#bigdataprojects

[centizen]

Why Do So Many Big Data Projects Fail?

In our business analytics project work, we have often come in after several big data project failures of one kind or another. There are many reasons for this. They generally are not because of unproven technologies that were used because we have found that many new projects involving well-developed technologies fail. Why is this? Most surveys are quick to blame the scope, changing business requirements, lack of adequate skills etc. Based on our experience to date, we find that there are key attributes leading to successful big data initiatives that need to be carefully considered before you start a project. The understanding of these key attributes, below, will hopefully help you to avoid the most common pitfalls of big data projects.

Key attributes of successful Big Data projects

Develop a common understanding of what big data means for you

There is often a misconception of just what big data is about. Big data refers not just to the data but also the methodologies and technologies used to store and analyze the data. It is not simply “a lot of data”. It’s also not the size that counts but what you do with it. Understanding the definition and total scope of big data for your company is key to avoiding some of the most common errors that could occur.

Choose good use cases

Avoid choosing bad use cases by selecting specific and well defined use cases that solve real business problems and that your team already understand well. For example, a good use case could be that you want to improve the segmentation and targeting of specific marketing offers.

Prioritize what data and analytics you include in your analysis

Make sure that the data you’re collecting is the right data. Launching into a big data initiative with the idea that “We’ll just collect all the data that we can, and work out what to do with it later” often leads to disaster. Start with the data you already understand and flow that source of data into your data lake instead of flowing every possible source of data to the data lake.

Then next layer in one or two additional sources to enrich your analysis of web clickstream data or call centre text. Your cross-functional team can meet quarterly to prioritize and select the right use cases for implementation. Realize that it takes a lot of effort to import, clean and organize each data source.

Include non-data science subject matter experts (SMEs) in your team

Non-data science SMEs are the ones who understand their fields inside and out. They provide a context that allows you to understand what the data is saying. These SMEs are what frequently holds big data projects together. By offering on-the-job data science training to analysts in your organization interested in working in big data science, you will be able to far more efficiently fill project roles internally over hiring externally.

Ensure buy-in at all levels and good communication throughout the project

Big data projects need buy-in at every level, including senior leadership, middle management, nuts and bolts techies who will be carrying out the analytics and the workers themselves whose tasks will be affected by the results of the big data project. Everyone needs to understand what the big data project is doing and why? Not everyone needs to understand the ins and outs of the technical algorithms which may be running across the distributed, unstructured data that is analyzed in real time. But there should always be a logical, common-sense reason for what you are asking each member of the project team to do in the project. Good communication makes this happen.

Trust

All team members, data scientists and SMEs alike, must be able to trust each other. This is all about psychological safety and feeling empowered to contribute.

Summary

Big data initiatives executed well delivers significant and quantifiable business value to companies that take the extra time to plan, implement and roll out. Big data changes the strategy for data-driven businesses by overcoming barriers to analyzing large amounts of data, different types of unstructured and semi-structured data, and data that requires quick turnaround on results.

Being aware of the attributes of success above for big data projects would be a good start to making sure your big data project, whether it is your first or next one, delivers real business value and performance improvements to your organization.

[hariprasadcj-blog]

Big data Projects in Chennai

The projects in big data are more relevant to the engineering students who are in IT, CSE departments can have an idea to do a project in big data which is very complicated but it is helpful in gaining the most  experienced knowledge in big data in the engineering category.

We have provided more big data projects for the students who are interested in making a big data project. That we will give you the clear details about the projects which you selected. 

For any information about Big data Projects in Chennai

Visit us: http://projectcentrechennai.in/2019-2020-ieee-bigdata-and-software-engineering-projects-chennai.php

Admission Office:

Door No. 68 & 70, Ground Floor,

No. 174, Raahat Plaza,

Vadapalani, Chennai.

Contact: 9751800789

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Big Data ramp walks into 2020 with huge prospects ahead.They are inevitable like a sunrise on every morning.  

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In this video by Intellipaat, you learn the various terminology and concepts related to Big Data, Amazon Elastic MapReduce, Hadoop and a live workshop to help you navigate the AWS console.

Big Data processing is the frequent and popular use of cloud services and resources, especially due to the necessity of sheer computing power. AWS (Amazon Web Services) has developed a myriad of services that help in the effective utilization of Big Data.

