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Data Analytics5 Trends That Would Shape Data Analytics

5 Trends That Would Shape Data Analytics

In the past year, if you have had the privilege to interact with enterprise customers of different sizes, you
would notice a couple of trends. These trends are taking the data and analytics industry by storm. Just
look at the actions of investors and research firms.

1.From Data Center/Cloud to Distributed Cloud

There is a major shift to the cloud from the data center. Cloud infrastructure has continued to grow by
over 35%. As a result of regulation, some countries are mandated to have their data in physical data
centers. This has made public cloud services available in different physical locations. This has led to
different deployment models. We now have public cloud, private cloud, and multi-cloud.

2.Great Convergence of Data Analytics and AI/ML

In the past, companies had data lakes that served artificial intelligence and data warehouses that
supported analytics. In essence, the data pipelines are duplicated. This adds to the complexity and comes
with additional cost. The additional cost is not necessary because the data is in duplicate places, to begin
with. Research conducted by A16z shows that some experts believe data warehouses and data lakes
would converge. This would result in simplified technology and vendor landscape.

For technology companies who have complex data needs, their data needs to serve two purposes:

  1. Enable better decision making by making use of the data,
  2. Build systems that support data intelligence and apply the intelligence to applications.

3.From IT-Centric Data Workflows to Self-Service Analytics

Change in delivery method from the on-demand manual method to the self-served analytics method. Such
a platform can fulfill a large majority of the needs of business users. The overflow can be defined based
on the current data assets. Moreover, IT would still govern the datasets to prevent misuse and excessive
cost. The new process of data delivery would be optimized as a result. It would have a balance between
efficiency and cost.

4.Manual Analytics & Insight to Augmented Analytics

The conventional approach uses visual-based data discovery to let business analysts and users get
value. In the case of complex data, users should focus on exploring their hypotheses. They would manually
test different combinations of data for an accurate conclusion. This is prone to human error and bias.

A new era of data and analytics has emerged. This uses machine learning to help human users to uncover
hidden insights. The highlight of this method is that it applies algorithms to the data and is more effective.
It is less likely to miss essential insights in the data.

5.Convergence of Delayed Insights and Real-time Analytics

Before this time, a refresh of data comes in batches. Today, data needs to be timely. It is important to
detect and take immediate action when an incident occurs. This is a critical factor in the success of a
business.

Furthermore, consumers are demanding a more engaging user experience. To achieve this, applications
and businesses need to provide responses in real-time. It is difficult to identify anomalies without
historical data. This has driven the trend of convergence of different types of data.

In summary, we see a trend that favors the democratization of analytics. This would empower businesses
to get better value and actionable insights from their data.

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