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Data in the Air


You may have heard the quote often saying that data is the oil of the new era. In the early 2000s, the world started to realize the value of data. Data generation increased tremendously at the start of the first decade of the millennium. As the chart below shows, the volume of data and information created, captured, copied, and consumed worldwide from 2010 to 2020 increased from 2 zettabytes to 64 zettabytes! Just five years after 2020, it is expected to be multiplied by 3. That is quite impressive.


We can see that this volume will continue to grow. Opposite to the oil, data is not diminishing. The question is, how can we benefit from this data?


Data for an organization is anything useful, such as documents, emails, audio files, video files, image files, user comments, or even ideas in users’ minds. There are many dimensions to discuss about data: privacy, security, ethics, quality of the data, etc. We can make a very long list. However, in this blog article, I would like to discuss the use of the cloud to better understand the data for organizations.


In fact, using the cloud to work on your data would not be a choice anymore; it would be an obligation. Organizations cannot keep investing in storage hardware and trying to make efficient use of this hardware. In addition, this will be a limitation for the organizations. On the other hand, the cloud offers you economies of scale, automation, rapid elasticity, and access to data. For an organization, the data is valuable only if we can analyze it, create applicable models that help the business, automate the data to enhance the operation and give accurate insights about our people, customers, and industry so the organization can be more agile, competitive, and efficient.


Data Warehouse Solutions

Data warehouse solutions like Google Cloud’s BigQuery enable rapid analysis of large and multi-dimensional datasets. That will be your central hub for all business data. Different data types will be consolidated here, and BigQuery will work for you to create an instrumental analysis for your business in seconds. BigQuery will do this for you serverless, and wait, what does it mean serverless?


Serverless

Serverless means all needed resources, such as computing power, will be automatically provisioned behind the scenes to run your queries. BigQuery is a fully managed data warehouse with downtime-free upgrades, maintenance, and seamless scaling. BigQuery allows you to analyze petabytes of data using breakneck speeds and zero operational overhead. As a result, the organization will not pay anything for computing power unless they are actually running a query. There is no need to think about storage capacity, compute power, etc., so there is no limitation, and it is cost-effective because you pay only for what you use.


You can add to your analysis accurate reporting and have great visibility with robust business intelligence tools like Looker, a data platform that sits on top of any analytics database and makes it simple to describe your data and define business metrics. In that way, you will be able to deliver more insights to more users, and the impact will be an improvement in productivity, decision-making, and innovation.


I want to show an example dashboard below:



Integrated AI and ML tools

Finally, you can benefit from the AI and ML tools to even have a much more significant impact on your business. Very briefly, AI (Artificial Intelligence) is a broad field or term that describes any kind of machine capable of a task that usually requires human intelligence, such as visual perception, speech recognition, decision-making, or translation between languages. We can briefly explain ML (Machine Learning) as a branch within the field of AI. Computers that can “learn” from data and make predictions or decisions without being explicitly programmed to do so, using algorithms or models to analyze data. These algorithms use historical data as input to predict new output values.


As a result, using cloud technologies on your data will give you tools, space, and time to better understand your customer, industry, and competition, so you can focus on your business, innovate, optimize your operation, and create more value.

⭐⭐⭐


If you want to unlock your data’s potential and drive innovation taking comfort in Kartaca’s 10+ years of experience, please check our page and contact us.


Kartaca is a Premier Partner for Google Cloud in the Sell and Service Engagement Models with “Cloud Migration” and “Data Analytics” specializations.




TL;DR

What are the statistics for cloud computing in 2023?

In the early 2000s, the world started to realize the value of data. Data generation increased tremendously at the start of the first decade of the millennium. The volume of data and information created, captured, copied, and consumed worldwide from 2010 to 2020 increased from 2 zettabytes to 64 zettabytes! Just five years after 2020, it is expected to be multiplied by 3.

Why cloud computing is important for companies?

The cloud offers you economies of scale, automation, rapid elasticity, and access to data. For an organization, the data is valuable only if we can analyze it, create applicable models that help the business, automate the data to enhance the operation and give accurate insights about our people, customers, and industry so the organization can be more agile, competitive, and efficient.
As a result, using cloud technologies on your data will give you tools, space, and time to better understand your customer, industry, and competition, so you can focus on your business, innovate, optimize your operation, and create more value.

What is artificial intelligence and machine learning? Why should you leverage integrated AI and machine learning tools?

You can benefit from the AI and ML tools to even have a much more significant impact on your business. Very briefly, AI (Artificial Intelligence) is a broad field or term that describes any kind of machine capable of a task that usually requires human intelligence, such as visual perception, speech recognition, decision-making, or translation between languages. We can briefly explain ML (Machine Learning) as a branch within the field of AI. Computers that can “learn” from data and make predictions or decisions without being explicitly programmed to do so, using algorithms or models to analyze data. These algorithms use historical data as input to predict new output values.


Author: Abdullah Safi

Published on: 25.04.2023