NASSCOM ILF 2015: HOW ANALYTICS IS TRANSFORMING COMPETITIVE STRATEGY by Kislaya Prasad

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HOW ANALYTICS IS TRANSFORMING COMPETITIVE STRATEGY

HOW ANALYTICS IS TRANSFORMING COMPETITIVE STRATEGYKislaya Prasad, PhD

Artificial Intelligence

Ubiquitous Sensors2The Next Wave of Disruption

Key point to make is that we are at the cusp of the next wave of digital disruption and that the the future has arrived. The promise of artificial intelligence is finally being realized advances in robotics, natural language processing, image recognition, are transforming business (how production occurs, how supply chains are organized, how services are delivered, how goods are marketed and sold, how companies compete, ). The proliferation of sensors is transformational embedded in devices they enable control from remote locations, but also gather and transmit information.Already here on production lines and within devices recording performance characteristics and usage patterns, on wearable devices tracking health metrics, and within our smart phones transmitting geographic locations.However, the real power is in their interconnectedness one smart sensor would be merely interesting; a billion interconnected sensors redefine the parameters of our world. In this talk I will draw out the implications of this wave of disruption for business strategy2The new technologies add to the trove of digital data 3Continuation of a Grand Trend

Expanding digital footprint The new technologies add to the trove of digital data. Key point is that, in this sense, what is going on is the continuation (and perhaps even the culmination) of a grand trend.

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Data4Key AssetData is the new OilOn the technology side you ask How do I store, secure, manipulate, process, and manage this resource? and those are all important questions.On the strategy side you ask Who owns the data?

Also worth noting I am not saying data & algorithms.

4Creating business value from large and diverse data sets5Key Capability of the New Era

Data Science and Business AnalyticsData ScientistsData scientists need to have the mix of skills and capabilities required to create business value from large and diverse data sets. This includes the ability to handle and manipulate the data, together with knowledge of statistics, machine learning, optimization and other techniques for extracting knowledge from the data. Good data scientists are going to be the critical resource of the new IT era. The most successful companies will be those with superior Data Science and Business Analytics capability.

5Storage, manipulation, aggregation & visualization of digital data

Predictive analytics6Todays Business Analytics

Some of the classic applications of the current era predicting preferences for books, movies and music; predicting disease outbreaks from search queries; predicting who will click on ads; identifying people you may know; etc.

It is worth observing that some of the great new companies of our age such as Google, Facebook, Amazon, LinkedIn, Netflix, etc. are distinguished precisely by their capability in data and algorithms. 6Asset & operations optimizationPredicting preferencesPredicting health events Determining who will click on adsIdentifying people you may knowDetecting fraud7Classic Applications

7Making effective use of sensor data

Integration of intelligence into business (products, processes and decisions)8Future Business Analytics

With ready sensor data, we may expect greater emphasis on stream data mining (a purely technological issues). Consequently, we may expect new devices that will adapt to usage patterns and provide input for add-on services. Such adaptation my already be found at rudimentary levels in thermostats and other devices on the market today. With intelligent interconnected household devices, the opportunity to learn about consumer preferences will only increase. The subtle point (and link to business analytics) here is that if devices learn to adapt to the needs and preferences of consumers, this new knowledge needs to inform future business decisions.8Devices that adapt to usage patterns and learn about user needs and preferences9Applications

Usage data as source of business intelligenceNew knowledge will inform business decisionsThe continuous sensor data will be a rich resource and will enable the construction of superior predictive models (for all of the aforementioned applications)9EfficiencyKnowledge of Customers10How Data Creates Value

Asset & Operations OptimizationUsage PatternsKnowledge of PreferencesHealth Status

1011Competitive Implications

Market SegmentationProduct DifferentiationCustomizationNew ProductsAdd-on Sales & Service11Data ownershipData stewardshipCulture of evidence-based decision making12Who Wins?

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kprasad@umd.eduThank You!13