It’s time to welcome AI analytics and retire big data dashboards.
While we’re all accustomed to traditional BI tools, AI analytics can uncover hidden opportunities within data, that humans simply cannot. General business intelligence tools cannot keep up with the pace of modern-day businesses; the information they provide is too late. Let’s understand the shortcomings of big data dashboards to access why AI analytics can provide more accurate real-time.
Demerits Of Big Data Dashboards
Dashboards Don’t Show Actionable Visualizations
The bar graphs and charts will show you a picture of what is working well and what is not but in a very generalized way. But these visualizations don’t tell why those metrics hold a specific value and why it is necessary to execute the fast reaction to control the business problem. The quick reaction of solving the problem and leveraging the opportunity where business intelligence integrates with business strategy. But with general indications like the kind the dashboard gives, it’s difficult.
Big Data Dashboards Ignore Isolated But Significant Data Irregularities
Oftentimes, business scenarios that affect only one component of businesses go ignored amidst all the statistics and metrics. But at what cost? A small dip may represent a large, missed opportunity. For example, a small spike in online orders from a specific demographic might indicate a small-scale marketing success that earned more revenue. Another hitch in the system is the dashboard’s nature of summarizing which blocks real-time insights. In order to find the reason for the metrics, one needs to group and correlate multiple anomalies with a combination of event data. But this cannot be possible without analyzing data to the most intricate level. For this type of intricacies, a solution that scales millions of individual metrics that’s being collected. However, traditional dashboards can’t keep with the massive amounts of data that constantly pour in.
One Can’t Be Dependent On Big Data Dashboards Alone
On their own, big data dashboards cannot detect anything. This is the reason data analysts set up manual monitoring sessions, called static thresholds. This static threshold generates a lot of alerts on which data analysts have to spend the majority of their time figuring out what the cause is. While doing this, data analysts miss out on focusing on other important things.
The Insights Are Usually Too Late
Big data dashboards suffer from inherent business insight latency. Even if there is human monitoring the system, the dashboards will still miss out on real-time actionable insights, or be too late. In bigger business scenarios, this lag can cost more to the business than just time, money, and the reputation of the company is on the line.
The Advantages Of AI Analytics
AI analytics groups and correlates several anomalies to bring the most important insights first. AI analytics systems can monitor millions of metrics at an intricate level, giving the user the details and the method to identify business incidents that matter, even the subtle ones that big data dashboards would have missed.
Automated anomaly detection, a feature of artificial intelligence analytics, will give talented data professionals more time to focus on something that’s more important to push the business forward. Artificial intelligence-based analytics will constantly be analyzing the business data, detecting errors, and identifying patterns.
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