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Why Old-World Business Intelligence Is Not Enough to Navigate the New Normal

In this special guest feature, David Drai, Founder and CEO, Anodot, discusses how organizations that implement business monitoring and real-time analytics now will not only better manage challenging business conditions in the short-term but will be well-positioned for growth when economic recovery begins in 2022. David co-founded Anodot out of his own frustration with not being able to get real time insights in spite of having access to real time big data. Anodot has developed state of the art AI-powered analytics, that illuminates business blind spots, so companies will never miss another business incident.

2021 has ushered in a new era of business uncertainty, forcing organizations to manage their businesses more proactively and be better prepared to handle unexpected changes and potential crisis situations.  In the past, traditional business intelligence (BI) tools worked effectively. But times have changed, and organizations can no longer reactively manage volatile economic conditions. 

In this new world, organizations need more than just traditional BI. These tools effectively query data, slice and dice it in various ways, and provide compelling visualizations. Gartner offers insight on traditional BI and why organizations are considering a more modern approach: https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/

BI provides Retrospective Analytics, using data to understand what happened in the past in order to plan for future events. When the future trajectory is more certain, retrospective analytics can help bring greater clarity when making business decisions. In our current climate, however, the past is no longer relevant because so much has changed.

Organizations now must adapt quickly to changing conditions and increased uncertainty – this is now the new normal. Traditional BI is too reactive, and the speed of analysis is not fast enough to help organizations manage this challenging dynamic. Furthermore, the majority of the analysis done with traditional BI is manual: an analyst forms a query to answer questions and needs to review and analyze the results. This method is slow and static, and incapable of being scaled to the demands of today’s enterprise.

New technologies allow organizations to automate business monitoring and anomaly detection. Below is an example of an AI-driven alert triggered after the system detected an anomalous drop in an organization’s revenue. Their metric shifted to the KPI’s new pattern soon after the onset of COVID-19 in the United States.

Cost-cutting is also important in this new environment. Forward-thinking companies are using automated processes to uncover issues faster, with systems that apply machine learning (ML) algorithms to constantly analyze data and catch costly errors in real time. 

BI alone is not as effective with cost cutting because it takes too much time until anomalies trigger static thresholds. Furthermore, to get deeper insights, BI dashboards force users to drill down manually, which is a time-consuming process.  The best approach is to use vendor solutions that offer the fastest time to value and much higher ROI. Developing in-house solutions are costly and time to value is at least 18 months.

These companies are also shifting from pull analytics to push analytics, which provides far more insights very quickly so proactive measures can be taken immediately. The insights derived from the data, such as overspending on marketing campaigns or undercharging customers, will help teams troubleshoot potential problems and make better decisions in the future.

More companies are embracing automated business monitoring and real-time analytics to enhance their existing analytics stack and effectively manage increasingly uncertain business conditions. Here are three best practices to follow when implementing these breakthrough technologies:

Focus analytics efforts on clear pain points. Do less data and analytics explorations and implement solutions to known problems with a clear ROI and business outcome in mind. For instance:

  • Anomaly detection for cloud cost monitoring or campaign monitoring
  • Forecasting demand for better inventory and supply chain management, churn prediction, etc.

Consider vendor solutions. When available, solutions from vendors will be cheaper and faster to implement than home-grown solutions. Vendor solutions will be higher quality because of the vendor’s deep expertise. It is also an expense that is easy to cut if the ROI is not sufficient.

Perform the analytics across both the front- and back-office KPIs, as issues can manifest across the business in a variety of ways – and those may even conflict. For example, an adtech company may have customers that reduce spend significantly, such as travel and hospitality organizations, while others that increase spend, such as food delivery services, especially in this current environment. Looking at the data at the aggregate level will impair the ability to understand the full impact of potential issues and current trends that may be producing conflicting data.

Organizations that implement business monitoring and real-time analytics now will not only better manage challenging business conditions in the short-term but will be well-positioned for growth when economic recovery begins in 2022.

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