Your company might process terabytes of data every day – but no matter how much your data grows, does your BI dashboard keep falling behind?

Dashboard visualizations do not capture the full scope of business data, and they don’t update in real time. In the end, data analysts who rely on dashboards for business decisions spend most of their time sifting through and refreshing screens, trying to spot trends as they occur.

It’s what many consider dashboard hell.

Time is money, and wasting time with manual monitoring is very costly.  Here are five ways to find some real answers fast.

1. Proactive Management Takes Acting in the Moment

Most dashboards do not show data in real time.

This is detrimental to modern businesses, which often run tightly-integrated ecosystems of applications and infrastructure that stretch across multiple area of operations. These ecosystems are sensitive. For example, leading adtech platform Rubicon Project fields trillions of bid requests per month and needs to analyze billions of data points. In that environment, every minute counts.

According to Gartner, downtime costs the average business more than $300K per hour. The ability to work in real time reduces time to detection and time to resolution, which is essential to moving from reactive to proactive incident management.

2. Prevent Small, Impactful Incidents from Getting Lost

Catching noticeable incidents is rather easy. But detecting hard-to-spot incidents is statistically unlikely when done with dashboards – which gives them the time to accumulate to the same impact as larger incidents.

Important business incidents affecting only one component of the business can get lost in a KPI, especially when they a calculated average of multiple metrics. Averages hide important data. For example, your server cluster might be displaying a 99.99% average worth of uptime. This KPI doesn’t tell you if one of your servers is displaying an anomalously high amount of downtime, however. In a data center with thousands of servers, a single server is a tiny data point, but depending on what that server is running, it could be incredibly important.

3. Filter the Noise and Find the Signals that Matter

Data drives decision-making. While the noise of a dashboard can be comforting, there is no assurance that the right contextual information is there to make a decision when necessary.

How do you tell which data is important, and which data is worth ignoring? You can’t, or at least you can’t filter data at the speed that business requires. Data complexity, data-type growth and data volumes threaten to overwhelm the dashboard interface, weakening the dashboard’s consumability. It’s humanly impossible to focus our eyes on more than one place at a time. Which brings us to our next point.

4. Don’t Rely on Human-Powered Correlation

For data analysts, when they catch an anomaly, such as a spike in error rates, on their dashboard, they can try to correlate this with any other relevant behaviors. This might mean flipping through other dashboards that they’re monitoring in order to triangulate that spike with other issues to narrow down the root cause. This is a time-consuming practice, completely dependent on the ability of the analyst to spot key issues and the level data granularity.

Dashboards are deceivingly simple and most users take for granted all the data science that takes place behind the scenes to clean and link the vast amounts of data that go into business reports. At the end of the day, preparing data for dashboard analysis can take up to 80% of the time devoted to a typical project.

Furthermore, If the human operator isn’t paying attention, or if the data isn’t sufficiently detailed (for example, if it’s one of the heavily-averaged KPIs mentioned above), it’s entirely likely that the analyst will dismiss the error spike as a false positive, or just overlook the issue.

Even with manual correlation, when the analysts does manage to correlate events to an error, it’s still a slow process

5. Leverage the Power of AI Analytics

For large amounts of streaming data,  AI analytics provides real time insights, helping analysts support the business and identify issues in real-time.

Gartner even listed augmented analytics – analytics technology that uses AI/ML techniques –  as the technology most likely to disrupt the world of analytics. It certainly has the power to transform how we access, share and discover information at every level of an organization.

Conclusion

For analysts, AI-driven, real-time analytics means an end to the era of being glued to dashboards. It means accurate alerts of anomalies, before customers notice and can complain. AI analytics provides real-time insights that enables data analysts to proactively mitigate issues before they become emergencies.

Topics: AnalyticsBusiness IntelligenceBig Data
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