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Ten tips to avoid your #BigData project failure
If you’ve shown some interest to this week blog post, we assume that it’s because you want to avoid making mistakes that could cause your Big Data project failure.
Here are some points you must consider:
✓ Begin any Big Data project with a data review and classification process. In order to design a powerful storage architecture you must determine whether data is structured, unstructured, qualitative or quantitative. It is also a good idea to estimate the growth of data based on past trends and future strategies.
✓ Create a simple overview of how data flows within your organization. Having a simple diagram showing where data is created, stored, and circulated is useful when it comes to work within a group. Putting everyone on the same wavelength can help you avoid misunderstandings that are expensive.
✓ Consider future data storage requirements based on the success of the Big Data project. Big Data projects may reveal new information or require you to change business processes. Information from the project may in other hand require additional storage capacity, resulting in exponential growth in capacity requirements. Always think in long term.
✓ Be flexible. Many projects are based on both scale-up and scale-out storage technologies. Each organization and project is unique. The choice of a storage technology must be based on the objective to be achieved and not on a particular technical architecture. Many vendors offer scale-up and scale-out products that can work together.
✓ Data storage requirements may increase, but consider to move automatically low-visited data to a cheaper, slower storage device. Removal is also a considerable option in the long term. Regardless of where the data comes from, where it is processed and where it is stored, it always has a useful life. Deciding to delete data is a complex task, but this can enable you to realize huge savings in long term. Automatically moving data to a slower storage device is an easier option that always involves great benefits.
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✓ Ask providers what will happen when you reach capacity limits or theoretical performance limits. Even if you start with a small Big Data project, it will surely get bigger with time. Understanding how the chosen technology can evolve will help you avoid unpleasant surprises in the years to come.
✓ Be prepared for the worst. Even the simplest machines can eventually break down or black out. Ask your vendor what would happen if various elements of the storage platform were to fall. A well-designed system should never have a point of failure.
✓ Create a quota system early in the project to prevent future management problems. IT projects tend to occupy all available space if they are not controlled. Quotas allow you to define how much space each user or project can access. Assign the responsibility for managing this capacity to an entity responsible for this data or define a policy.
✓ Always involve IT security experts in your Big Data projects. Digital data has value. Even though the Big Data project involves only one research group, the IT security team must be involved from the start, so that security remains at the heart of the project.
✓ Don’t forget to take management time into account when it comes to calculate storage costs. Total storage costs must include the time required to supply and manage the platform. A resilient, highly automated system that does not require a full-time administrator saves far more money in the long run than a less expensive hardware that requires a lot of manual labor.
I really hope these tips will help you in your project planning. In case you’ve any question or need an advice please feel free to write us and our #DataHeros will contact you asap!