Hyperautomation Part 2: Automated Algorithmic Analytics

A couple of weeks back, I penned my thoughts on the topic of Hyperautomation. First, thanks to so many of you that had reached out in response to the article. The reactions ranged from "spot on" to "I didn't realize the potential opportunity for Automation in ERP." In general, I walked away humbled by your feedback, commendations, and recommendations for future writeups.

In that spirit, I want to write about a topic in automation near and dear to me. It's the ability to automate analytics, and in particular, to conduct algorithmic derivative analysis in an automated fashion.

This quote from the famed physicist Niels Bohr pretty much sums it up. Forecasting is hard, especially if accurate predictions are part of what's required. The reality is that the further out one goes, the harder it is to predict and forecast accurately. But therein is the classic innovator's dilemma – short term predictions can be useful, but what's more impactful is the ability to forecast accurately over a more extended period.

When combined with the ability to simulate changes and conditional events in the underlying dataset and to visualize consequential impacts, the predictive forecast becomes a critical instrument in the company's analytical armory.

Stephan Unger, Daimler Mobility's Chief Financial Officer, says, "To master the digital transformation, a company must take a comprehensive approach to algorithm-based forward-looking steering," automated forecasting is fast becoming critical to a company's transformational imperatives. He continues, "This includes not only advanced analytical methods, new technologies, and the right expertise, but also an engaging approach to change management."

Five key components are typically required to achieve automated analytics and algorithmic forecasting:

1. Rich Datasets that provide historical and statistical insights
2. Analytical Algorithms that harness cutting edge predictions, and recommendations.
3. AI Applications that bring in automation at scale
4. Modern Compute Infrastructure to support the analytical and derivative calculations in a highly performant manner
5. Human intelligence provides a real uplift when combined with machine intelligence as humans can translate and evaluate the machine's conclusions into actions and decisions.

Automated algorithmic approaches to forecasting can streamline and simplify the traditional spreadsheet-driven manual methods present in companies. It can also eliminate the biases that people introduce, unintentionally, into the forecasting process. And lastly, it allows for frequent, rapid updates, and adds support for simulations and modeling that enable the modern digital enterprise to align itself for success.

At Auditoria, we have built the industry's first Cognitive Automation platform that combines automation of tasks and business processes, and automation of analytics, in one simple SaaS offering. Combined with cutting edge NLP and ML, we empower CFOs to digitally transform the back-office and bring automated outcomes to every function in their charter.

My team and I will be writing more on specific approaches for automated analytics in future articles. If we can help introduce you to the topic of automated analytics and automated algorithmic forecasting, please do reach out to us. We look forward to sharing further insights and discussing best practices with you all.