Capture and integration of different types of data in the Cloud

Cloud storage is a cloud computing model that allows data and files to be stored on the Internet through a cloud computing provider, which is accessed through the public Internet or a dedicated private network connection.

There are three places where you can store, edit, and share files: OneDrive (personal), OneDrive for Business, and Microsoft 365 for group. But what is the best option?

There are three types of clouds for storage: public, private, and hybrid.

There are also three ways to shape this storage: block, file, or object.

Public cloud storage

Disk offers 15 GB and 10 GB respectively for free). This type of storage is designed to be able to work with massive amounts of users, allowing data isolation, access and security for each user.

This strategy facilitates mechanisms and processes to obtain data, store and analyze it so that it can be transformed into information and help in decision-making, increasing competitiveness.

Effective BI helps businesses and organizations ask and answer questions about data. Additionally, business intelligence enables organizations to make better decisions by providing current and historical data within their business context.

Big data, the Internet of Things (IoT), Software as a Service (SaaS), cloud activity, and many more tools are driving a boom in the amount of both data sources and data in the world.

Most of this data has already been collected and stored in sandbox environments or separate data warehouses.

Data integration is the process that brings that data together to generate greater value from data and insights.

Data integration is very important if your company wants to apply digital transformation strategies, since the ability to improve operations, increase customer satisfaction and compete in a world that is increasingly digital will depend on the statistics generated from of all your data.

Google Cloud's data integration solution is Cloud Data Fusion, a fully managed cloud-native data integration service that helps users effectively build and manage ETL/ELT data pipelines.

What are the data integration challenges?
The difficulty of using data integration platforms

Experienced data professionals are difficult and expensive to find, but are generally required to implement most data integration platforms. Business analysts who need access to data to make decisions often rely on these experts, which slows the time-to-value of data insights.

High capital and operating expenses of data integration infrastructure

Capital and operating expenses increase when you procure, deploy, maintain, and manage the infrastructure necessary for an enterprise-grade data integration initiative. Cloud-based data integration as a managed service can address this cost issue directly.

Data tightly linked with applications

Previously, data was so tied to and dependent on specific applications that it couldn't be retrieved and used elsewhere in your business. Today, you can see how the application and data layers are decoupled, so you can use your data more flexibly.

Although natural language processing is not a new science, the technology is advancing rapidly thanks to increased interest in human-machine communication, as well as the availability of big data, powerful computers, and improved algorithms.

As a human, you can speak and write in English, Spanish, or Chinese. But the native language of a computer – known as code or machine language – is largely incomprehensible to most people.

At the lowest levels of your device, communication takes place not with words but through millions of zeroes and ones that produce logical actions.

Reducing customer complaints with NLP

Royal Bank of Scotland uses text analytics, NLP technique, to extract important trends from customer feedback in many forms.

The company analyzes data from emails, surveys, and conversations with customer service centers to identify the root cause of customer dissatisfaction and implement improvements.

Why is natural language processing important?
Large volumes of textual data

Natural language processing helps computers communicate with humans in their language and scales other language-related tasks. For example, NLP makes it possible for computers to read text, listen to spoken voice, interpret it, measure sentiment, and determine which parts are important.