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What is Important in Data Transformation?

Data has become one of the critical components of any business in the modern era. It is for this reason that you keep hearing some conversations around big data and big data analytics. With this, data transformation has also gained fame. It is the process where data is analyzed, reviewed and converted by data scientists from a given format to another. This process is essential for organizations, especially at a time when data integration is required for an effective running of operations and security. It might involve the conversion of large amounts of data and data types, elimination of duplicate data, enriching data and aggregating it. Here is how data is transformed:


Have You Solved All These Big Data Issues?

Big data is a unique technology both in terms of its size and scale. With the advancement of machine learning and data science, big has a potential of impacting live and operations in different industries, in unprecedented levels and scope. Over the past decade, the amount of data generated by different industries has increased substantially. While more data can create an opportunity for businesses, it can also mean more problems if not well used. Here are some of the big data problems that must be solved to get the most:


Big Data is Critical to the Transportation Industry

Big data is expected to be a force to reckon with in the future in the transport and logistics sector. But the benefits it promises to offer are already being felt. Before going deeper into potential uses of this technology in the future, one has to know areas in which it is being leveraged now. As the demand for transportation and freight services rise, companies are forced to embrace new opportunities, including those provided by big data.


Production Improvement Enabled by Big Data

In the modern data-driven economy, competing successfully and making sound investment decisions is a competitive endeavor that is also costly. It is for this reason that you are likely to hear discussions in professional business circles about big data and big data analytics. In the manufacturing sector, manufacturers look for ways that drive production, enhance efficiency, and increase innovation. This is where big data becomes highly useful. Big data allows companies to achieve productivity, gain efficiency in operations, and uncover new insights that drive innovation. With big data analytics, manufacturing companies can get information and patterns that will enable them to improve their processes, efficiency of supply, and identify aspects that affect production.


New Way to Share Data

According to Tech Crunch, InfoSum has raised $15 million in funding to bring its new way to share data to market.

Data protection and data privacy have gone from niche concerns to mainstream issues in the last several years, thanks to new regulations and a cascade of costly breaches that have laid bare the problems that arise when information and data security are treated haphazardly.. 


Changing Social Science Through the Use of Big Data

As accurate as it is, talking about how vast amounts of data can help solve most of the world’s pressing issues has become a cliché. This, however, does not mean that the importance of big data in solving these problems should be overlooked. Big data is helping in solving many problems more efficiently than before. In many industries, data is being leveraged alongside the advances in data science and the tremendous growth in computing power to solve specific problems fast and effectively. In industries where people have been struggling to make meaning out of vast amounts of text data, for example, machine learning can be used to identify patterns hidden in this data that would have otherwise have gone unnoticed.


Protect Your Organization's Data Against Cyber Threats

Cyber attacks are imminent. According to a ThreatPost article, “Most organizations have experienced some type of attack within the course of a year, with only 7 percent of respondents claiming not to have experienced an attack at all.” An organization must change their rationale from “what will we do if we get attacked” to “what will be do when we get attacked.”  Having a solid plan will decrease the time it takes to fend off the attack and save the company thousands of dollars.


Search More than 50,000 Resources on COVID-19

As the world grapples to understand what COVID-19 is and finding vaccines that will work, researchers have already created more than 50,000 articles that have information about the coronavirus. That is a massive amount of information that can be too overwhelming to make sense from. Some companies are now coming up with ways of enabling researchers to search through the web and process information fast for use in research.


Antidotes and Cures May Be Hidden in Massive Data Stores

As COVID-19 continues ravaging all the continents of the world, the technology industry has been working around the clock to find a way to help combat the deadly virus. However, it is becoming challenging for the sector that has a limited reach to single-handedly tackle such a massive outbreak more so when it comes to trying to find treatments. Regardless of this, companies that have massive computing resources have been supporting different initiatives offering their resources to effortsaft that help in the fight. The sector is awash with hopes in the ability of AI to help the world back to its feet after being turned upside down by COVID-19. The AI-Aided drug discovery may be a magic bullet as the world tries whatever is possible to come up with the right vaccine and possibly a cure to avert more deaths from the current pandemic.


This Is Why Data Scientists Are Needed For Big Data

As artificial intelligence (AI) continues becoming an area of interest for many enterprises, more and more companies are now coming to terms with the importance of big data and having the right skills and people in place to fulfill their goals. Specifically, there has been a significant surge in demand for data scientists in many companies as AI, as well as its application in machine learning (ML) and predictive analytics, among others continue gaining momentum in organizations.


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