The advent of the COVID19 pandemic has opened the eyes of all the organizations, big or small, that digital transformation is the only answer. Integrating business processes with a digital platform offers a remarkable opportunity for the companies to create strong bonds to drive recurring profits. The entire management of different operations becomes more consolidated.
Even though this new heightened attention to the digital transformation has surfaced, there are a few data challenges causing primary obstacles in the digital transformation of the companies. The spread and outcomes of this pandemic are almost under control, but the intensification of this transformation has caused a ruckus.
Now that the companies are facing such obstacles during digital transformation, new critical points are surfacing that need to be tackled. In order to bring data under control and meet these challenges, every organization will have to give deeper thoughts and seek better services.
These technology trends are going to specialize and intensify the digital transformation of the companies. These three trends will show a new path to follow for such transformation.
1.Machine-understandable data transformation
One of the biggest hurdles is that data cannot be understood by machines properly. We do not have that perfect platform where data can be understood and interpreted by machines the way humans do. Machine-understandable data is the prime constraint as the platforms cannot handle variance, irregularity, and distribution appropriately.
The decision-making process of a software platform entirely depends on how it is coded and that makes it rigid. Human intelligence is entirely based on powerful analysis and contextual intelligence. Machines, on the other hand, need to be fed with proper functions in order to make data more understandable for making better decisions.
In this aspect, a new trend has risen that adopts modern approaches for data integration for fusing knowledge and data management. This technique develops systems that allow enterprises to identify hidden relationships and facts via inference mechanisms. Due to this technique, a large scale of data would have not been recognized as an important asset for the companies.
This technique revolutionizes the patterns making data not only machine-readable but also understandable. It is done by capturing, reading, and encoding contexts of the real world by connecting with the data sources based on a project, process, person, topic, etc.
This stage is rather a transformation from data interpretation to knowledge achieving automation and a higher degree of interpretation. This transformation requires higher-level insights regarding the relationships between things and the entire world. It might sound very critical but certainly achievable.
2.Semantic graphs broaden the landscape and fuel data integration
Relational systems that are used contemporarily are not designed for supporting relationship-rich landscapes of data and connections with the drastically changing trends, meaning, and requirements. Data integration, in fact, is an artifact in a consolidated way developed not to manage large-scale data and uneven information systems.
An algorithm depends on what the coder feeds as functions to read data. It means that the coder will define what is meaningful to the algorithms used for such information systems. It makes the algorithm understand and access data. On the other hand, systems developed by IT companies are not commendable in interpreting relationships in real-world complex data and connections.
To make it more realistic, the rules need to be drastically changed from what is being followed these days. In this aspect, semantic data models and graphs are the finest and fastest-growing technology leading to achieving what we have been lacking. In fact, it is the most natural way data can be represented and understood.
This new method of representing data will enable machines to create and discover more connections. The newfound relationships will also help in the proper interpretation of data fed to the information systems. The latest trends of creating and utilizing semantic graphs and data models will lead to connecting unstructured, semi-structured, and structured sources to reveal a better picture. In this aspect, it will become easier to connect enterprise data and identify the relationships that are hidden and existing within it.
3.Data fabric will be driven by query answering
Data fabrics are the prime networking elements that connect and weave data management systems existing in a bigger domain. This element enables accessing more data and helps applications interpret better. This fabric system will emerge in a better form in the upcoming years to make enterprise resource planning more efficient.
In this decade, data fabric will enter the next generation level and will earn a significant spot in digital transformation. The data management domain will be enriched with the presence of such elements developed to make data interpretation more mature.
Filing systems used in enterprises have become more insignificant as we cannot find the right information within a short span of time. These systems need to be more consolidated and resourceful. Data fabric techniques, in this aspect, will be the best dabet to deliver the ideal platform where users can access and find specific files or information relevant to an entity in the business. A crucial increase in data connectedness has become mandatory due to the emergence of large-scale and digital transformation of businesses.
In this context, data warehouses and data lakes are the old concepts that do not fit into the modern requirements of digital transformation. With time, these concepts and applications will fade away. Due to the increasing diversity in the data landscapes, crating data catalogs has given some relief. Cloud-based data warehouses are not efficient enough to fulfill this need as they have limited relational models to incorporate into the systems.
Even though data catalogs have emerged, a big question still hangs over our heads. How can we get business answers from such data source catalogs? This is where the query machines are being set so that users can find specific data based on their requirements. This step will enable an enterprise to find the answers in no time and will also create a platform for using and reusing data more efficiently.
Data fabric in collaboration with commendable query machines can help us bridge the gap of finding data. This newly emerging technology can help us to eradicate disparity between fragmented landscapes of data and provide a consolidated platform. It will help enterprises to seek answers to questions, make data highly actionable, and derive insights significantly. These powerful query-answering software platforms will enable an enterprise to utilize data more efficiently and derive the answers they are looking for.
With the emergence of new data and formats due to the frantic changes in the business models and scenarios, every enterprise is in dire need of a digitalized platform with efficient data management systems. Generated data needs to be aptly interpreted and used for apt decision making. For this, these three new technology trends will be seen getting better and better.
The new significant development in creating such platforms will make data governance and distribution better. The machines will understand data better and will answer the questions asked by the users. It will help enterprises to seek apt information based on certain business entities and will help to make better decisions in the future. Enterprises should be ready to handle a bigger scale of data and make necessary transformations to go digital for handling future requirements.
Author Bio: Jafar Sadhik
Jafar is a passionate digital marketer possessing in-depth knowledge in the fields like SaaS tools, CX, churn statistics, and others. He loves to read books during leisure time and is a great admirer of Agatha Christie’s works.
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