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Table of contents
1.
Introduction To Challenges
2.
Roadmap
3.
Tips Before Getting Started
4.
Frequently Asked Questions
4.1.
Define Tokenization in the Big Data context.
4.2.
Define Data Anonymization.
4.3.
Define Feature Selection.
4.4.
Define the Filter method in feature selection.
4.5.
How are Big Data and Data Science correlated? 
5.
Conclusion
Last Updated: Mar 27, 2024
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Big Data Implementation Road Map

Author Rajkeshav
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Speaker
Ashwin Goyal
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Introduction To Challenges

Implementation plans for Big Data will vary based on operational objectives, the maturity of the data management environment, and the magnitude of the risks. Begin by planning by considering all of the issues that will allow us to develop a road map for implementation. 

A few things that we need to think about are

  • Urgency in business
  • Estimated capacity
  • Software development technique of choice
  • Budgets and available skill sets
  • Appetite for danger

 

Urgency In Business

Many ambitious organizations appear to be in constant need of the most cutting-edge technologies. In some cases, an organization can show that having access to crucial significant data sources can lead to new initiatives. It makes sense to develop a strategy and plan in these situations. It's a common misconception that Big Data adoption and deployment are well-defined undertakings. So, regardless of the other elements at play, the time required to build big data solutions should be there on any road map. Furthermore, design duties should never be skipped. This reduces the utility of any Big Data endeavor.

Estimated Capacity

Because the entry of big data into the environment is required, we must be able to answer the question "How much data do we require?" as well as "How quickly do we need to analyze it?" The responses will provide context for the road map's design, implementation, and testing phases. 

Software Development Technique of Choice

Most businesses and organizations have IT teams that adhere to predetermined development processes and practices. Some of these development methodologies are ideal for big data implementations, while others are not. Big data projects benefit from an agile and dynamic development process. Iterative approaches gradually deliver a business solution using short time cycles with rapid results and ongoing user participation. As a result, it's no surprise that the most effective development methodology for big data applications is iterative.

Budgets And Available Skill Sets

Budget requirements for a new type of project, such as big data, are often difficult to predict. The ideal approach is to have a comprehensive understanding of Big Data adoption's anticipated costs and benefits before securing a budget for the project. The best method for establishing the optimal strategy to project budgeting is to use an iterative approach. As a result, budgets can be set up in advance and then released as milestones as the project progresses. The best method for establishing the optimal strategy to project budgeting is to use an iterative approach. As a result, budgets can be set up in advance and then released as milestones as the project progresses.

Appetite For Danger

Every company has a culture that dictates how much risk management it is willing to take on. We may be pushed to take more risks on possible market innovation in a highly competitive market than a company whose products are required by customers and where there are fewer competitors.

Roadmap

  • Identify business owners,  define strategy, establish goals,  build a team,  establish or integrate into EDM,  research best practices, and secure findings.
  • Deploy business applications,  Deploy its operations practices,  refine significant data requirements for integrity and volatility,  deploy analytics and visualizations through infrastructure and applications for best performance, and perform after-action assessments
  • Identify Big data sources, identify affected business processes,  create technology and operations requirements,  define desired business outcomes,  begin technology implementation, and iterate with key business stakeholders.

Most operations are the same and can frequently be carried out in parallel, depending on organizational maturity.  

It is simple to get started if some of the people involved have done it previously. We won't find many people who have "been there, done that when new disruptive businesses and technologies like big data." Here are a few things to think about as we explore using big data in our company or organization:

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Tips Before Getting Started

  • Get some assistance. Don't be afraid to hire a few experts as consultants. Check to see whether they know their "stuff" and if they are capable of mentoring others in the business. They must be willing to work their way out of a job.
  • Get some instruction. Take classes, purchase and study books, conduct Internet research, ask questions, and attend a conference or two. Having a stronger foundation can help with all of the subsequent decision-making.
  • Set reasonable expectations. Some people believe that having minimal expectations is the secret to happiness. Properly defined expectations can spell the difference between success and failure in the corporate world. A successful project may be considered a failure if the business benefits are exaggerated if it takes 50% longer to complete.

Frequently Asked Questions

Define Tokenization in the Big Data context.

The Tokenization technique safeguards sensitive data by replacing it with random tokens or alias values that have no meaning to anyone who obtains unauthorised access to it.

Define Data Anonymization.

When data is anonymized, all data that may be uniquely linked to an individual is removed (for example, a person's name, Social Security number, or credit card number).

Define Feature Selection.

Feature selection is obtaining only the necessary features from a given set of Big Data. Big Data may contain many aspects that are not required at any one time during processing.

Define the Filter method in feature selection.

The selection of features in this method is not dependent on the specified classifiers. A variable ranking strategy is used to pick variables for ordering purposes.

How are Big Data and Data Science correlated? 

Data science encompasses a wide range of tasks that involve the study of Big Data, the discovery of patterns and trends in data, the interpretation of statistical terminology, and predicting future trends. Big Data is only one component of Data Science.

Conclusion

In this article, we discussed the issues related to the Big Data roadmapThe actual road map, and some tips before carrying out a career in this field. If you are interested in this field and want to learn more about Python and Machine Learning, upskill with coding ninjas complete programs for Artificial Intelligence and Data Science. Try out frequently asked interview problems on Code studio.

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