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Big Data Consolidation is Dependent on These Four Actions Featured

Big Data Consolidation is Dependent on These Four Actions Erik Mclean

Over the past few years, big data has evolved from a boardroom buzzword to a force that is now taking the world by storm. A few years ago, business leaders lacked ways to harness vast amounts of data and analyze it to get information. However, big data has become a game-changer, with administrators and business leaders now analyzing and getting knowledge about customers, business processes and daily operations. Although a point of success within a business, big data presents many challenges that must be addressed for success to be realized in big data initiatives.

What makes big data a challenge is the massive amount of data from different sources and in different formats that require large storage and good processing and analysis. With unstructured information flowing fast from different sources, leveraging it and getting the right insight for decision-making is never easy. This is where big data consolidation comes in. Data consolidation aims to make collected data manageable and usable.

Here are the four tips for success in big data consolidation:

  • Migration of data away from legacy applications

Migration is always the first step in data consolidation. It entails moving information away from legacy applications or programs and ensuring that it can be leveraged successfully in the application that uses this data frequently. It is recommended that legacy applications be retired altogether to achieve better consolidation of big data. It is important to collect critical information in legacy systems then migrate it into new applications or systems before retiring the older applications. Doing so makes data actionable, and systems are faster.

  • Understand the real cost of data consolidation

First, you must understand the implementation cost of not consolidating data. Once you know the cost of not doing so, find out the cost of consolidation and alternatives, if any. Check if consolidation is an economically viable option. As a business, this action is critical because you would not want to operate at a loss. In reality, the cost of consolidation is often a one-time thing. Once you are done with consolidation, you will enjoy the potential of big data within your company. Consider aspects that may lead to loss, such as security, personnel and natural disasters. These three can affect your applications and data centers that store your data.

  • Be selective

Although all data is indeed important for an organization, not all the data should be consolidated. Instead, stakeholders should carefully choose what should be consolidated and what should not. Select information that, if consolidated, can ensure productivity and does not hamper performance. Administrators should consider instances where data does not need consolidation, such as when security dictates that some data should be kept in separate servers or when data is outdated and will only serve to fill the database. Some data should not be consolidated because they will not add any value and might slow down hardware resources.

  • Make use of professional services

Although most stakeholders and decision-makers love doing their big data consolidation internally, seeking professional help to streamline your operations will be helpful. You must always know that big data consolidation can be complicated, and having an extra professional hand can be advantageous to the organization and data. Consider stakeholders' opinions before hiring professionals to help your organization in the management and consolidation of your data. A unified approach from skilled professionals can deliver the necessary consultative support, expertise, and proper planning and execution solution. By following the best approaches and with support from IT service providers, you will realize efficiency, flexibility, and cost-saving due to the right big data consolidation.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

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