Home » 3 Trends That Are Making Real-Time Analytics More Challenging

3 Trends That Are Making Real-Time Analytics More Challenging

Today is the age of big data and the Internet of Things (IoT), and more than ever, real-time analytics play a huge role in responsive decision-making by organizations and enterprises all over the world. With this trend has come the development of equally fast and reliable data management solutions that can keep up with such demanding requirements from various industries and fields of business.

 The key to harnessing real-time data these days is real-time data replication for analytics and integration. High-tech data replication software capture information at their various sources and touchpoints, and then copies them into a separate business intelligence (BI) database through the technology of log-based change data capture or CDC. This technique is one of the most advantageous to use given the volume and speed of information in big data and the IoT.

Information in the BI database is constantly updated through log changes, and users are assured that they are working with the latest or near real-time data. This way, the analytics software performs more accurately, and business decisions can be made that are more relevant and responsive to current market situations and trends.

How Data Is Changing

Data integration is the biggest challenge to real-time data analytics, given the vast and varied sources of data in the age of big data and the IoT. Traditional methods of data warehousing and batch processing are becoming outdated, as the world of business and commerce is pretty much 24-7 these days. There is less and less opportunity to pause and process data, while there is more and more demand for analytical insight to keep up with continuous operations.

The nature of big data is evolving, and at present these are the top 3 trends that are demanding more innovative real-time analytics solutions and techniques:

Ever-Increasing Numbers of Data Sources

 Not too long ago, almost all data warehouses comprised information from on-premise ERP (enterprise resource planning) systems, websites, and legacy applications. While these are still being used, they are slowly being heavily enhanced by cloud ERP applications as well as data centers located in various geographical locations all around the globe.

Immense Data Volumes

 The amount of data that are being generated from everywhere (quite literally) is astounding, and it continues to grow by leaps and bounds. Imagine that everything from cloud computing to mobile devices and the whole IoT generate loads and loads of data, almost virtually every second. Here are some amazing ways to look at it—Google maintains 16 data centers that comprise around a million servers, consuming .01% of the earth’s entire energy supply. Or that data nomenclature is now at the “exabyte” level, which is equivalent to data contained in around 250 million DVDs.

More Diverse Data Types and Schemas

Data warehouse architects, designers, and developers used to only focus on migrating OLTP or short online transaction processing data from relational databases and flat files, into similar relational data warehouses with dimensional schemas. These days, data warehouse designers need to work with HDFS (Hadoop Distributed File Systems) and other similar file systems, as well as columnar databases, NoSQL databases, machine data, and others. These data types are structured, semi-structured, or unstructured.

Investing in Real-Time Analytics

In today’s fast-paced, technology-based world, it is no longer a question of whether a business or enterprise should look into real-time analytics and data replication solutions—it is an imperative if such business wants to survive, adapt, and make the right decisions at the right moment. It is important to work with the latest technologies available, and to partner with the right vendor that can come up with solutions that are tailor-fit to an organization’s particular needs and demands in terms of data management and analytics.

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