Post on 17-Jul-2015
Cognitive Analytics Reimagining the future of Business in the
Digital Era
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The status quo Cognition is the mental action or process of acquiring knowledge and understanding through thought,
experience, and the senses. Cognition leads to formation of perceptions and intuitions that leads to knowledge.
The evolution of cognitive computing has passed through the stages of pure academic setup to intelligent
commercial applications like Watson, Numenta at enterprise level and voice assisted applications like Siri and
Google Now.
Such Applications are sensory or mobile based (Apple Siri), contextual and dynamic. Enabled by scalable
processing power and associative memory such event driven applications emulates human behavior. These
applications utilize context based hypothesis to generate associative insights using machine learning and
natural language processing. Cognitive analytics combines the sophistication of natural language processing
and machine learning to provide probabilistic responses to user queries based on contextualized information.
The evolution of analytical data processing follows a path correlated positively to Business Data volume and processing speed of enterprise
business applications
Cognitive Applications are the next step forward towards the future of prescriptive analytics. While in
predictive and prescriptive analytics insights are derived by processing structured and unstructured data
sources available across multiple data sources, cognitive computing seeks to add a level of consciousness to
the process of data crunching. This is driven by massive processing power as it involves processing of
peta/Exabytes of data spread across multiple disparate data sources to generate actionable insights by
eliminating spurious co-relations in a narrower limit of confidence. The massive amount of structured and
unstructured data is characterized by volume, variety and velocity but keeping in mind the usability of analytical
insights veracity is the prime dimension that dictates the applicability and utility of derived business insights.
This is where the effectiveness of cognitive applications come in, extracting the trustworthy insights by
eliminating deviations on either side of the data spectrum.
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The Industry The industry is rapidly evolving and an empowered consumer is the one deciding the success of organizational
strategy. The enterprise has to rapidly adapt to understand the pulse of the customer by utilizing analytical
models that describe, predicts and prescribes the consumer behavior to ensure proactive responsiveness.
Surveys suggest that organizations that have historically been trying to understand the customer sentiment
and behavior have achieved highest sales growth and profitability in the long run. Amazon is a prime example
of a firm who has traditionally focused on comprehending customer behavior.
But the stakes are increasing, competitive rivalry driven by availability of substitutes and increasing bargaining
power of the consumer has increased price pressures and customer churn rates. Surveys in e commerce
domain suggest that a 1% decrease in CSAT scores roughly corresponds in 50% increase in probability of
defection among the top 5% customer who contribute to an average of 40% of a firm’s revenue.
The challenge lies in assuming a critical market position that enables us to optimize price and demand, enhance
customer loyalty and identify advocates of a brand that would enable us to scale up operations by mitigating
risks arising from customer churn that threatens to bring down the growth potential of a firm.
Retail/CPG, banking, healthcare are some of the sectors that have traditionally leveraged customer experience
management to enhance loyalty and advocacy to drive revenue growth. These sectors have always been the
early adopters of disruptions in the customer experience management.
At present these industries are in a state of flux. Traditional business processes are at a strategic inflexion point,
customer relationship management has evolved from mere solving of queries to emulating and simulating
consumer behavior to achieve near real-time results. The new era of CRM will be predominantly dictated by
intelligent applications like that of IBM Watson which will not only help you identify the best consumer
portfolio for revenue growth but at the same time they will help you gather real time customer behavior and
business insights.
Cognitive Computing Architecture
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The cognitive model is driven by a highly scalable processing platform that can be cloud based or on premise.
