Even though Big Data is perceived to be a novice concept, but since the last few years, the use of this advanced technology to handle the large datasets generated over the internet has surged significantly. The constantly rising huge volumes of data and the need for in-depth analysis, storage, transferring, etc., for devising vital business strategies have compelled enterprises to rope in the Big Data technology to bring in operational efficiency and increased revenue.
The implementation of Big Data technology requires pooling of a team of professionals who can gauge the infrastructure requirements and design suitable Big Data project for an enterprise. But first of all, the Big Data project team should consider these guidelines for ensuring successful implementation and working of the technology:-
1. IT is Not Solely Responsible for Big Data
Most important aspects of the best practices in the implementation of Big Data is that the data analytics solutions represent the business and are not just a result of IT processes. IT just helps in the creation of a new and innovative way to store, transfer and analyze data that form a crucial base for vital business decisions.
2. Analyse the Business Requirements First
Big Data implementation task must begin with collecting, analyzing and understanding the basic business requirements. This is a pertinent step as it helps in aligning the Big Data project with the organizational goals.
3. Adopt an Intelligent yet Cautious Approach
The Big Data project for any business starts with monitoring, collecting and analyzing a particular data set. Over the time, it has been witnessed that an organization’s needs to incorporate more data sets gradually emerge owing to the value they harness from previous analysis of data sets. Rather than opting for getting all the previous and current data sets into Big Data process, an intelligent but cautious approach must be adopted to analyze the effectiveness of the Big Data project devised by the organization. On the practical grounds, the successful start of a Big Data project demands smaller steps in the direction of identifying specific and essential business opportunities that contribute to the larger goals of the organization.
4. Analyse Data Requirements
It is always advised to carry out a complete evaluation to judge whether a business is ready for employing Big Data analytics or not and how can it be advantageous for the long-term success of the company. This important business decision must be taken to evaluate which of the data needs to be retained, managed and processed and which data needs to be eliminated.
5. Develop an IT Governance Program
The availability of Big Data skilled professionals is falling short of the rising potential of Big Data. With experts required to manage and mine relevant information while ensuring the seamless working of the project, one of the best ways to combat this shortage of seasoned experts is by standardizing the process through a stringent IT governance program.
6. Establish Centre of Excellence (CoE)
Error minimization is one of the major challenges in a Big Data project that can be efficiently tackled by establishing a Center of Excellence (CoE) as it aids in sharing solution knowledge, artifacts of plans and ensures constant oversight on projects. Roping in this approach can benefit the organization by driving the Big Data and overall information architecture in a structured manner.
7. Allow Experimenting & Creation of Prototypes
Data experiments and construction of prototypes by data scientists in their preferred languages must be allowed as a successful data concept can be reprogrammed with the help of the dedicated IT team of professionals. Sometimes, concepts and processes that were presumed to be impossible might become a possibility through such experimentation with the Big Data technology. These experiments can be implemented during Big Data training to make the scientists learn more.
8. Combine the Benefits of Using Cloud Operating Model
The provisioning of a well planned private and public cloud model along with deployment of security strategy supports the changing requirements by creating on-demand analytical sandboxes. It also guides the resource management in controlling the entire data flow, integration, pre-processing, database summarizing, post processing and analytical modelling.
9. Link Enterprise Application Data with Big Data
Connecting a link with the enterprise application data helps in unlocking the true potential of Big Data. Instead of discarding their previous investments in infrastructure, data warehouses, platform and business intelligence, organizations must fully utilize them to create new goals and understand the correlation between different types of data and their sources to come to meaningful and profitable conclusions.
10. Collaborate Analytics & Decision Making Into Operational Routine
To gain a competitive advantage over others, a business must bring analytics to priority and must establish their stance as a data-driven organization. With widening the scope of Big Data, enterprises must understand that such data analytics can change their business modeling and future potential and must be incorporated into their operational routine. Therefore, data analytics should not be perceived as a part of the decision-making process but an equal stakeholder in commanding the working of the organization.