A Call to Action
There is tremendous competitive advantage available to organizations who have people who can work with various data types and apply a range of analytics techniques to describe past events, predict future ones and prescribe decision options.
“CIOs rank analytics as the number one contributing factor to an organization’s competitiveness. Organizations that use advanced analytics have 33% more revenue growth and 12% more profit growth. Financial top performers are 64% more likely to use analytics to evaluate multiple business factors on an ongoing basis compared to low performing peers.”
Ambient Analytics is the intersection between Ambient Intelligence and Prescriptive Analytics. It leverages the increasing volumes of data and applies business rules, mathematics and computer science to further enhance the resulting predictions and prescribed actions.
The concept of Ambient Intelligence (AmI) was originally developed by Philips Research in the late-1990s. In an AmI world, devices work collaboratively to support peoples’ lives using information that is hidden in the network connecting these devices, now known as the Internet of Things (IoT).
Dr. Mazlan Abbas, CEO of REDtone IoT, describes AmI as the intersection of three key technologies: Ubiquitous Computing, Ubiquitous Communication and an Intelligent User Interface. 
- Ubiquitous Computing: The integration of microprocessors into everyday objects like furniture, clothing, white goods, toys, even paint.
- Ubiquitous Communication: Technology that enables these objects to communicate with each other and the user by means of ad-hoc and wireless networking.
- An Intelligent User Interface: Technology that facilitates the inhabitants of the AmI environment to control and interact with the environment in a natural and personalized way.
Philips Research predicts that by 2020, as devices grow smaller, more connected and more integrated into our environment, technology will disappear into our surroundings. That by combining data and sensors, things and people, lives will be enhanced, work will be performed differently and the competition paradigm will shift.
The diagram below illustrates the phases of Business Intelligence maturity. Business Intelligence has gone from being able to describe what happened in the past, to understanding why it happened, to now being able to predict how likely it is that a specific event will happen in the future. With each phase, the amount of human intervention required has decreased and the “time-to-insight” is reduced.
Prescriptive Analytics takes BI to the next level; optimizing decision-making and reducing the “time-to-action”. This process combines data with business objectives and customer centricity and the outputs are the actual decisions, recommendations and/or automation. This approach also applies the principles of machine learning to continually take in new data to make more accurate predictions and prescribe increasingly better decision options, with an enhanced view of the implications of each decision.
Industry analysts report that Prescriptive Analytics is still in the early stage of adoption with less than 5% of organizations currently applying these techniques and that initial implementations are tactical in nature; focused on cost reduction, maintenance and monitoring. Although we are still far from realizing the strategic promise of re-imagining the Customer experience, a recent study published by Cognizant reveals that 80% of enterprises plan to invest in Predictive Analytics over the next five years.
In today’s digital world, data is produced at an accelerating rate. First with the growth of social media and now with the introduction of the Internet of Things (IoT), IBM predicts that by 2020 there will be 5 times the volume of data that exists today, of which only 4% will be collected from the sensors on the IoT. As the IoT matures, data volumes will continue to grow exponentially.
While experts agree that this analytic capability must be embedded and automated at the operational level, overcoming the challenges to integrate and aggregate disparate data sources with relevant context will not be easy. Another challenge will be designing the User Experience (UX) in a way that it can learn from the user, detect the user’s needs and behavior, and through these, predict what the user wants, when they want them, requiring less instruction from this user. These, and many more challenges, await on the path to adoption.
The unprecedented access to data raises a myriad of issues, both Corporate and Consumer. This manifesto is a call to action for the Advanced Analytics community to work collaboratively to both accelerate the pace of the adoption of these techniques in pursuit of improved Customer experience, while protecting interests, both private and public.
Following this publication will be a series of articles that address the issues raised by these maturing disciplines. Stay tuned for more detail on these and other related topics.
- User Experience (UX)
- Data Quality
- Talent Shortage
- Organizational Readiness
 Salamone, Salvatore, Specialist Editor, QuinStreet Enterprise (2015) “Forward Looking BI”
 Abbas, Dr. Mazlan, CEO of REDtone IoT (July 2013) “Ambient Intelligence”.
 Linden, Alexander, Gartner (July 2015) “Hype Cycle for Advanced Analytics”
 O’Neal, Kelle and Roe, Charles, Cognizant (2015) “Business Intelligence versus Data Science: A Dataversity® 2015 Report”
 IBM data scientists break big data into four dimensions: volume, variety, velocity and veracity. This infographic’s sources: McKinsey Global Institute, Twitter, Cisco, Gartner, EMC, SAS, IBM, MEPTEC, QAS “The Four V’s of Big Data”.
 Allied Business Intelligence (ABI) Research (April 2015) “Data Captured by IoT Connections”