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3 Data Science Myths Busted in the Corporate World
Data-Science in the corporate world
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Data science is on the rise and it is soon becoming an integral part of our lives. We’re all living in the age of information and due to high usage of the internet, it has become easier for anyone to collect any kind of data they’re in search of.
Also, every minute,
• Over 4 million hours of content is being uploaded on the internet.
• Internet users generating 2.5 quintillion bytes of data daily.
• 4.1 million videos are viewed daily on YouTube.
• 3.6 million searches delivered by Google.
• 600 new edits are published by Wikipedia users.
If you’ve spent some time in the data science field, then perhaps you’ve come across certain data science myths that need to be busted today. Heck, if you want to get into the corporate world you might have probably heard rumors about data science.
All those rumors you’ve heard about data science are myths that somehow gets suppressed by many.
The data science industry is often misunderstood and misinterpreted. This is because of the presence of several terms that sound similar. Most professionals in the corporate world have often been misguided leading to an abundance of myths. Here’s my top five: -
Myth 1: Data science is not required for my project
Data is analyzed using spreadsheets for a small amount of data. Spreadsheets do not work well for a large amount of data. In-depth knowledge of data visualization tools such as ggplot or Tableau is mandatory. People are often mistaken for the fact that data science is not required for a project.
However, several teams process data and analyze it for a different context. Be it sales, marketing, operations, e-commerce, logistics or engineering everybody needs to work on data one way or another. Everyone needs to analyze their data, extract, clean it for further insights that will improve business decisions.
Without the expertise of skilled professionals in data science, it gets extremely difficult for companies to gain the maximum benefit from the data they’ve collected. Today, big data scientists’ powers business value by using cutting-edge technology turning it into actionable insights. Several organizations are now opening doors for the data science industry to unlock their power.
Modern businesses are awash to data.
For departments that are not purely technology-based, business intelligence and data mining tools are used for data analysis. Data science is not in its nascent stage; thus, it is applied in almost every industry today.
The major focus is not on gathering and analyzing the data but on gaining positive insights
Myth 2: Setting up infrastructure is way too expensive for implementation and maintenance
Local deployments increase the risk of product failure and pose hidden costs. Even worse it won’t be able to facilitate teamwork and neither help in collaboration and data sharing. The major keys that will lead to the success in the deployment of data science projects are collaboration and agility. It is always recommended to work in a team to explore different ways of analyzing data.
To overcome this problem, you can set up a central deployment, where all the technologies stay involved and are open source. However, this may pose higher cost-effectiveness in terms of implementation and maintenance. Backup and security of the data are certain factors that need attention.
A smarter way to implement data science projects is by leveraging on an existing cloud, e.g. SaturnCloud.io. The best part about the service provided by this Cloud is that the deployment of the infrastructure can be made with just a single click. Thus, promoting collaboration from zero minutes allowing it to scale up and down the projects. A cloud service eliminates the need to deal with individual libraries or software updates assuring there are backup and security. If your project makes a hit you can use your private cloud to access this project.
Cloud services have helped the data science industry by making deployment of projects easier, cheaper, and faster.
Myth 3: Though data science is a cutting-edge technology I don’t need it in my industry
Data science can be intimidating for a newbie who has just made his entrance in the corporate world. To be honest, it has just made our lives easier by providing smarter ways to analyze and process data. in terms of flexibility and performance, a spreadsheet won’t be of aid. Data visualization tools such as RapidMiner has greatly improved the performance in data visualization of larger data.
People often confused that data science is limited to experts like big data scientists, however, this is not true. For an average business user, the incorporation of plain statistics to a large data set gaining the flexibility to manipulate data is much more valued than an elaborated AI algorithm.
Data science is a vast field and can add value to any kind of businesses today. From gathering data to insights across workflow and deployment of projects. It is a field where any kind of data can be analyzed to deliver outstanding results.
Also, every minute,
• Over 4 million hours of content is being uploaded on the internet.
• Internet users generating 2.5 quintillion bytes of data daily.
• 4.1 million videos are viewed daily on YouTube.
• 3.6 million searches delivered by Google.
• 600 new edits are published by Wikipedia users.
If you’ve spent some time in the data science field, then perhaps you’ve come across certain data science myths that need to be busted today. Heck, if you want to get into the corporate world you might have probably heard rumors about data science.
All those rumors you’ve heard about data science are myths that somehow gets suppressed by many.
The data science industry is often misunderstood and misinterpreted. This is because of the presence of several terms that sound similar. Most professionals in the corporate world have often been misguided leading to an abundance of myths. Here’s my top five: -
Myth 1: Data science is not required for my project
Data is analyzed using spreadsheets for a small amount of data. Spreadsheets do not work well for a large amount of data. In-depth knowledge of data visualization tools such as ggplot or Tableau is mandatory. People are often mistaken for the fact that data science is not required for a project.
However, several teams process data and analyze it for a different context. Be it sales, marketing, operations, e-commerce, logistics or engineering everybody needs to work on data one way or another. Everyone needs to analyze their data, extract, clean it for further insights that will improve business decisions.
Without the expertise of skilled professionals in data science, it gets extremely difficult for companies to gain the maximum benefit from the data they’ve collected. Today, big data scientists’ powers business value by using cutting-edge technology turning it into actionable insights. Several organizations are now opening doors for the data science industry to unlock their power.
Modern businesses are awash to data.
For departments that are not purely technology-based, business intelligence and data mining tools are used for data analysis. Data science is not in its nascent stage; thus, it is applied in almost every industry today.
The major focus is not on gathering and analyzing the data but on gaining positive insights
Myth 2: Setting up infrastructure is way too expensive for implementation and maintenance
Local deployments increase the risk of product failure and pose hidden costs. Even worse it won’t be able to facilitate teamwork and neither help in collaboration and data sharing. The major keys that will lead to the success in the deployment of data science projects are collaboration and agility. It is always recommended to work in a team to explore different ways of analyzing data.
To overcome this problem, you can set up a central deployment, where all the technologies stay involved and are open source. However, this may pose higher cost-effectiveness in terms of implementation and maintenance. Backup and security of the data are certain factors that need attention.
A smarter way to implement data science projects is by leveraging on an existing cloud, e.g. SaturnCloud.io. The best part about the service provided by this Cloud is that the deployment of the infrastructure can be made with just a single click. Thus, promoting collaboration from zero minutes allowing it to scale up and down the projects. A cloud service eliminates the need to deal with individual libraries or software updates assuring there are backup and security. If your project makes a hit you can use your private cloud to access this project.
Cloud services have helped the data science industry by making deployment of projects easier, cheaper, and faster.
Myth 3: Though data science is a cutting-edge technology I don’t need it in my industry
Data science can be intimidating for a newbie who has just made his entrance in the corporate world. To be honest, it has just made our lives easier by providing smarter ways to analyze and process data. in terms of flexibility and performance, a spreadsheet won’t be of aid. Data visualization tools such as RapidMiner has greatly improved the performance in data visualization of larger data.
People often confused that data science is limited to experts like big data scientists, however, this is not true. For an average business user, the incorporation of plain statistics to a large data set gaining the flexibility to manipulate data is much more valued than an elaborated AI algorithm.
Data science is a vast field and can add value to any kind of businesses today. From gathering data to insights across workflow and deployment of projects. It is a field where any kind of data can be analyzed to deliver outstanding results.
Source: https://www.dasca.org/
