Project Metamorphosis: Wir präsentieren die Event-Streaming-Plattform der nächsten GenerationMehr Erfahren

Big Data Analytics - The Complete Guide

Big data analytics refers to extremely large, complex sets of data that are analyzed for business insights, operational efficiency, and patterns to uncover business opportunities and mitigate risks. Learn how big data works with examples, use cases, and the best technologies for modern organizations.

Big Data Analytics Explained

Firstly, What is Big Data?

The term “big data” refers to complex, fast, and large data that is very difficult to process using traditional methods.

While the term "big data" has been around for a long time and had its peak in 2001, when Doug Laney articulated the definition as the 3 Vs of big data: volume, velocity and variety.

Data Management Explained

Data management is the process of collecting big data from various sources and includes storing, processing, validating, securing, processing, cleansing the data. Data management is table stakes for all companies benefiting from big data analytics and insights.

An effective data management process is important because it ensures that the information is accurate, reliable and as up-to-date as possible for everyone who needs to access it for analysis, reporting and making business decisions. Not only is data management include new processes, it also involves understanding and updating existing architectures, policies and best practices and platforms.

Ensuring that data management is done correctly becomes of utmost importance as big data is every company’s capital. The users of the data has expectations on accuracy, reliability and truth and this has impact out on decision makers, executives and shareholders of the company.

Data Management Benefits & Use Cases

Importance of Data Management

If you look at all the successful companies in the world, you'll notice they all continuously collect and analyze big data to increase their value proposition, understand customers, and continuously improve operations and efficiency.

There are an infinite number of big data use cases and increasingly, data provides the competitive advantages and value for these companies. Big data analytics allows for large data sets to be sampled, providing significantly more accurate results, allowing organizations to unify data for deep business insights, mitigate risks, and make informed decisions at large scale.

Benefits of Data Management & Analytics

The data created in an organization is valuable, and by managing big data correctly, numerous competitive advantages arise. Here are the most common benefits:

  • Accessibility: Access control of users privileges enables data to be access by the right people, increasing ease of use. New data sources can be created, updated and accessed with ease which ensures that all levels and departments can get what they need while maintaining data privacy and compliance.
  • Cost Efficiency: When different parts of the organization are gathering data for analysis, it is likely duplicate work is involved to collect and correlate the data. Having a proper data management process will also reduce the storage of duplicate data and compute costs of analysis.
  • Minimize Security Risks: Without a comprehensive enterprise data management process, ad hoc data collection and analysis on local or cloud machines presents several security risks on collection, storage, and access. Best practices in data management protects the organization from malicious attacks or data theft.
  • Data Compliance: In a world where most organizations are subject to data compliance rules like the protection of personal information (GDPR, CCPA), payment information (PCI), health information (HIPAA), data management helps the organization to be compliant. Generate archives and schedule removal of data based on compliance rules.
  • Data Accuracy and Reduced Data Loss: Data management helps maintain the accuracy, reliability of the ever increasing amount of data being stored and processed. Without a comprehensive data management plan, it may be too late to recognize that there was data loss that was of importance of the company.
  • Multi-Cloud Strategy: Data management helps utilize a hybrid approach to storage across on-premise servers as well as multiple clouds. This helps balance goals of high availability, redundancy, disaster recovery and cost savings.
  • Better Products and Services: Having access to better quality, recent, and accurate data helps the company make more timely, accurate decisions. Data management enables team members to perform data analysis and make data driven decisions which allows the business to continue to improve and offer more relevant product, services, and customer success.

Challenges of Data Management

Die meisten Organisationen stehen vor einer Flut an Daten, die aus neuen Applikationen, neuen Geschäftschancen, IoT und mehr stammen. Die ideale Architektur, die die meisten sich vorstellen, ist ein sauberes, optimiertes System, das es Unternehmen ermöglicht, aus all diesen Daten Kapital zu schlagen.

However, dealing with the sheer volume of data that arrives in various formats, from numerous sources, and as structured/unstructured data.

As this data continues to grow in volume and complexity, complications often arise. As such, it helps to have a solid plan to focus on the data that’s needed, how it’ll be used, and the analytics that will be performed for maximum benefit.

Steps to a Successful Data Management Strategy

  1. Assess business needs: Understanding the types of information, decisions, and analyses your business can benefit from will lay the foundation of your data strategy.
  2. Outline data management objectives: how to aggregate, organize, store, share, and analyze data.
  3. Data Aggregation: Aggregating data across all sources is one of the hardships, because data resides in all types of servers, devices, data lakes, data warehouses, and in various locations and formats. For proper big data management, only does past and present data need to be collected, real-time data processing is required to connect the dots. You’ll also need to consider where your data resides, structure, format, and how massive this data is.
  4. Data Storage: maintaining the quality, accessibility, and integrity of data while maintaining security and compliance.
  5. Analyze and Interpret Data: Once all data is collected and analyzed, what goals do you aim to achieve? Is it better customer interactions, predictive analytics, or better operating efficiency?

Real-Time Data Management – The Key to Success

A major challenge in modern data management is the ability to streamline all data types, from all sources and formats into a single pane. The ability to process and integrate data in real-time allows for digitalization, speedy time-to-market, quick innovation, and agile projects.

Real Time Businesses Rely On Real Time Data

A stock market is dynamic and changes rapidly. Same with shopping websites, ride share apps, weather reports, and Netflix recommendations. By utilizing data in storage along with real-time data integration, they revolutionize big data management in a world of distributed, ever changing data.

Combined with past data, this vast set of present, real-time data can help businesses

  • Improve their product with new features
  • Improve and personalize the customer journey
  • Improve the customer service and support experience
  • Improve performance and IT systems operations based on peak usage and off-peak usage.
  • Add insights to innovations in their machine learning and AI initiatives
  • Add operational efficiencies and better utilize resources
  • Increase the bottom line of the business
  • Accurate insights that are timely and reflect reality so that the executive team and team members can make better decisions.

How Real-Time Data Platforms Can Help

Confluent is a data streaming platform designed to integrate data from countless sources at scale, including traditional databases and modern, distributed architectures. Originally envisioned as a fast and scalable distributed messaging queue, it has rapidly expanded into a full-scale streaming platform, capable of not just collecting batches of data, but storage and real-time data aggregation, processing, and analytics.

See how you can start by downloading Confluent, the leading distribution of Apache Kafka and the most powerful enterprise event streaming platform in the industry, or learn more about real-time data streaming.

Gute Gründe für Confluent

Confluent is the only complete data management platform that seamlessly integrates 100+ data sources for real-time data management. Deploy anywhere with 24/7 platinum support.