10 Vital Trends to Consider for Data Cloud Analytics

4 months ago 3123
Sourced from InfoWorld.

2021 has been the year of the Data Cloud. Snowflake estimates that there are still hundreds of millions of data sets isolated in cloud data storage and on-premises data centres globally.

The Data Cloud eliminates these silos, allowing a business to seamlessly unify, analyse, and share its data to reach deeper insights and even open new revenue streams.

As this shift to the Data Cloud takes hold, what data analytics trends will emerge?

Snowflake’s team of data analytics experts has 10 predictions about what to expect in areas including data science, data engineering, data governance, and data sharing.

Below are 10 vital trends that will help you start thinking about how your organisation can unlock the value of data faster and more efficiently:

1. The Value of Data will Rise Exponentially

Data—both internal and third-party—has become critical to create insights that help ensure business survival.

The value of data has become even more important to business success. The key to unlocking data’s value will be the ability to easily unify, integrate, analyse, and share data.

2. The Rise of the Data Cloud

Many IT leaders point to data silos as their biggest challenge to realising data’s value. The Data Cloud is one unified environment where teams can collaborate on all sorts of data, bringing many different workloads together in a secure, governed manner.

The need to copy or move data will become a thing of the past, helping to eliminate many security and governance issues.

3. Modernisation Beyond the Data Warehouse

Achieving a complete view of data requires modernising not just your technology, but also your processes and workforce.

Leading companies will build new approaches to data by creating new roles and responsibilities, enabling organisations to be agile for changing requirements.

4.  Governance Continues to Be a Top Concern

As more data is shared and businesses face more privacy regulations, governance must be pervasive.

Governance involves knowing your data (understanding and classifying data across your entire ecosystem), unifying security and governance (simplifying governance across workloads with centralised controls), and controlling your data (implementing flexible governance and security policies that don’t hinder innovation).

5.  So Long, Data lakes/Warehouses

Gone are the days when organisations needed to figure out locations and data types for data lakes versus data warehouses, and how to integrate different solutions and systems.

Today, a modern data approach enabled by the Data Cloud takes a holistic approach to data, enabling companies to focus on the value of data instead of the logistics of its storage and usage.

6.  Collaboration Goes Mainstream

It’s very difficult, if not impossible, for disparate teams to collaborate on data. Organisations often have to create copies of databases, or offload data sets, and send them outside of the organisation.

This creates operational overhead and a massive governance problem, because the second you ship data to a third party, you lose control of it.

Companies are increasingly using Snowflake’s Data Cloud and Snowflake Data Marketplace to collaborate on data in virtually any format, at a near-unlimited scale, without copying or moving it.

7. Analytics Democratisation Becomes Attainable

Today, organisations are empowering every employee in every department with access to self-service analytics. This is being enabled by easy and governed access to increase focus on improving data literacy.

Ready-to-use data along with scalable platforms that can handle the workloads, easy-to-use visual analytics tools that can answer questions in near real-time, and an increased focus on improving data literacy.

8. Data Processing is Available to All

Multiple users can access the same platform to process data for different purposes, including data preparation, data augmentation, feature engineering, and machine learning. As engineers use a more diverse set of languages and tools, data architectures are becoming more complex.

The Data Cloud reduces complexity, enabling all users to process data differently according to their needs.

9. Every App Becomes a Data App

Today’s applications ingest, process, and encode massive amounts of data, no matter which category they are in.

The challenge is to make sure data infrastructures can handle the massive data loads and user expectations for rapid response times. The Data Cloud can support data-intensive applications without the cost and complexity of legacy data systems.

10. A Single Platform for Data Becomes the Solution

Companies that want to bring the capabilities of advanced analytics to their employees can take a tool-based approach and perform complex integrations, but this approach becomes labour-intensive and costly.

A single platform enables multiple workloads in one place—including data engineering, data science, data sharing, data applications, data warehousing, and data lakes—helping to unlock value faster and easier.

By Julien Alteirac, Regional Vice President Northern Europe & Emerging Markets at Snowflake.

Edited by Luis Monzon
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