As a data analytics researcher, I know that implementing real-time analytics is a huge task for most enterprises, especially for those dealing with big data. It’s worth the work, though; real-time analytics can help your enterprise reach insight faster and handle streaming data sources that give your analysis more depth. I’ve identified four key challenges and key tactics to help you overcome them.
Challenge #1: A vague definition of real time
In our data consulting practice, our clients have different interpretations result in inconsistent requirements. Imagine that the C-suite has opted to adopt real-time analytics but the management team understands the term in a different way and has different expectations. Will such a project be successful? Probably not. A vague definition means uncertainty about possible use cases and which business tasks (both current and future) can be solved.
In our data consulting practice, our clients have different interpretations of the term real time. In the context of analytics, some believe real time means getting instantaneous insights and others are fine waiting several minutes between data collection and the analytics system’s response.
Key takeaway: You must invest significant time and effort to gather detailed requirements from all stakeholders. At the end of this stage, your team must unanimously agree on what real time means, what data you need in real time, and what data sources you should use.
Challenge #2: An irrelevant architecture
Once you’ve nailed down the meaning of real time and clearly formulated the requirements for real-time analytics, it’s time to proceed with the architecture design. Your architecture will need the ability to process your data at high speed. However, the processing-speed requirements can vary from milliseconds to minutes, depending on the data source and application.
Your architecture should also be able to deal with spikes in data volume and be able to scale up as your data grows. Certainly, you don’t want to find out that the architecture that seemed perfect a year or two ago is not able to process your data volume if it doubles.
Companies that plan to adopt real-time analytics often lose sight of offline analytics, but you need both real-time and offline analytics to get insights. For example, sending instant alerts is a great application for real-time analytics; identifying models and patterns with machine learning is a time-consuming process not suitable for real-time processing.
Running real-time analytics and offline analytics on the same data may create conflicts for computing resources and hinder performance. Your architecture should be designed to resolve such conflicts.