changers for companies if they know how to leverage them!  
  • Yet the challenging part is how they understand such massive amounts of data with expensive and complex tools. After understanding and discovering the patterns in big data, companies can increase their productivity, efficiency, cut down costs,  make better business decisions and can have a lot more benefits.  
    Now, Big data projects can be massive assets or major failures to the organization depending on which kind of data is collected and how they are handled.
  • Just like that; with the right tools and the right expertise, companies can benefit a lot from big data while avoiding some mistakes such as focusing only on technology instead of business requirements. Let’s understand some of the major big data mistakes and how they can be mitigated.  

Common mistakes people do in Big data :

1. Starting Big in the Beginning – 
Gathering all the data that can be possibly collected will not ensure the yielding of potential benefits to the organization. And collecting big data that are mostly irrelevant to the expected results will eventually be hard to organize and give no useful insights for the organization. That is why it is important to collect sufficient and strategic data that will render requisite results. Machine learning experts with hands-on experience can assist the organization to find such data.  
Note – Quality of data matters more than quantity.  

2. Not Leveraging the Data to Level Up –
Companies have realized that their customer’s data are highly valuable, which can be used to improve their business strategy to achieve their objectives.  
Yet many businesses, especially small businesses are not leveraging the power of data collected from customers to discover essential insights that can potentially benefit them. So those businesses must make use of this opportunity by overcoming small obstacles such as adapting change and new practices for their progression.  

3. Not Conducting Goal-Oriented Analysis of Data –
The goal of conducting the big data implementation should be clear at the beginning itself.  
If the organization is not sure and specific about the objective of the analysis, then the project will end up being unsuccessful. Once it lists down the objectives, it will get directions to discover relative trends in big data.  

4. Underrating Data Visualization –
Data visualization is as crucial as collecting large chunks of data and unveiling the patterns in them. Underrating visualization is one of the major mistakes of big data.  
Why is data visualization so crucial? Because data visualization is the process of representing the data and the derived pattern in a convenient and catchy manner for easy understanding. Such as charts, infographics, graphs, etc… It is also essential for making data-driven decisions.  

5. Focusing on Short-term Benefits –
Data collected from customers are highly crucial as mentioned earlier. It can tremendously benefit the organization with the help of technology to simplify data collection, auto-scaling for handling dynamic data volumes and enabling operations like AI, all while allowing some room for personalization.  

Yet many organizations focus on the short-term benefits of the tools instead of the long-term benefits they can have.  

6. Insufficient Data Security –
Data security and administration are undoubtedly essential aspects of organizations. That’s why they must have a profound understanding of the data, audit the data manipulation periodically and provide access to the privileged users. So, no company should ignore the importance of data security.  

7. Dormant Data Silo –
Companies are gathering and storing their customers’ data successfully, but most of them fail to leverage the data through pattern discovery. Just having all their data in a silo will not be beneficial unless the companies use them to improve their functional performances to achieve their goals efficiently.  

Finally, Big data can be companies’ catalysts for future enhancement if they carefully dodge the mistakes and extract essential trends from the data professionally. Well, that’s all about big data mistakes.

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