Setting Up Data Analytics Platforms On Cloud – Some Important Considerations To Make

Enterprise database admins and decision-makers are still confused when it comes to making use of the huge amount of data flowing in through various sources for decision making. It is a challenging task for the database administrators to access and analyze the data for any good.

There are cloud data warehouses, which may help centralize the information from various sources to conduct analytics, predictive modeling, and forecasting, along with other advanced use cases of machine learning to gain some actionable insights.

Cloud is a high-performing and scalable platform that will help organizations achieve better data usage and faster time to get insights. However, simply moving your data onto the cloud may not make it fully actionable.

One thing you need to clearly understand is that where you store your data may not address the issue of how it is being used. About 90% of the data management professionals consider it is challenging to make the data available in the given format to be used for analytics.

While considering a cloud data warehouse for storing your data, one may need to migrate the data sources at the first point. After this move, you have to figure out how you can use the data for analytics and reporting to get some valuable insight from it.

In order to construct a data analytics platform for analytics on the cloud, you have to first set up and design a solid data infrastructure and a cloud-based solution to run it successfully. This article will discuss a few important do’s and don’ts while building a data analytics platform on the cloud.

Do consider data lakes and CDW as a data source

The barrier between a data lake and cloud data warehouses is now being narrowed as these technologies appeal more and more to the data professionals who plan to store their data at a centralized location.

A data lake is not a replacement for the cloud data warehouse. These are actually supplementing technologies, which serve various use cases by offering some similar capacities.

Most enterprises, which maintain a data lake, may also be managing a data warehouse and it. Going a step ahead, many of the cloud service providers now combine data warehousing technologies and data lakes into a single platform to enable better analytics.

You may call it a data warehouse or a data Lake, but the data is centralized and made useful, which is the key. Further, breaking down the data silos effectively will help you to easily access the data you collect at any time and keep it synchronized and up to date for your needs. For better consultation regarding cloud data warehousing, you can approach providers like RemoteDBA.com.

Do not skip data transformation

Loading the data simply may not be enough in order to gain insights for analytics. Data needed to be transformed by joining together data from different sources to produce any valuable and reliable analytics.

This has to be prepared from the raw data, which needs to be in a normalized form. The traditional processes like ETL and manual coding usage, along with some failure to plan test data before the ETL, may cause some errors like duplicates, term missing, and other such issues.

The modern ETL for ELT tools will help you reduce the need for any manual coding and help cut down any errors. Data transformation is a critical step for using the data in advance use cases and for reporting.

Data transformation will help to load data into the cloud warehouses and help manipulate it into the required format for analytics and business intelligence.

Do not migrate the entire data at once

It may be tempting for the database manages to extract the data and load everything into cloud storage once you are into it.

This may be a wrong approach; instead, you need to identify smaller use cases that feature clear a matrix in order to familiarize yourself and learn cloud data management.

In order to gain some support for cloud data efforts, you have to prove its business value to the stakeholders and decision-makers. You can start with a basic analytic use case and figure out the matrix, to which the data can shed some light.

For this, you may choose data from various sources with well-defined KPIs like the click-through rate of an e-commerce website. Here, you will be able to easily provide data from both the mail marketing system and e-com platform to the cloud IT solutions and show how quickly the reports can be generated.

Do consider long term while getting a software

As we learned by now, the cloud can offer scalability and flexibility in terms of data management. The software you may choose for this purpose may leverage both of these functional attributes.

You may choose solutions that can grow further as your business grow and not the ones that may outgrow after only a certain time. First, determine if the vendors can also grow with you or can offer you scalable solutions throughout the cloud data journey.

As you progress from smaller projects to complex ones, the cloud vendor also should grow along with you to ensure adequate support. This approach will help you to be more comfortable with the solutions you have.

When you are ready to explore more and more robust features, you should have appropriate tools you trust without going back and starting from scratch.

Do not undervalue the security requirements

Security on the cloud is an increasingly important need for all types of enterprises. You need to ensure that the data analytics platform of your choice and the data analysis architecture you construct meets all necessary requirements in terms of security for your business as well as industry standards.

While you are searching for a cloud model, you also need to understand that security is the user’s primary responsibility, whichever model you entail. 

Cloud data transformation will certainly enable you to unlock more and more insights for your organization for data-driven decision-making. However, to achieve this goal, first, you have to assess your organization’s proficiency in terms of cloud-based data management.

Suppose you think that you and your team are ready to go forward for scalable infrastructure and construct a future-proof platform for analytics next. In that case, you can start exploring the tools, compare the features, and identify the best ones to start with.

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