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Table of contents
1.
Introduction
2.
Do involve all business sections in a big data initiative.
3.
Do evaluate all infrastructure models for big data implementation.
4.
Do consider traditional data sources in big data planning.
5.
Do consider a consistent set of data.
6.
Do distribute the data.
7.
Don't Rely on a Single Approach to Big Data Analytics.
8.
Don't start a large big data initiative before you are ready.
9.
Don't use data in isolation.
10.
Don't ignore data security.
11.
Don't ignore the performance part of big data analytics.
12.
Frequently Asked Questions
13.
Conclusion
Last Updated: Mar 27, 2024
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Ten Big Data Do’s and Don’ts

Author Mayank Goyal
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Master Python: Predicting weather forecasts
Speaker
Ashwin Goyal
Product Manager @

Introduction

Big data has a lot of potential in a variety of businesses. Big data can have a tremendous impact on decision-making and analytics, provided correctly and efficiently. However, the value of big data can only be realized if it is managed systematically. Most businesses are doing pilots to determine if they can use big data sources to transform decision-making. It's too simple to make blunders that can wreak havoc on your business strategy. As we embark on our road to big data, we will acquire some suggestions in this post about what we should do and what we should avoid.

Do involve all business sections in a big data initiative.

A big data venture is not a stand-alone project; it requires the participation of all business units to achieve meaningful value and insight. Big data may assist businesses in leveraging vast amounts of data to obtain insight into customer behavior, events, trends, and predictions, among other things. A data snapshot, which only captures a portion of the total volume of data handled in big data, cannot do this. As a result, businesses focus more on all forms of data coming from all potential avenues/business units to understand the correct pattern.

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Do evaluate all infrastructure models for big data implementation.

Any big data endeavor must deal with a large amount of data and its administration. Because big data deal with petabytes of data, data centers are the only way to manage it. At the same time, the cost factor must be examined before picking and finalizing any storage facility. Cloud services are frequently the best option. However, different cloud environments' offerings must be compared to determine which is best. Because storage is one of the most critical components of any big data implementation, it is a consideration that should be carefully considered in any big data project.

Do consider traditional data sources in big data planning.

There are many different big data sources, and the number of sources is growing every day. This massive amount of data is fed into big data processing. As a result, some businesses believe traditional data sources are useless. This is not the case, as conventional data is an important part of every big data story's success. Because conventional data offers useful information, it should be combined with other big data sources. Only when all data sources (traditional and non-traditional) are included can the true value of big data be determined.

Do consider a consistent set of data.

When you finish analyzing a large data set, you'll likely find that the data fit into a pattern. This set of facts can now lead your company to investigate a new problem thoroughly. Remember that this information could have come through uncleansed customer service sites or social media platforms. As a result, before you put your faith in the data, be sure you're working with a consistent set of metadata so you can integrate it into your organization and analyze it alongside the data from your record systems.

Do distribute the data.

When dealing with large amounts of data, don't expect you'll be able to manage everything on a single server. Learn how to use Hadoop and other distributed computing approaches to successfully manage your data's size, diversity, and speed.

Don't Rely on a Single Approach to Big Data Analytics.

For processing large data, various technologies are available on the market. Apache Hadoop and MapReduce are the cornerstones of all big data technology. As a result, it's critical to assess the appropriate technology for the job. Predictive analytics, prescriptive analytics, text analytics, stream data analytics, and other significant analytics methodologies are only a few examples. It is critical to choose the right method/approach to obtain the intended result. It's best to avoid relying on a single strategy and instead study various options before deciding on the best fit for your problem.

Don't start a large big data initiative before you are ready.

You're right to be enthusiastic about big data's possibilities for your business. Big data can differentiate between being first to market in a hot new market or being left behind. However, before you sprint, take a walk. It would help if you began with pilot projects to gather experience. It would help if you collaborated with professionals that could prevent you from making costly mistakes due to your lack of knowledge.

Don't use data in isolation.

Big data sources are all around us, and they're getting bigger every day. Combining all of this data to provide accurate analytics results is critical. Different data integration tools are available on the market, but they should be thoroughly analyzed before being used. Integrating big data is a difficult operation because the data from various sources are in different formats, but it is essential for good analytics results.

Don't ignore data security.

In big data planning, data security is a crucial factor. Security is not strictly implemented because the data is initially in petabytes (before any processing). However, after some processing, you'll have a subset of data that can be used to gain insight. Data security becomes critical at this point. Data becomes more valuable to an organization when processed and fine-tuned. This finely calibrated output data is confidential and must be protected. Data security must be implemented as part of the big data life cycle.

Don't ignore the performance part of big data analytics.

The outcome of big data analytics is only useful if it performs well. Big data provides additional insights by processing a large volume of data faster. As a result, it must be managed successfully and efficiently. If large data performance is not adequately handled, it will cause problems and render the entire effort useless.

Frequently Asked Questions

1. What is the purpose of big data?

Big data is a combination of technologies for storing, analyzing, and managing large amounts of data, as well as a macro-tool for seeing patterns in the chaos of this information explosion in order to construct smart solutions. It is now employed in a wide range of fields, including medical, agriculture, gaming, and environmental protection.

2. What are the five categories of big data?

Big data is a collection of data from a range of sources that is usually described in terms of five characteristics: volume, value, diversity, velocity, and veracity.

3. What are the five most important big data use cases?

Five Use Cases for Big Data are:

  • For the purpose of analyzing customer sentiment.
  •  For the purpose of Behavioural Analytics.
  •  For the purpose of customer segmentation.
  •  In order to provide Predictive Support.
  •  For the purpose of detecting fraud.

Conclusion

Let us brief out the article. The focus of our article has been on the dos and don'ts of big data initiatives. Big data is a new field, and many businesses are still in the planning stages of deployment. To reduce risk and errors, it's critical to understand big data best practices. The discussion points were gathered from real-world project experiences, and they will provide some guidance for implementing a successful big data strategy. That’s all from the article. I hope you all like it.

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