Cognos Analytics 11.1, augmented intelligence, advanced and embedded analytics, and beyond!
The golden era of analytics is upon us; smarter systems, augmented with artificial intelligence, capable of managing and filtering massive amounts of data, empowering end users to make smarter decisions, and delivering major returns on investment in the process. According to Gary Quirke, CEO of QueBIT, in a recent CFO magazine webinar, “this period in history will be defined by the commencement of man and machine working in perfect harmony, combining machine artificial intelligence with the irreplaceable power of human intuitive reasoning to achieve optimum decision making performance."
With measurable examples of returns on investment exceeding $200 million annually, it's easy to demonstrate the high value of embedding AI and machine learning into traditional business processes. Quirke believes the companies which are successful in embedding of AI and machine learning directly into their business processes, will be the ones that thrive and succeed, and those that are either unwilling or unable to do this will ultimately fall behind.
According to CIO magazine, data science is becoming democratized to a larger group of data-astute business users. In a recent Gartner poll, 50% of CFOs declared that “leveraging predictive analytics" was a top focus to improve business performance by 2020. This is consistent with recent trends in the software industry. For example, IBM has embedded AI and predictive analytics directly into its market leading business intelligence platform, Cognos Analytics. The latest version of Cognos Analytics is a forerunner in natively embedding natural language processing, augmented intelligence, and predictive modeling functionality for all types of business professionals. QueBIT has developed a number of resources to assist readers of this blog in better understanding the capabilities of the latest release of Cognos Analytics:
For readers that are currently using earlier versions of Cognos Business Intelligence (e.g. version 10.2.2) or Cognos Analytics (e.g. Version 11.0), or indeed any other business intelligence platform, we strongly recommend signing up for a free trial of Cognos Analytics 11.1 and explore further in your own time.
Software tools will continue to drive greater levels of automation of the work that has historically been performed by humans. In addition, organizations will increasingly look to build custom systems that leverage AI and machine learning to both augment and automate core business processes, and to improve human decision making. This will help organizations to eradicate imperfections in business decision making that are driven by the natural flaws in human thought process. Studies have shown that humans have a tendency towards adopting bias in their decision making, and tend to see things in black and white terms; it’s either good or it’s bad. For most decisions, the reality is somewhere in between these extremes, and superior decision making is achieved by leveraging the machine to perform the complex mathematical and statistical calculations, on the massive amounts of data, that humans would simply not be able to perform.
While it may be appealing to think that investments in the latest software can deliver staggering returns on investment, it is also important to recognize the importance of adding true data science knowledge and experience into the process. The machines can only do part of the job, and the data scientists are required to help determine the right data and models for the machines to act upon, and interpret the results correctly, to avoid acting upon false negative and false positive analyses. Finding skilled data science resources has not been an easy task historically because the availability of these skills has been very limited. However, times are fast changing. Many universities now include a level of AI and machine learning study in many of their courses beyond engineering and computer science; in particular, many business courses are now incorporating data science as a core requirement of study. In addition, organizations can work with partners like QueBIT to help deliver materially impactful solutions, and to also accelerate the transfer of data science knowledge into their organizations. The key to long term success with analytics will be the ability to have ready access to the required data science skill sets, either by developing these skills in house, or by partnering with companies like QueBIT. This is what will separate successful companies from the less successful in companies, in this golden era of analytics.
