Importance of Big Data Analytics

In 2018, it was estimated that 2.5 quintillion bytes of data were created globally each day, and that number has continued to grow since. It’s clear that organizations are dealing with data at an unprecedented level in business today. Importance of Big Data Analytics is our topic now.

Big data analytics refers to the strategy and tools used to collect, process, and derive insight from large-volume data sets. Everything from internet-of-things (IoT) sensors to financial records can provide decision-ready information for your business. 

Key Benefits of Big Data Analytics

Big data analytics plays a crucial role in improving decision-making processes, particularly in breaking down silos and centralizing information. The actual value lies in consolidating data— big or small—into a single, unified system. By eliminating data silos and creating a centralized system for your data, organizations can reach next-level success. 

What are the benefits of big data analytics?

Improved Decision Making

Accessing all relevant data from a single source enables more informed and accurate decisions. Information extracted from myriad sources, all in one place, is accessible in real time, making it more straightforward to make the best decisions for your organization. 

Competitive Advantage

Having all this information at your fingertips can help you understand trends and provide strategic foresight for your business, which can help you build a competitive advantage in your industry. 

Enhanced Customer Experience

Unified data leads to more consistent and efficient service delivery, improving customer satisfaction, and reporting from a single source rather than a fractured view of your customer.

Increased Efficiency and Cost Reduction

Studies have shown that centralizing and analyzing data leads to significant operational efficiencies and cost savings. Avoiding data duplication also saves costs and creates a single source of truth for you to work with. 

Tools and Technologies for Big Data Analytics

Below are some tools that can help centralize your data and lead to better insight. dbSeer is happy to guide your organization in its digital transformation journey and help you pick the best fit. 

Data Lake versus Data Warehouse 

Big data is collected from a range of sources and can be stored in a data lake or data warehouse prior to being processed. It is used to extract insight and be useful for analysis. According to this explainer from AWS, organizations often use both a data lake and a data warehouse as they serve different needs and provide different benefits.

How might they serve your need for big data analytics?

Using a Data Lake for Big Data Analytics

Data lakes are designed to store structured or unstructured data at scale and provide flexibility to run different types of analysis. Platforms like Databricks and Spark allow organizations to efficiently process and analyze large-scale data in your data lake. 

• Databricks and Apache Spark: Databricks is a commercial, open, unified analytics platform that revolutionizes data management. It fosters a collaborative environment for users to build, test, and deploy machine learning and analytics applications. Its scalable open-source Apache Spark system is optimized for processing large datasets to better make sense of your data in a streamlined fashion. It can be easily integrated into several BI tools, helping elevate your business strategy. Together, Databricks offers a commercial platform that enhances Spark’s capabilities with added optimizations and ease of use, making big data analytics a breeze.

Using a Data Warehouse for Big Data Analytics

Data warehouses deliver high-speed, optimized querying for structured data. Tools like Snowflake and Redshift optimize structured data efficiently in terms of time and cost. These solutions excel at BI and task reporting for your organization. 

• ​​Snowflake: Snowflake is a data warehousing platform designed for scalability and performance. It is a flexible and cost-effective approach to data management. This allows your business to securely and efficiently store, analyze, and share data, all in the cloud. Unlike Spark and Databricks, which focus on data lakes, Snowflake operates as a columnar data warehouse, enabling optimized read performance for analytics. 

• Amazon Redshift: Amazon Redshift is another robust columnar database optimized for data warehousing and analytics. It is well-suited for heavy workloads and provides fast query performance across large datasets.

Challenges in Implementing Big Data Analytics

When implementing big data analytics within your organization, you may need to be mindful of two challenges in your digital transformation. 

• Skill Gaps and Talent Shortage
  • As technology continues to change, you may find yourself short on individuals within your organization who have the knowledge to understand how to implement these changes.
    • Building a data culture within your organization will help address this issue, but leaning on dbSeer to guide you will help. With 20+ years of expertise in the field, dbSeer will happily meet you where you need us. 

• Ongoing Cost of Building and Maintaining Your Data Architecture 
  • Once you implement the necessary big data architecture, this may require substantial investment, both financially and in terms of time.
    • Everything from ongoing maintenance to scalability costs may come as hidden fees and unexpected, unwanted surprises for your business. Working with dbSeer can help mitigate that. We never want to surprise you with hidden fees and want to ensure your business success. 

At dbSeer, we understand the importance of scaling your business and making sure your data moves with you. Let us be your data experts. We want to ensure you meet the needs of the 21st Century. Reach out today so we can help you take the first steps.