Ambient Analytics Manifesto
A call to Action
by Viktor Litvinov, GRT, a member of the Ambient Consortium
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.
Ambient Intelligence
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.
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.
[2]
·
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.
Prescriptive Analytics
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
[3]
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.
[4]
Ambient Analytics
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
[5],
of which only 4% will be collected from the sensors on the IoT.
[6]
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.
- Security
- Privacy
- User Experience
- Data Quality
- Talent Shortage
- Organizational Readiness
[1]
Salamone, Salvatore, Specialist Editor, QuinStreet Enterprise (2015) “Forward
Looking BI”
[2] Abbas,
Dr. Mazlan, CEO of REDtone IOT (July 2013) “Ambient Intelligence”.
[3]
Linden, Alexander, Gartner (July 2015) “Hype Cycle for Advanced Analytics”
[4]
O’Neal, Kelle and Roe, Charles, Cognizant (2015) “Business Intelligence versus
Data Science: A Dataversity® 2015 Report”
[5] 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”.
[6] Allied
Business Intelligence (ABI) Research (April 2015) “Data Captured by IoT
Connections”
Viktor Litvinov is an accomplished entrepreneur skilled in startup, business development, marketing, and operations. Taking vision to successful implementation, he is a thought leader in Digital Transformation, Information Security and Cloud Technologies.
