Real-time data has become a corporate buzzword. Every organization wants it, every BI vendor is selling it, and every executive assumes it is necessary. However, the reality is that most companies chasing real-time analytics have not clearly defined what problem they are solving.
Real-time data can be transformative in the right contexts. However, for many businesses, it is an expensive and overly complex solution that does not significantly improve decision-making. Instead, it often introduces unnecessary technical challenges and operational inefficiencies.
Before investing heavily in real-time infrastructure, it is essential to assess when real-time data truly adds value and when a well-structured batch processing strategy is the better choice.
Understanding the Obsession with Real-Time Data
Most business leaders will immediately say yes when asked if they want real-time data. The assumption is that faster data leads to better decisions. However, this assumption often fails to consider critical factors:
- Not all decisions require immediate updates. Most strategic business decisions operate on daily, weekly, or monthly timelines rather than in real-time.
- Real-time data processing is expensive. Moving from batch processing to a real-time architecture increases compute costs, introduces greater complexity, and requires ongoing system monitoring.
- Speed does not fix poor data quality. If data is incomplete, inconsistent, or unreliable, real-time processing only delivers flawed insights faster.
Batch Processing is the Better Approach
For most business functions, batch processing remains the more practical and cost-effective solution.
- Financial reporting: does not require second by second updates. Profit and loss statements, budgeting, and forecasting operate on monthly and quarterly cadences.
- Marketing analytics: do not need continuous updates. Campaign performance, customer engagement, and conversion rates can be evaluated on a daily or weekly basis.
- Supply chain forecasting: is driven by historical trends rather than momentary fluctuations. Real-time insights do not necessarily improve long-term demand planning.
Batch processing is simpler, more reliable, and more cost-effective. It allows organizations to focus on data quality, governance, and accuracy before prioritizing speed.
Real-Time Data is Not the Solution to a Poor Data Strategy
The demand for real-time data often arises from a lack of a clear data strategy. Organizations assume that real-time analytics will enhance decision-making when the more significant challenge is ensuring data accuracy, accessibility, and alignment with business objectives.
Before investing in real-time capabilities, organizations should evaluate:
- What decisions are we trying to improve?
- How often do we need updated data?
- Is the cost and complexity of real-time processing justified?
For organizations looking to optimize their data architecture and decision-making processes, now is the time to take a strategic approach.
Contact us to learn how Upright Analytics can help your organization maximize data efficiency.