[reflections17]

Big Data - A short introduction – Job and Career Prospects

The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs.

Volume - Organizations collect data from a variety of sources, including transactions, smart (IoT) devices, industrial equipment, videos, images, audio, social media and more.

Velocity - With the growth in the Internet of Things, data streams into businesses at an unprecedented speed and must be handled in a timely manner.

Variety - Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, emails, videos, audios, stock ticker data and financial transactions.

Big Data consists of Semi-Structured and Unstructured Data which is generated by the following:

Rapid increase of Social Media Platforms like Facebook, YouTube, Twitter, Instagram, LinkedIn, Pinterest etc.

Availability of low cost Smart Mobile phones with Mobile Apps Loaded.

6.7 Billion Smart Phones in the world with Mobile Apps

Deployment of Low-Cost Sensors across the world - which is about 46 Billion Sensors connected by 2021

All the above technologies have contributed to the rapid rise in Big Data Technologies - like Internet of Things (IoT), Data Science, Artificial Intelligence (AI), Machine Learning (ML), Natural language processing (NLP)

Why Is Big Data Important?

Improve operations

Cost Saving

Time Saving

Quicker and Better Decision Making Within Organizations

Improve Customer Experience

Provide better customer service

Create personalized marketing campaigns

Applications of Big Data –

Secure Air Traffic System

Auto Driving Car

E-commerce

Smart Traffic System

Transportation

Healthcare

Financial and banking sector

Education Sector

Media and Entertainment Sector

Reasons Why Big Data Analytics is the Best Career Move –

Huge Job Opportunities & Meeting the Skill Gap

Salary Aspects

Adoption of Big Data Analytics is Growing

The Rise of Unstructured and Semi structured Data Analytics

Analytics: A Key Factor in Decision Making

Numerous Choices in Job Titles and Type of Analytics

Career Prospects and Jobs for Software Engineers in the domain of Big Data:

Global Big Data Analytics Market to Grow 4.5 Times by 2025.

The average salary for Big Data Jobs in India is - ₹ 7,00,000 per year

The average salary for Big Data Jobs in USA is $187,500 per year

Big Data Role & their average salary:

Big Data Engineer - ₹ 10,00,000 - 20,00,000 P.A.

BIG DATA LEAD DEVELOPER - ₹ 16,00,000 - 30,00,000 P.A.

Big Data Developer - Python/Hadoop - ₹ 5,00,000 - 9,00,000 P.A.

Big Data Quality Assurance Engineer - ₹ 8,00,000 - 9,00,000 P.A.

Big Data Engineer / Python/ Scala /Spark /panda /matplotlib - ₹ 11,00,000 - 13,00,000 P.A.

Big Data Engineer - Spark, Scala, AWS - ₹ 10,00,000 - 18,00,000 P.A.

Big Data Engineer - $140,000 - $160,000 P.A

Bigdata Cybersecurity engineer - $180,000 - $190,000 P.A

Big Data Testing Engineer -$180,000 - $190,000 P.A

Big Data Solutions Architect - Data & Analytics

Lead Big Data Developer

What are the skills required for Big Data Engineer:

Analytical Skills

Familiarity with Business Domain and Big Data Tools

Data Visualization Skills

Problem Solving Skills

SQL – Structured Query Language

Familiarity with Programming Skills

Familiarity with Cloud Technologies like – Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP)

Familiarity with data analysis tools, especially SQL, NoSQL, SAS, and Hadoop.

Tags:

#BigData #Career #CareerProspects #BigDataEngineer #SoftwareEngineer #DataScience #Training #ArtificialIntelligence #MachineLearning #AI #ML #NLP #BigDataTrainings #BigDataVendors #BigDataCourses #BigDataDatasets #BigDataGigs #BigDataTechstacks #BigDataProjects #BigDataHackathons #BigDataCareer #BigDataBlogs #Blogs

About the author:

Article Contributed by BigDataLogin Operations Team - with inputs from various global resources. Website: https://www.bigdatalogin.com/

BigDataLogin is a One-Stop Digital Platform for the $200 Billion Big Data and Software industry. Users can connect with the global Big Data and software community.

Users can also find Jobs, Trainings, Datasets, Vendors, 1:1 Online Mentors for fixing Technical issues. You can visit their website at https://www.bigdatalogin.com/

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