What You Need to Know About Data Analytics and Its Scope In 2023

What you need to know about data analytics and its future scope in the year 2023

Living in the 21^st century, almost every one of you might have come across the word data analytics. Data analytics and its scope in 2023 are remarkable and it is one of the most buzzing technologies around the world. In this article, we will help you understand things in cloud-based analytics and data analytics in 2023.

Data isn't very useful on its own. Data only becomes useful when it is comprehended and infused into application experiences. The desire to put data to use has fuelled a surge in cloud-based analytics. Though the cloud currently accounts for a small portion of IT spending—roughly 6% according to IDC in 2020—all of the momenta is shifting away from on-premises, legacy business intelligence tools and toward more modern, cloud-native options such as Amazon Redshift, Google BigQuery, Snowflake or Databricks. The popularity of combining data and cloud is reflected in Snowflake's meteoric rise up the DB-Engines database popularity rankings, from 170 in November 2016 to 11 in January 2023.

Some of Snowflake's success can be attributed to its performance, scalability, and separation of storage and computing, combined with other advantages. However, cloud computing may provide an even greater benefit. Snowflake was created in the cloud and provides a natural path for businesses looking to migrate to the cloud. Yes, the same cloud is propelling new databases ahead of legacy alternatives. In 2023, the same cloud promises to continue to upend the data world.

All cloud, all the time?

David Linthicum's predicted that "2023 could be the year of public cloud repatriation," we do agree that we shouldn't fall in love with technology and treat every business problem as a nail. The cloud solves many problems, but not all of them. However, the cloud is essential in areas involving advanced data-driven applications, as Linthicum acknowledges: "When advanced IT services are involved (AI, massive scaling, deep analytics, quantum computing, etc.), public clouds typically are more economical."

Not only is it more cost-effective, but it is also more practical.

AWS executive Matt Wood made this case to me years ago, and it's just as compelling today as it was then. "Those who buy expensive infrastructure going out discovered that the problem scope and domain shift," he said. "By the time they get around to answering the original question, the business has moved on." "If you drop a chunk of change on the data centre that is frozen in time," he continued, the questions you can ask of your data are stuck in a time warp.

Even in difficult economic times, the wrong way to think about the cloud is through the lens of cost. Elastic infrastructure breeds flexibility in data interpretation. Dollars from logic, rather than dollars and cents. That refers to cloud-based analytics tools.

Companies appear to recognize this. Snowflake CFO Mike Scarpelli discussed competitive dynamics in the data warehousing market at a recent analyst conference. "We will never compete with Teradata [an incumbent data analytics company founded in the on-premises software era]. Teradata is never against a customer's decision to go off-premises. They have decided to depart." Where should an enterprise look if it is already looking to the cloud as part of a digital transformation exercise? "When we compete for an on-premises migration, it is always [against] Google, Microsoft, [and] AWS [but] AWS tends to partner with us more out of the gate," Scarpelli says.

Data democratisation

Another reason for cloud success, or it can be, is simplicity. Of course, the cloud is not inherently more user-friendly, but many cloud systems have emphasised a SaaS approach that prioritises user experience. Consider the following Reddit post, which describes a user's experience with Snowflake: "If you need a PhD in physics to use your SaaS tool, your tool is useless. The C-suite loves it, MySQL users love it, and the only people struggling to win over are nerdy engineers who believe that they could do it all themselves and that everyone in the world would eventually learn PySpark."