To help them make better decisions, managers and C-suite executives alike demand access to analytics data. With the use of business analytics, executives can turn mountains of operational, product, and customer data into insightful knowledge that facilitates quick decisions and profitable outcomes.
Though they have their drawbacks, traditional business intelligence and KPI dashboards have been widely used solutions. It takes a lot of work to create dashboards and management reports, and in order to show the data in a graphical or report style, one must be aware of what to search for.
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The Drawbacks of Conventional Dashboards
High-level summary data that only addresses a few of the company’s most important KPIs often makes up the information that surfaces. This is mostly due to the fact that KPI dashboards and BI systems are not designed to manage high numbers of metrics. Managers are thus making choices based on insufficient information.
These systems show historical data instead of real-time data, in addition to having insufficient depth and breadth of data. While this is adequate for tracking patterns over time (e.g., if sales are rising over time, how much is spent each month on cloud services, etc.), utilizing historical data eliminates the capacity to decide what to do and respond on current events. Furthermore, the absence of context and data linkages in high-level reports and dashboards makes them useless for tracing the origin of a problem.
To sum up, business dashboards serve a function in presenting high-level summary information, but they are not equipped to provide comprehensive, real-time information to enable immediate choices and actions.
Beyond Dashboards: AI-Driven Analytics Expand to Provide Detailed Information
An improvement over dashboards is AI-powered analytics, which allows for the scalability to handle all pertinent business data. This enables an organization to keep an eye on everything and see anything, particularly happenings they weren’t aware they needed to search for—the unknown unknowns.
AI-powered analytics absorb and evaluate enormous amounts of data in real-time through autonomous machine learning. One such technology is the Autonomous Business Monitoring platform, which offers businesses “a data analyst in the cloud.” Let’s examine the many advantages of constantly monitoring minute changes in the company as they happen with analytics driven by AI.
Use Real-Time Data to Quickly Make Decisions and Take Action
Because data is entering the system in real-time from many sources throughout the company, AI-powered analytics can process it. By processing the data and searching for anomalies, machine learning algorithms can identify problems early on rather than years after.
If necessary, it enables firms to promptly adjust their procedures in order to lessen the effects of unfavorable abnormal behavior. Not all anomalies, of course, are bad; in fact, some can be signs that something positive is developing or increasing, and it would be preferable to find out sooner rather than later.
Consider the scenario where a famous person endorses a product on Instagram. Sales of that product may increase significantly as a result of the favorable publicity this external reference has created, but only if the company acts quickly enough to take advantage of the free publicity.
This was a lesson that a big clothing company had to learn the hard way when its BI team found a celebrity endorsement a few days later. They could have taken advantage of the opportunity by raising the price or adding more inventory to meet customer demand if they had realized in real-time the sharp increase in sales for that product and the quickly declining inventory of that product in one of their regional warehouses.
The clothing firm is currently trying to identify any abrupt increases in sales of any of its items. This information may be detected in a matter of minutes, and by promptly alerting the firm to any spikes in demand, it can take appropriate action to guarantee that there is enough inventory on hand to meet the unexpected but much-welcome demand.
Utilize Metrics on a Large Scale
An AI-powered analytics system may operate with millions or even billions of metrics at simultaneously, whereas a KPI dashboard may be able to collect and provide data on thousands of variables. More metrics provide for more coverage, or depth and breadth, of the business’s activities, as well as greater granularity.
Minute Media is an ad tech firm that collects over 700,000 indicators to keep an eye on the company from all aspects. The business employs Autonomous Detection to find anomalies in the data that can point to problems with video player performance, invalid traffic, or other circumstances that result in ad revenue loss. Minute Media has improved its bottom line by increasing its margins on ad sales since putting the AI-powered analytics solution into place.
Align Metrics from Various Sources
Now when hundreds or perhaps more of metrics are being used, some of them will have linkages with one another that aren’t always immediately apparent. For instance, a DNS server outage in another country might be affecting a business’s online traffic, leading to a decrease in visitors and income. This cause-and-effect link can only be found via analytics driven by AI.
To find previously undiscovered links among measurements, solutions like those that automatically correlate information from several sources across the organization are available. When utilized for root cause analysis and to shorten the time it takes to notice an issue and take remedial action, correlation analysis is extremely beneficial (TTD, TTR).
The simultaneous occurrence of two odd occurrences or anomalies might aid in determining the root cause of an issue. If an issue can be identified and resolved quickly, the cost to the company will be reduced when it arises.
Take a look at this sample ad tech use case. To boost their ad serving income, Google and Microsoft both rely on deep learning advancements. These businesses can quickly detect patterns and correlations thanks to AI-powered analytics; for example, they may immediately link a decrease in a client’s ad bidding activity to server slowness. The ad tech provider can assist the bidding activity return to normal levels by resolving the latency issue after the fundamental cause has been promptly recognized.