It supports the language processing, data modelling and analysis functions that can be accessed by enterprise
or business users via mobile, web platforms and enterprise interfaces. The core of the analytics engine is driven
by an ETL engine that integrates and transforms disparate data sources with an additional block for analysis,
hypothesis and qualification of integrated data. The user interacts with the analytical engine via a UI that can
be driven by structured queries or ad-hoc queries driven by form based interface applications
Challenges to adoption There are various challenges that may possibly be constraining adoption of cognitive technologies at the
present context
Technological Readiness
An enterprises technological readiness will define the smooth adoption and contextual fitment of
cognitive technologies in the business processes. Sophistication of underlying platforms will play the
major role in this context. There is a need for interconnectivity and interoperability among existing
business processes to make seamless integration of data sources a reality. While a well-defined data
integration strategy will actually facilitate adoption but at the same time, having the flexibility to re-
organize and re-orient business processes to suit business transformation needs is equally important.
Leadership
Survey data suggests that most of the enterprise leadership is not ready to take the leap of faith due
to lack of awareness and rigidity to break the status quo. Lack of active projects and apprehension
about capital intensive projects have hindered budget allocation and enterprise buy in at the
leadership level
Strategic Fitment
Cognitive technologies are not a generic fit for all industries, there is a need to tailor solutions as per
industry requirements and business strategy. Healthcare, insurance, retail and banking are the primary
sectors that can possibly benefit this disruption in the current state. These industries have traditionally
been investing billions of dollars in predictive analytics to reap long term business benefits and have
been achieving ROIs across multiple high investment projects.
Collaboration
The best of the world class research in cognitive analytics are being carried out in conjunction with the
world’s top technology institutes. IBMs Watson has been a product of collaborative effort with MIT,
CMU and other US universities. Enterprises need to have a productive partnerships with such
institutions to foster creative thinking and co-innovate.
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Industries and Use Cases
Healthcare
This sector is characterized by widespread use of analytics, research and data crunching. The
healthcare industry generates thousands of GBs of data every moment. More than 70% of this data is
unstructured and disaggregated. Healthcare compliance practices dictates the record of data in a
particular standard but the reporting practices vary from place to place. It is not an industry that is
defined by static business rules and regulations. The healthcare industry has traditionally been volatile
and there are multiple instances where Enterprise data mining projects have proved futile because of
the rapidly evolving business rules. This sector is perhaps the perfect fit for adoption of cognitive
technologies keeping in mind the fact this would allow the combination of structured and unstructured
records to gain insights ranging from patient healthcare, drug research and even healthcare insurance
claim processing. Cognitive analytics is already being used for cancer research to identify patients at
risk and carry out effective treatment based on associative insights.
Retail
Retail sector has been effectively leveraging analytics to improve customer loyalty and promote
advocacy to enhance revenue growth. The online as well as bring and mortar retail has utilized
advanced analytics to merge structured consumer data from POS and unstructured social data to
generate consumer behavior insights that can be used for targeted promotions. This has helped retail
majors both in online and offline space to reduce churn, increase footfall, improve shopping
experience and increase Customer life time value in the long term. The solutions range from self-help
kiosks, virtual in-store purchase advisors, real time behavior based offers. Lost sales opportunities
contribute to revenue losses close to $600 Billion worldwide. Intuitive cognitive solutions can help
solve the problem of customer defection and improve sales. In retail such applications can be
leveraged to improve customer loyalty by adding a touch of personalization and contextualization in
case of real time offer management.
Banking and Financial Services
Recent trends in adoption of analytics in banking suggests a tendency to adopt predictive technologies
to identify high quality investment opportunities, quantify implicit risks and fine tune investment
portfolios. Cognitive digital advisors enable safer investment and reap maximum benefits by providing
specific investment advice in near real time scenario. Development bank of Singapore has turned to
IBM’s Watson to revamp its analytics capabilities in the Consumer Banking and Wealth Management
division. The aim is to integrate corporate customer data and unstructured data available in social
domain to enhance its CRM effectiveness and optimize investment returns. The technology aims to
focus on improving the more creative and human interaction elements of business processes.
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Abhishek Nandan
Consultant
Business Tech Consulting, Brillio
abhishek.nandan@brillio.com
Mobile 9900912161