Real-time data has become increasingly important for making informed decisions on a moment-to-moment basis. Such real-time data integration has added a new dimension to business intelligence (BI), yet, it presents several challenges that businesses must overcome to make real-time BI effective.
Challenges such as managing the volume and velocity of data, ensuring data accuracy, and dealing with data structure and format inconsistencies can hinder the ability of organisations to fully leverage the benefits of real-time data.
In this blog post, we examine the challenges associated with real-time data integration and explore effective strategies for addressing them.
The challenges of integrating real-time data sources for BI ?
1. The broadening definition of data
The traditional definition of data used to be limited to transactional data such as orders, users, and products. This type of data was typically stored in tables within relational databases, and the focus of data integration was on moving and transforming data between different systems.
However, the definition of data has significantly expanded in recent years with the emergence of event data. Event data captures not only what is, but also what happens, such as website page views, hardware errors, and much more. This type of data tends to be much larger in volume than transactional data, which can complicate the traditional approach to data integration.
In addition to the emergence of event data, the variety of specialised data systems has also increased in recent years. These systems are used for a wide range of use cases such as online storage, batch processing, and search. This presents a dual challenge for organisations as they need to get more types of data into more systems in real-time. The use of legacy extract, transform, load (ETL) tools is no longer sufficient, as organisations require up-to-the-minute insights and event-driven programming to ensure the quality, relevance, and accuracy of their data.
As a result, the definition of data has broadened to include real-time data integration and insights, streaming data pipelines, and the ability to transform data streams on the fly from any system, application, or device.
The challenge for businesses is to ensure they have a single source of truth for all their data, with pre-built connectors and streaming data governance that can seamlessly ingest, aggregate, and transform data streams at any scale.
2. Data volume and velocity
Data Volume and Velocity are two significant challenges organisations face when integrating real-time data sources with their BI platform. Real-time data streams generate a massive volume of data in a short amount of time, which can quickly overwhelm the BI platform’s processing power and memory.
As the velocity of the data is exceptionally high, the BI platform must be equipped to handle such large volumes of data in real-time. Otherwise, the data processing time will be slowed down, leading to delays in decision-making and reduced efficiency.
Organisations need to ensure that their BI platform can handle the volume and velocity of real-time data sources by investing in high-performance computing infrastructure and using data compression techniques to reduce the data size. This will enable the BI platform to process real-time data streams quickly and efficiently, enabling organisations to make informed decisions in real-time.
3. Data Quality
Real-time data streams are prone to errors, inconsistencies, and inaccuracies due to various factors, such as network delays, data transmission errors, and system failures. These issues can significantly impact the quality of the data and render it useless for decision-making purposes. Therefore, the BI platform must be equipped to handle and correct these issues in real-time to ensure the accuracy and reliability of the data.
4. Data integration
Real-time data sources come in various formats and structures, which can make it challenging to integrate them with the BI platform.
Each real-time data source may have a unique structure, schema, and data format, making it difficult to combine data from different sources into a single view. To provide a comprehensive view of the data, the data platform must be able to handle different data sources and formats.
5. Skills shortage
Finding skilled personnel with the expertise to develop and maintain real-time data pipelines can be a significant challenge.
The skills required for real-time data integration are highly specialised and are in short supply in the job market. This shortage of skilled personnel can lead to longer development times, slower deployment, and higher costs for businesses.
However, by investing in training and development programs or partnering with technology vendors, businesses can address this challenge.
Real-time data integration presents several challenges. However, with the growing demand for real-time insights, it has become more critical than ever for businesses to overcome these challenges and integrate real-time data into their existing systems seamlessly. To address this need, we have developed a software solution that packages a streamlined approach for real-time data integration.
By leveraging the best practices that we have developed, businesses can integrate real-time data seamlessly with minimal disruption, and gain valuable insights that can drive better business decisions.
Let’s take a closer look.
Digazu for seamless integration
Digazu is a software solution that packages the required technologies for stream integration in a low-code approach. With Digazu, non-experts can easily create real-time data pipelines and integrate them with existing BI tools without impacting business processes that do not require real-time data.
With Digazu, not only will you be able to maintain well-functioning BI tools as well as BI chains, hence minimising disruption but you will also be able to upgrade your data integration with real-time capabilities while ensuring seamless integration with your current systems and BI environments.
Real-time data integration for business intelligence presents many challenges. However, with the right approach, these challenges can be overcome. It is essential to select the right technology and infrastructure to manage and analyse the data effectively.