It is hard to talk about Big Data without talking about management, integration and warehousing matters. There are just so many things that are involved in data management. Almost all businesses have Big Data projects that are currently in the pipeline. Handling Big Data is a major headache. There are so many things that you can do in order to have an easier time when it comes to handling Big Data, including the development of a data warehouse.

Of those Big Data projects in the works, however, only a handful of them that are actually incorporating IT management best practices to get maximum benefit from their data. The reason why businesses are all about Big Data nowadays is because many are waking up to the realization of what an asset data is. When data is managed well and IT efforts are executed correctly, the results are always amazing.

Here are some best practices for Big Data management that will benefit your business greatly.

1. Set up a data warehouse
Handling Big Data will require massive storage space, and what better space than a data warehouse? A data warehouse is simply a central point where all the business data from disparate sources is stored and integrated. Setting up a data warehouse will make data management so much easier. You will be able to access data quite effortlessly. On top of this, it will make data analysis a walk in the park.

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The only problem with data warehousing is the cost that is involved. Setting up a data warehouse is more like building a real warehouse online in the cloud. The costs are high, so you need to get it right from the beginning because repairs can be prohibitively pricey.

2. Agile and prototypical approaches needed
It is important to note that Big Data projects are always naturally iterative. They therefore require prototypical approaches. Sandbox environments that make it possible for data analysts to query Big Data fast and then publish the results are of great importance to the Big Data value process. The data that these queries operate upon is not structured neatly into some fixed-record-length systems such that you know what the final result will be. Therefore, it is necessary to have an iterative process that will operate in such an unpredictable data environment.

3. Public clouds also work
Most large businesses will shy away from using public clouds primarily because of security and governance issues. However, in most cases, public clouds provide an environment that is ideal for speedy Big Data analytics prototyping. This is as long as you get your prototypes off the public cloud the moment you are done running them. Public clouds are also an economical place to store and archive your raw Big Data. The use of public clouds, however, should be clearly articulated in your IT policy.