Real Time Data and Analytics
The vast majority of Business Intelligence and Analytics solutions in place today operate on out-of-date, backwards looking, historical data.
When you are operating on strategic timeframes and asking long-term questions, e.g. about last quarter or last week's sales data, this is absolutely fine. The benefits of analysing up-to-the-minute data are minimal.
Why Real-Time Data Matters
There are many scenarios where businesses are looking to move towards much more real-time solutions:
- Using data for operational purposes, for instance guiding employees' "next best action" continually throughout the day
- Real-time monitoring of critical KPIs and indicators to identify problems and opportunities early
- Optimising or facilitating the customer experience in real time
- Security, compliance, regulatory or safety controls where the business is at risk due to slow data processing
In all of these situations, the value of data decays over time. The earlier we can get it into the hands of our employees, algorithms and customers the better - even down to millisecond granularity.
The Benefits of Real-Time Data & Analytics
By processing data in real time, businesses can dramatically improve their performance and bottom line:
- Leverage data for operational analytics to identify immediate steps to grow business and improve efficiency
- Create more proactive and personalized customer experiences
- Improve employee experience with "up to the minute" views and next-best-action recommendations
- Detect and respond to anomalies before they impact KPIs
- Increase market share and revenue while decreasing operational costs
The Challenges Associated With Real-Time Data
Moving from traditional Business Intelligence towards more real-time processing presents several technical challenges:
Current Infrastructure Limitations
- Most data and analytics systems are based on centralized data warehouses
- Infrequent batch ETL jobs loading data on an hourly or daily basis
- Consumption primarily through reports and dashboards accessed infrequently
Technical Complexities
- Data-intensive tasks requiring thousands of parallel event processing with low latency
- Need for exactly-once processing to prevent message loss or duplication
- Complex analytics across data streams, time windows, and historical data
- Handling edge cases like errors, anomalies, and late-arriving data
Evolution from Dashboards to Automation
Traditional Dashboard Limitations
Most analytics programs rely on reports and dashboards that:
- Require user login
- Update infrequently
- Involve manual interpretation and action
- Create delays between insight and action
Moving Toward Real-Time Automation
The gold standard should be real-time automated responses:
- Instant alerts when orders pass SLA
- Immediate warehouse updates for cancelled orders
- Real-time fraud detection before payment acceptance
- Dynamic pricing adjustments based on demand
Why Now?
Companies across industries are investing in becoming more data-driven, using their data more intelligently and in real-time to improve their customer experience and business efficiency. Well-trodden case studies include technology companies such as Google, Amazon, Uber or Netflix who use their vast quantities of data to offer amazing digital experiences. Companies that don't make this leap will likely fall behind in terms of customer experience and operational efficiency.
Getting Started
As this is a complex technology modernization journey, it's worthwhile starting early:
- Implement one or two streaming use cases
- Create a microservice to drive some real-time change
- Offer employees a taste of real-time data on the user experience
The benefits will immediately become apparent as new features hit production, allowing your organization to build momentum toward a more real-time, data-driven future.
