BIG DATA MANAGEMENT, DATA VISUALIZATION, DATA ARCHITECTURE
Article | August 18, 2022
Over the past couple of years, a significant rise in the trend of digitalization has been witnessed across almost all industries, resulting in the creation of large volumes of data. In addition, an unprecedented proliferation of applications and the rise in the use of social media, cloud and mobile computing, the Internet of Things, and others have created the need for collecting, combining, and curating massive amounts of data.
As the importance of data continues to grow across businesses, companies aim to collect data from the web, social media, AI-powered devices, and other sources in different formats, making it trickier for them to manage this unstructured data.
Hence, smarter companies are investing in innovative solutions, such as data virtualization, to access and modify data stored across siloed, disparate systems through a unified view. This helps them bridge critical decision-making data together, fuel analytics, and make strategic and well-informed decisions.
Why is Data Virtualization Emerging as A New Frontier in Data Management?
In the current competitive corporate world, where data needs are increasing at the same rate as the volume of data companies hold, it is becoming essential to manage and harness data effectively. As enterprises focus on accumulating multiple types of data, the effort of managing it has outgrown the capacity of traditional data integration tools, such as data warehouse software and Extract Transform Load (ETL) systems.
With the growing need for more effective data integration solutions, high-speed information sharing, and non-stop data transmission, advanced tools such as data virtualization are gaining massive popularity among corporate firms and other IT infrastructures.
Data virtualization empowers organizations to accumulate and integrate data from multiple channels, locations, sources, and formats to create a unified stream of data without any redundancy or overlap, resulting in faster integration speeds and enhanced decision-making.
What are the key features that make data virtualization a new frontier in data management? Let's see:
Modernize Information Infrastructure
With the ability to hide the underlying systems, data virtualization allows companies to replace their old infrastructure with cutting-edge cloud applications without affecting day-to-day business operations.
Enhance Data Protection
Data virtualization enables CxOs to identify and isolate vital source systems from users and applications, which assists organizations in preventing the latter from making unintended changes to the data, as well as allowing them to enforce data governance and security.
Deliver Information Faster and Cheaper
Data replication takes time and costs money; the "zero replication" method used by data virtualization allows businesses to obtain up-to-the-minute information without having to invest in additional storage space, thereby saving on the operation cost.
Increase Business Productivity
By delivering data in real time, the integration of data virtualization empowers businesses to access the most recent data during regular business operations. In addition, it enhances the utilization of servers and storage resources and allows data engineering teams to do more in less time, thereby increasing productivity.
Use Fewer Development Resources
Data virtualization lowers the need for human coding, allowing developers to focus on the faster delivery of information at scale. With its simplified view-based methodology, data virtualization also enables CxOs to reduce development resources by around one-fourth.
Data Virtualization: The Future Ahead
With the growing significance of data across enterprises and increasing data volume, variety, complexity, compliance requirements, and others, every organization is looking for well-governed, consistent, and secure data that is easy to access and use.
As data virtualization unifies and integrates the data from different systems, providing new ways to access, manage, and deliver data without replicating it, more and more organizations are investing in data virtualization software and solutions and driving greater business value from their data.
BIG DATA MANAGEMENT
Article | July 15, 2022
Whilst there are many people that associate AI with sci-fi novels and films, its reputation as an antagonist to fictional dystopic worlds is now becoming a thing of the past, as the technology becomes more and more integrated into our everyday lives.AI technologies have become increasingly more present in our daily lives, not just with Alexa’s in the home, but also throughout businesses everywhere, disrupting a variety of different industries with often tremendous results. The technology has helped to streamline even the most mundane of tasks whilst having a breath-taking impact on a company’s efficiency and productivity.However, AI has not only transformed administrative processes and freed up more time for companies, it has also contributed to some ground-breaking moments in business, being a must-have for many in order to keep up with the competition.
BIG DATA MANAGEMENT
Article | July 11, 2022
The latest pace of advancements in technology paves way for businesses to pay attention to digital strategy in order to drive effective digital transformation. Digital strategy focuses on leveraging technology to enhance business performance, specifying the direction where organizations can create new competitive advantages with it. Despite a lot of buzz around its advancement, digital transformation initiatives in most businesses are still in its infancy.Organizations that have successfully implemented and are effectively navigating their way towards digital transformation have seen that deploying a low-code workflow automation platform makes them more efficient.
Article | February 10, 2020
We are a species invested in predicting the future as if our lives depended on it. Indeed, good predictions of where wolves might lurk were once a matter of survival. Even as civilization made us physically safer, prediction has remained a mainstay of culture, from the haruspices of ancient Rome inspecting animal entrails to business analysts dissecting a wealth of transactions to foretell future sales. With these caveats in mind, I predict that in 2020 (and the decade ahead) we will struggle if we unquestioningly adopt artificial intelligence (AI) in predictive analytics, founded on an unjustified overconfidence in the almost mythical power of AI's mathematical foundations. This is another form of the disease of technochauvinism I discussed in a previous article.