Two of the most important trends that shape the era of digitization and the fourth industrial revolution are data modernization and cloud computing. Data modernization refers to the use of advanced data management tools for handling very large amounts of data from a variety of heterogeneous sources, including both well-structured and unstructured datasets. Specifically, data modernization tools enable organizations to collect and analyze the proliferating volumes of social media and Internet of Things (IoT) datasets, which can be hardly stored and processed within conventional databases and data warehouses. The use of such tools facilitates the development of Big Data and Artificial Intelligence (AI) applications. On the other hand, cloud computing enables access to large amounts of IT resources such as storage and computing cycles, which are also among the prerequisites for the development, deployment and operation of data-driven applications at scale.
These two trends complement and reinforce each other. Data modernization tools run on the cloud in order to take advantage of its scalability, capacity, elasticity and quality of service. Hence, the expanded use of cloud computing boosts data modernization. At the same time data modernization drives the development of cloud computing, given that the execution of data-centric applications like AI hinges on the availability of large amounts of computing resources. It is no accident that some of the most popular cloud applications like Machine Learning as a Service (MLaaS) are directly related to data modernization tools. In this context, it’s not clear whether data modernization drives cloud computing or the other way round. To explore this concept, one has to take a closer look on these two technological trends.
Traditionally, organizations have been focusing on transactional data and reporting applications based on legacy databases. This is no longer the case, as modern enterprises have to deal with unstructured data as well, such as images, voice audio, comments on social networks, content of e-mails, as well as data from sensors and internet connected devices. Conventional databases fall short when it comes to handling large volumes of such unstructured data. That’s where data modernization infrastructures and tools come in. As a first step, they complement legacy transaction databases with other datastores like Big Data databases, Data Lakes and NoSQL databases. The latter offer significant scalability and cost effectiveness advantages over conventional data management infrastructures.
Note however that legacy databases and data warehouses are still present in the enterprise data management infrastructures. This is because such databases are superior when it comes to working with structured data in transactional applications. However, data modernization infrastructures provide the means for developing novel Big Data applications that enable new opportunities for improving business processes and generating new revenue streams.
The cloud is nowadays the enabling infrastructure for data modernization, as it offers a number of compelling features such as:
Based on the above features, the cloud is an essential infrastructure for data modernization. This does not mean that it is not possible to implement data modernization as part of an on-premise data center infrastructures. There are many companies that dispose with their own private cloud for their Big Data applications. In several cases they have good reasons for doing so, such as the need to alleviate trust concerns, as well as the need to comply with privacy and data protection regulations. Nevertheless, the cloud offers important scalability, automation and cost effectiveness advantages that data modernization experts can hardly ignore.
There is no silver bullet about how enterprises should distribute their budget among cloud and data modernization investments. There are however some best practices for successful data modernization in the cloud:
Overall data modernization is a core element of digital transformation, while the cloud is both a means and a consequence for data modernization. The two trends go hand-in-hand and it seems that more data modernization gives rise to increased cloud spending and vice versa. Enterprises had better consider some best practices for planning their investments in cloud and data modernization. Best practices like the above-listed ones can help them in maximizing the value for money for their data modernization projects and their deployment in the cloud.
The different flavours of edge computing infrastructures
Technology Enablers of Manufacturing-as-a-Service
Cloud Continuum: From Cloud to IoT to Edge Computing
Optimal Neural Network Architectures for Edge AI
CIEM solutions: Manage access risk in multi-cloud environments
The Future is Here: Unravelling the Potential of 5G Technology for Businesses
How Mid-Market Companies Are Revolutionizing eCommerce?
The Art and Science of AB testing for UI/UX design
The Future of ERP Systems
Seven Popular Large Language Models
We're here to help!
No obligation quotes in 48 hours. Teams setup within 2 weeks.
If you are a Service Provider looking to register, please fill out this Information Request and someone will get in touch.
Outsource with Confidence to high quality Service Providers.
If you are a Service Provider looking to register, please fill out
this Information Request and someone will get in
Enter your email id and we'll send a link to reset your password to the address
we have for your account.
The IT Exchange service provider network is exclusive and by-invite. There is
no cost to get on-board;
if you are competent in your areas of focus, then you are welcome. As a part of this exclusive