Another issue to consider is that analytics projects can fail if proper goals aren’t defined and measured. When companies don’t align on what real-time analytics means for their company and how they plan to leverage its insights, they can unknowingly put time and money at risk. It’s important to define measurable objectives, such as improving data accuracy percentage in a given period of time or reducing data error rates, to guide data collection and analysis. This approach results in more actionable france rcs data takeaways from the insights real-time data provides.
Additionally, some organizations disregard the importance of data quality and look only at speed. Unreliable or outdated data results in poor outcomes and customer experiences. The quality of the data fed into a system is equally important as the real-time aspect. Inaccurate or unclean data can lead to business decisions that potentially hurt an organization’s reputation and ROI. Data validation and audits should be prioritized for organizations looking to deliver premium customer experiences.
Real-time analysis requires processing vast amounts of data quickly, which is expensive, and calculations can take too long to query and aggregate large columns of data. Plus, integrating data from diverse sources, especially when dealing with multiple data types ranging from structured to unstructured, is a significant barrier due to the latency and costs of setting up and performing complex extract, transform, and load (ETL) actions.