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Many big data environments combine multiple systems in a distributed architecture; for example, a
central data lake might be integrated with other platforms, including relational databases or a data
warehouse. The data in big data systems may be left in its raw form and then filtered and organised as
needed for particular analytics uses. In other cases, it’s pre-processed using data mining tools and data
preparation software, so it’s ready for applications that are run regularly.
Big data processing places heavy demands on the underlying computer infrastructure. The required
computing power is often provided by clustered systems that distribute processing workloads across
hundreds or thousands of commodity servers. Getting that kind of processing capacity in a cost-effective
way is a challenge. As a result, the cloud is a popular location for big data systems. Organisations can
deploy their own cloud-based systems or use managed big-data-as-a-service offerings from cloud
providers. Cloud users can scale up the required number of servers just long enough to complete big
data analytics projects. The business only pays for the storage and compute time it uses, and the cloud
instances can be turned off until they’re needed again.

How Big Data Analytics Works?
To get valid and relevant results from big data analytics applications, data scientists and other data
analysts must have a detailed understanding of the available data and a sense of what they’re looking
for in it. That makes data preparation, which includes profiling, cleansing, validation, and transformation
of data sets, a crucial first step in the analytics process.
Once the data has been gathered and prepared for analysis, various data science and advanced analytics
disciplines can be applied to run different applications, using tools that provide big data analytics features
and capabilities. Those disciplines include machine learning and its deep learning offshoot, predictive
modelling, data mining, statistical analysis, streaming analytics, text mining, and more.
Using customer data as an example, the different branches of analytics that can be done with sets of
big data include the following:
• Comparative analysis: This examines customer behaviour metrics and real-time customer
engagement in order to compare a company’s products, services, and branding with those of its
competitors.
• Social media listening: This analyses what people are saying on social media about a business
or product, which can help identify potential problems and target audiences for marketing
campaigns.
• Marketing analytics: This provides information that can be used to improve marketing campaigns
and promotional offers for products, services, and business initiatives.
• Sentiment analysis: All the data that’s gathered on customers can be analysed to reveal how they
feel about a company or brand, customer satisfaction levels, potential issues, and how customer
service could be improved.

Big Data and AI
Big data and AI have a synergistic relationship. Big data analytics leverages AI for better data analysis.
In turn, AI requires a massive scale of data to learn and improve decision-making processes. With this
convergence, you can more easily leverage advanced analytics capabilities like augmented or predictive
analytics and more efficiently surface actionable insights from your vast stores of data. With Big data
and AI-powered analytics, you can empower your users with the intuitive tools and robust technologies
they need to extract high-value insights from data, fostering data literacy across your organisation while
reaping the benefits of becoming a truly data-driven organisation.

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