Machine Learning Implications for Intelligence and Insights - PART 2 of 9

Disrupting the Intelligence World 

Through the eyes of an intelligence professional, the business climate will be increasingly challenging. The onslaught of never-ending data, the proliferation of incubation test beds, the rise of internet business disruptors overnight - this all has a dramatic effect on the intelligence professional's ability to not only acquire the right data, but to be able to synthesize the analytics in order to drive it to the right conclusions, all while navigating through a non-stop, fluid information environment. Sequential processing of analytics is a thing of the past. The business climate is moving so fast that organizations need to incorporate intelligence and analytics as an integral part of their organizations, where the ongoing data collection, synthesis and integration with the entire company strategy is a normal part of the work process.

Unfortunately, Intelligence communities are often times stuck into the same mold of consistent use of common methodologies, fragmented infrastructure hierarchy, and limited ability to influence company direction. For the intelligence to be truly applied universally, corporations must take a pragmatic approach to leveraging the intelligence and analysis efforts throughout the company and recognize the value of systemic approaches to integrating intelligence into the overall strategy of the company.

The fundamental key differentiator is the ability to generate executable insights at a speed which is higher than that of your competitors, and to act upon those insights at the correct moment. History is fraught with examples of premature or mistimed product/technology introductions plagued with miscues due to poor intelligence, delayed decision making and ill-conceived execution tactics and strategies. But history is also rich in examples of introductions that have successfully mastered this challenge – first and foremost by understanding the technology curve, gathering insights and embracing business disruption challenges while identifying new opportunities for growth. This has been an extremely uncomfortable behavior for many CEOs – having to think outside of their business core competencies and take strategic action that may be far outside the revenue generating functions of the company. Of course, the risks and uncertainty many times outweigh the decision to move tangentially, but organizations cannot take the huge risk of “disruption impact avoidance” and put their heads in the sand with the hopes of it not affecting their company – especially in today’s digitized society!

        The questions you have to ask:

        What is the potential for this new idea?

        What information do I need to add this to our business strategy?

        How fast can I monetize the opportunity?

So, how does an intelligence organization capitalize on this information-rich digitized society to provide the content so organizations can be best equipped to act? The challenge is the ability to acquire, process, and analyze data on a virtually real-time basis. Realistic? Not entirely, but there are mechanisms that can help with getting the modeling behavior further down the timescale axis into an environment where analytics can be done more frequently, and with better accuracy than the historical traditional “batch” processing algorithms often used by the intelligence community.

A new and exciting capability is around, automating the collection of pertinent information and the subsequent downstream “learnings” of the data pool. The ability to utilize tools to gather and process this data through large central data repositories (Knowledge Management Systems, Business Information Systems, Customer Databases, etc.) is becoming a necessity. However, coupled with this is the application of Machine Learning to subsequently “predict” the business behavior and to automate much of the analytics process in order to have nimbler organizations and provide actionable insights on a faster basis. This is where organizations have the biggest return on investment when faced with the daunting challenges of the digitized society.

More to come...

This was part TWO in our series, based on the article “Machine Learning Implications for Intelligence and Insights”, written by Jesper Martell, Comintelli, and Paul Santilli, Hewlett Packard Enterprise.

WEBINAR: Machine Learning Implications for Intelligence and Insights

Presenters: Jesper Martell, Comintelli & Paul Santilli, Hewlett Packard Enterprise

Thursday, October 26, 2017, 10:00am Eastern, Hosted by  SCIP

This interactive webinar describes how Machine Learning (ML) can be applied to solve intelligence problems.

1. What is a Machine Learning algorithm?
2. How can new ML/AI technologies augment our intelligence capabilities?
3. What are some of the challenges and risks of ML?

Read more and register here!