Enterprise Big Data Implementation – Top 7 Do's and Don'ts
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Enterprise Big Data Implementation – Top 7 Do’s and Don’ts

Big Data is a fast-emerging business specialty with proven capabilities in various sectors related to business and administration. Organizations need to be well prepared and fine-tune their data by following best practices to make it work. Not every chunk of data makes usable big data, and here we will discuss what makes big data workable and the dos and don’ts of handling big data.

Big data do’s and don’ts.

As we said, big data comes with a lot of promises for different industries and business specialties. It can have a big impact on business decision-making and data analytics. However, the advantage of big data can only be leveraged if it is appropriately managed. The ideal practices of big data are being evolved gradually; however, there are established practices being followed by the winning big data users. Here are some do’s and don’ts for big data implementation. All this guidance is based on the knowledge and expertise of professionals with real-life big data project exposure.

Do’s of big data

  1. Do involve various business sectors in the big data initiatives.

Big data initiatives are not just isolated events, but they stay a crucial part of the business activity. Involvement of all the business units is necessary to get the total value out of big data and get actionable insights. Big data can help enterprises leverage big volume data to gain insights into user behavior, market trends, events and make predictions. It may not be possible with a random data snapshot. It can only capture a sampled part of the entire data volume. On the other hand, big data can process the data as a whole. As a result, organizations will be focusing on all data types coming from different sources to understand the bigger picture.

  1. To evaluate the big data implementation model.
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The volume of data included in big data management is a real concern while planning big data implementation. As you have to deal with data in petabytes, the ideal solution is to handle it with data centers. Simultaneously, it would be best to consider the cost factor before choosing the appropriate big data storage. It is ideal to consider cloud services as big data choices as these can be highly flexible, cost-effective, and scalable. However, the services of various cloud providers may vary, so you need to closely evaluate what they offer and then choose the appropriate one. Storage is the most critical component of big data implementation, and it should be evaluated carefully in every big data project. You may also get another perspective on Big Data challenges such as Variety, Volume, and or Velocity.

  1. Do consider the traditional data sources.

 As we have seen, there are plenty of different sources for big data, and these data points may keep on increasing day by day. A huge volume of data is being put into the big data stores for processing. Many may think that their traditional data sources may go out of use with this. This is not the truth, as traditional data sources remain the critical components of big data storage. Such conventional data sources always contain valuable information, which may be critical in big data analytics. So, these should also be used effectively in conjunction with unconventional big data sources. If you are looking for reliable consulting for big data planning, you can take the assistance of RemoteDBA.com experts. The actual value of big data lies in deriving information from all possible data sources and factoring in all those.

  1. Do consider consistent data sets.
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In an ideal big data environment, each piece of data comes from a unique source, and there are plenty of such sorted and unsorted sources for data. However, structure, format, and types of big data from various sources may vary largely. The major thing to consider is that this data is not structured and not cleansed at the sources when it enters into big data environments. So, before trusting any random data source, you have to check the consistency of the data through observation and objective analysis. After confirming the consistency of data and its reliability through these m measures, you can make it a part of your consistent big data source. Deriving consistent metadata set by carefully observing the pattern is important in planning any big data project.

  1. Don’t rely on a single approach for big data analytics.

There are different technologies in managing and processing big data for analysis. Apache Hadoop and MapReduce are the foundations of all big data platforms. So, you have to thoroughly evaluate the technology involved in different big data approaches to see which one suits the best to your need. Don’t adopt a random approach without testing. Choice of your apt big data approach is crucial to reaching the desired goal. Also, avoid relying on a single approach to your big data management, but keep a combination of more than one best approach to stay balanced.

  1. Don’t start big data in a big way until you are fully ready.

Slow and steady wins the big data race too. At real-time implementations of big data, it is advised that you start with smaller steps while initiating a big data project. Run some pilot projects and gain enough knowledge and insights to plan the actual big data implementation. As we know, the potential of rightly implemented big data is huge, but it can only be enjoyed when you reduce the scope of any mistake in its implementation. Take your time, get expert consulting, and then get set with big data step by step.

  1. Don’t underplay data security.
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Data is the most critical asset in big data planning. As it is in huge volumes of petabytes, you need to ensure strict security in every aspect of big data implementation. After processing, you may get a rich data subset that can offer you some actionable insights. Data security now becomes more essential at this point. As more and more data get processed, the more valuable it becomes to the organization. Therefore, data security needs to be a primary consideration at each phase of the big data lifecycle.

As an emerging field, organizations are still in the research and planning phase when it comes to adopting big data. So, we have focused on the big data initiative’s major do’s and don’ts. I hope these tips have provided you with essential insights into making your big data strategy more successful.

Enterprise Big Data Implementation – Top 7 Do’s and Don’ts

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