Category: Big Data Practices

To maximize the power of big data, it is recommended that you follow a set of best practices.

Establish significant data business objectives.

IT frequently gets sidetracked by the latest “shiny” object, such as a Hadoop cluster. Start your big data journey by outlining the business objective in detail. Gather, examine, and comprehend the business requirements first. Your project must have a business aim; it cannot just be a technical one. Before you even start the process of utilizing big data analytics, the first and most crucial step you should take is to understand the company’s requirements and goals. To have a target to shoot towards, business users must be clear about the outcomes and results they want to achieve.

Collaborate with partners to assess the situation and plan.

The IT department shouldn’t work on a big data project alone. To introduce an outside set of eyes to the organization and assess your current position, it must involve the data owner, a line of business or department, and maybe an outsider, such as a vendor providing big data technology or a consultancy. There should be constant monitoring throughout the process to ensure that you are gathering the data you require and that it will provide you with the insights you seek. Do not simply gather everything and inspect it once you are finished.

Find out what data you already have and what you need.

No amount of data can ever be equivalent to “good” data. It will be up to you to assess whether you have the correct data – frequently, data is disorganized and is in various formats since it is randomly gathered. Knowing what you lack is just as crucial as knowing what you have. It is not always possible to predict the data fields needed in advance, so be careful to build flexibility to make changes in the database infrastructure as you go. The bottom line is that you regularly need to test the data and evaluate the outcomes.

Maintain an ongoing dialogue. 

Collaboration is effective only when there is constant communication between IT and the stakeholders. Midway through a project, goals may change; in that case, IT must be informed and the required changes made. You might need to go from collecting one type of data to collecting another. In your opinion, that shouldn’t go on longer than it needs to.

Create a clear map that delineates anticipated or desired outcomes at critical intersections. Users should review 12-month project every three months. This offers you time to reflect and, if required, adjust your route.

Start slowly and move quickly in later stages.

The initial big data project shouldn’t have an exceptionally high bar. It is better to start with a tiny and simple-to-manage proof of concept or pilot project. One shouldn’t try to take on more than one can handle because there is a learning curve involved.

Pick a place in your business processes where you want to make improvements that won’t significantly impact if something goes wrong or poorly. Additionally, you may wish to employ DevOps and agile project methods and an iterative implementation process.

Analyze the demands on big data technology.

IDC claims that the great majority of data is unstructured—up to 90%. However, you must still consider the data sources to choose the most acceptable data repository. You can choose between structured query language (SQL) and NoSQL databases, with numerous variations of each type.

Apache Spark may be required for real-time processing, although Hadoop, a batch process, may be sufficient for non-real-time use cases. Geographic databases are another option for data spread across several places, which may be necessary for a business with numerous offices and data centers. Additionally, look at each database’s specialized analytics capabilities to determine whether they apply to you.

Align with cloud-based big data.

Since cloud computing usage is metered, and big data requires processing a lot of data, you must exercise caution when using it. Rapid prototyping is possible with the help of services like Amazon EMR and Google BigQuery. The advantage of the cloud is that you can prototype your environment before utilizing it.

Using a data subset and the numerous tools provided by cloud providers like Amazon Web Services (AWS) and Microsoft Azure, you can set up a development and test environment and use it as the testing platform in hours.

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