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5 Ways to Fuel Your Big Data Analytics in 2018 – 19

MMS Founder

Article originally posted on Data Science Central. Visit Data Science Central

Big data is the present and the future in the world of technological innovations. Big data is a collection of small details and information points in varying silos. It is a powerful technology that it can bring about a significant digital transformation with its use of data.

Organizations must utilize their data in such a way that they can gain its maximum advantage. The knowledge of how this needs to be done is very essential for big data to thrive and deliver the optimum results.

The big question about big data is how to fuel and maximize its advantage?

Here are a few steps that would fuel your big data analytics:

1)    Go the Agile way

The conventional norm of analytics depended heavily on IT support if changes were needed to be made to the ETL and static data warehouses.  This led to a complex and profoundly intertwined reporting system. Thus, it would take months and months to obtain analytics using this method. However, after the use of big analytics with Hadoop architecture, raw data can be worked on, curated and engineered much faster. The base of this is a quick understanding of spreadsheets, numbers and visuals. This has resulted in an overwhelming decrease in time for data delivery. Also, the data models used in the production process can be reused.

2)    Increased data delivery

Using analytics, you can make optimum use of all the raw data in your organisation. Insights and analysis can be gained on these sets of data by using different datasets and data models. You can create a bank of data within your organisation that can be utilised by engineers, business analysts, CDO’s, technical support and more.

3)    Artificial Intelligence

Using big data, you can upgrade your AI research and implementation to the next level. In today’s world, operationalising AI can be very tricky as the process is extremely customised in nature. This process is known as re-implementation and makes use of large AI frameworks that are big in cost and size. Hence, they are difficult to integrate with other systems. With big data, you can combine datasets with AI insights and generate systems that allow data preparation, future engineering and more.

4)    Use of cloud

Taking your data analytics to the cloud will give you scalability, flexibility, lower costs, faster response to business and reduced IT involvement.  Security is one of the significant concerns that organisations face when it comes to using the cloud for analytics. Hence, some organisations refrain from adapting to cloud and prefer on-premise analytics. However, by utilising specific security tools and platforms, you can seamlessly migrate to the cloud for all your significant data needs.

5)    More power to business intelligence (BI)

There is a need to interconnect big data with BI. All organisations work with big data to some extent, but their activities are always separated from their BI tools. With many similarities between big data and BI, these two can be brought together to create the data bank as mentioned earlier.  Big data powered by BI can bring about a digital transformation, increased customer engagement, better customer acquisition and retention.   

You have seen how big data enables organisations to transform and empower themselves in this digital economy digitally. You can enable yourself to take action on the insights revealed by your data. You can focus on removing various barriers between people, tools, systems and more using data analytics. Also, using big data, you can capitalise on various emerging technologies to bring about a global change by building banks of data that can be used to build better and more sophisticated systems.

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