Big Data

What is Big Data How we contribute in the Implementation

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Big data contributes to be larger chunks of the raw data which is analyzed, stored and collected through different means. The chunks of the collected data are being used by different business firms for enhancing efficiency and making better decisions.

It is possible to avail big data solutions both in the unstructured and structured forms. You can analyze and organize structured data into the database easily. However, it is really hard to analyze unstructured data. Furthermore, you need to make use of a bunch of different formats for achieving the goal. In addition to this, it is hard to interpret the same with the aid of traditional processes and data models.

Implementation of big data

Big data has earned a high reputation in different business firms. The significance of big data in different business processes and outcomes are altering daily. This write-up comprises of few integral best practices which should be opted by the implementation team for enhancing the chances of success:

Understand the requirements of the business before data collection

You need to start the implementation of big data by the collection, analysis, and understanding the requirements of the business. It is recognized to be the first step in the process of data analytics. You require aligning the big data projects with the goals of the business.

Implementation of big data is regarded as a business decision

Before implementing big data, you need to remember that the implementation of big data happens to be a business decision. Do not mistake it to be an IT decision.

Analytics solution has gained high prominence and success when you opt for the same from the business perspective instead of choosing the same as the IT/Engineering one.  Instead of the “Build it and they will come” model, you should go for  “Solutions that fit defined business needs.”

Making use of the iterative and agile approach for the implementation

The big data projects are known to begin with a certain use case and data set. Once the implementation is done, the business needs to evolve as they gain an understanding of the data. They also begin to harness the potential value of the data.

You should be able to use agile and iterative implementation techniques to deliver faster solutions. Speaking of big data analytics and practicalities, it is recommended to start small by the identification of high value and specific opportunities. It is possible to achieve the same with the big data framework.

Evaluation of the requirements of the data

To understand if the business is ready for big data analytics, it is recommended to conduct a complete data evaluation of the business. You also need to find out how it is possible to use big data for the benefit of the business.

For the process, you need to have specific inputs from the stakeholders of the business. You need to analyze what are the data, that should be managed, retailed and accessible and what are the data which should be discarded.

Easing the skills shortage with governance and standards

As big data has a lot of potential, there is an increasing shortage of professionals who are capable of mining and managing the information. The best option for overcoming the potential skill problems is known to be standardizing the efforts of big data within the IT Governance program.

Optimization of knowledge transfer within the center of excellence

You need to come up with the CoE or Center of Excellence for planning the artifacts, sharing the solution knowledge and ensuring oversight for different projects as they are useful in reducing the total number of mistakes.

Whether the big data is an expanding or a completely new investment, it is possible to share the hard and soft costs across the business firm. Another benefit of opting for the CoE approach is that it will continue driving the information architecture maturity along with big data in a more systematic and structured way.

Opting the sandbox approach for performance and prototype

You need to let the data scientists construct the prototypes and data experiments with the aid of a programming environment and preferred language.

After the successful concept proof, you need to reconfigure or reprogram implementations within the IT turn over team systematically. At times, it is hard to understand what you are looking for exactly as the technology is found to break the current ground and achieve specific results which were thought to be impossible.

Alignment with the cloud operating model

It is a prerequisite to creating the analytical sandboxes on the resource and on-demand management for controlling the complete flow of data, from integration, pre-processing, in-data summarization, integration, analytical modeling, and post-processing.

It is necessary to opt for public cloud provisioning, private cloud provisioning along with security strategy as they play a vital role to bestow support to the changing environment. In such cases, when the sensitivity of data offers assistance to faster in and out prototyping, it can prove to be very effective.

Relating big data with the enterprise data

For unleashing the big data value, it is a prerequisite to associate the same with the enterprise application data.

The business organization should be establishing new capabilities and making the best use of the prior investments in platform, infrastructure, data warehouses, business intelligence, instead of throwing them away. The integration capabilities are useful to the knowledge experts in correlating different types and sources of data to make specific associations and making meaningful discoveries.

Bottom Line

At present, a plethora of business organizations make use of big data for seeking information for bestowing support to the organization and serving the customer.

Thus, the business firm is sure to reap a lot of benefits from the big data. The use of big data has become increasingly common these days for outperforming peers. In the majority of the industries, competitors are opting for specific strategies which are the result of analyzed data for the capturing and innovation of value.