One of the most interesting and complicated challenges in Business Functionality is, creating a Business Architecture. For decades the IT Industry has been following an application centric Architecture while leading organizations are following a data centric approach. In this article, I’ll explain how the Data-centric approach differs from other Architecture/Approach for an Enterprise and what is the importance of it for an Enterprise?

The progression of information structural design:

A lot of the issue comes down to how information architecture has developed within Enterprises. Identifying the great importance of data as a Business-enabler, organizations initially tried to resolve the issue of Information Lake as they wanted to centralize their various data sources at a point.
This was the era of the enterprise data warehouse, used to store information, and of business intelligence software used to Identifying and reports it. This first age group of information tectonics helped accelerate corporate reporting, Identifying information-flow cycles, and shed light on the dark of internal divisions, highlighting operational issues and allowing businesses to capture competences that would have been hard to gain otherwise.
However, data warehouses need data frameworks and the data has to be interpreted to these frameworks. Data warehouses only keep a summary of the company’s transformed data, rather than the full picture. This is useful for business units that want to carry out simple reporting functions to respond to questions linked to their day-to-day needs, but it lacks the level of granularity required for accurate forecasting and predictive analysis.
The concept of Data Lake emerged as a possible solution to these issues. Data lakes are typically designed to complement data warehouses, lowering the cost of storage and computation. With a data lake, companies are capable of storing a large bulk of data.
However, this second-generation tectonics hasn’t fully resolved the issue either. While the data lake has administrated progressive improvements in analytics strengths and business intelligence, it hasn’t resolved the vital issue – that in an application-centric approach for IT, the applications still own the data.

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The basis of the setback: An application-centric approach :

In an application-centric approach, every application has its own data model and is liable for collecting and storing the data it needs. This causes large information lake within enterprises because every enterprise’s unit and every application have a sectioned and separated view of its own data. The data is not shared everywhere in the organization as a whole. That is to say that the data withdrawn from day to day business processes feed into the company’s data warehouses and data lakes, but the possible significance of that data is probably fed back into the enterprises’ functioning systems.
As the data is sectioned, users can only withdraw fractional intelligence. Moreover, data often has to be copied primarily into a data warehouse and then afterward in a data lake. This persistent replication of data has resulted in companies having to face with both unnecessarily huge amount of data as well as valuable data standard problems, leading to enough inefficiencies and execution problems.
The online companies that unwrapped big data on the world meantime were built around data from the beginning. They moved away from the traditional application-centric perspective and keep data at the core of their business-as-usual. As long as businesses use the traditional application-centric approach to IT they will contest to match the performance of disruptors.

 

Away from long-established Tech, The new trade as usual:

Digital disruptors initiated with a new business and IT pattern. They built data-centric enterprises, creating modern introduction such as Chief Data Officer and Chief Data Scientist. They also put data at the heart of their IT architecture, in recognizance of the value of data in driving the business, rather than being an outcome of a business process executed through an application being built for a distinct functionality. They also provided business units and the “IT teams” with the digital stages and ability they needed to move seamlessly between innovative scope and operational systems in a stretchy, secured and well-governed system. They also made a new view to business which has the benefit of the whole potential of Big Data technologies, rather than the limited use of them to solve a particular issue.
By taking a data-centric approach and building their IT systems around the concept of data-centric architecture, they have obtained a very ultimate and seemingly unreachable state as digital disruptors. In the information era, these innovative enterprises have departed the point where the data only supports or enables the business – accrescent, the data is the business. Only by rethinking their entire IT infrastructure and adopting the data-centric approaches of disruptors can incumbents embed data smartness into their manipulation and compete.

The resolution: A data-centric approach;

What is Data Centric?

Data centric refers to a structural design where data is the key and permanent asset, and applications come and go. In the data centric architecture, the data model precedes the implementation of any given application and will be around and valid long after it is moved out.

Why should today’s leading incumbents adopt a data-centric approach?

In the fast-moving digital period, companies don’t know what business processes they will need to execute or what their business rules will need to be like in the future. The only thing that seems fixed is that for decision-making they will require to use data. Becoming a data-driven organization is therefore vital.
The major characteristic of a data-centric network architecture is that the data is the central asset and that it is stable, while the applications around it may come and go. The data will be around and licit long after the consuming applications are gone. In this kind of architecture, the storage and groupage of the data are the first step of the process and forgo the maker of any given application.
Data is stored in a centralized location, in the form in which it was genuinely collected and only given a suitable structure by the checkout process actually using it. This approach allows room for more dynamic data analysis and can behave much more soon to the prompt changes within a business.
A complete transmutation of an incumbent’s architecture to take a data-centric approach will seem like a subjecting deed. For this reason, HEX64 offers a single product that can gradually integrate a company’s existing systems and frameworks. HEX64 Data Centric brings together the latest open source big data technologies, providing a platform that can transmute the heritage architecture of big enterprises, in small, smart steps.

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