deriving value from device data

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Deriving Value

from Device Data


Agenda

Device Data: Trends and Relevance

Business Model Implications

Value Added

The Way Forward: 3B Framework

Conclusion


Numerous devices supplement existing enterprise data sources

Boeing 737 engine creates 20TB of data per hour in flight

Oil rigs generate ~30,000 data points per second

This flood of information needs to be transformed into insights

Hitherto ignored device data can prove to be a differentiator

Device Data: Trends and Relevance

Analytics has helped increase profitability and enhance customer experience

Traditional data sources: CRM systems, HR data, historical sales figures, etc.

EIU Survey: Machine-generated Data Collection Trends

Majority of organizations actively collect device data

51.1 12.7 29.2 7

Collect Plan to collect Do not collect Don't know

% of respondents (CXOs) and their attitude towards device data

(e.g., sensors, smart grid, RFID, network logs, telematics, etc.)


New business line: GPS driven “anytime-anywhere” vehicle servicing

Ancillary business line: New insurance plans based on medical device data

Existing business enhancement: Improving cell phones using network logs

Business Model Implications

Telematics Data

• Insurance: Pay-as-you-drive insurance

• Automobile: Better inventory management

• Aerospace: Accurate equipment state assessment

Remote Sensing Data

• Consumer Electronics: Customer-oriented devices

• Logistics, retail, etc.: Better supply chain management

• IT departments: More automation

Device Logs

• Oil & Gas: Better risk mitigation

• City planning: Transportation and route optimization

Device Data and its Business Potential


Value Added

Value Added

New Revenue Streams

Enhanced Customer

Experience

Cost Reduction

Real-time data can help fine-tune preventive

maintenance schedules of machines

E.g., data can help diagnose faults in medical

equipment and thus avoid downtime

Tracking customer behavior through their

devices can help reduce attrition

E.g., cable & satellite services that track

viewing can customize channel offerings

Device data can lead to service

discovery and increase customer spends

E.g., a washing machine manufacturer

can offer remote diagnostic services

Device data analytics is

beneficial in cases where:

• Customer interaction is

limited

• Data can assist in device

maintenance


The Way Forwards: 3B Framework

Massive volumes of device data can be capitalized by:

• Investment in people, processes and technology

• Conscious effort to use it in strategic decision-making

Build a Business Case

Best-of-Breed

Technology

Base-lined Data

Processes

• Identify lines of business that

will benefit from device data

• Develop ROI models

• Isolate relevant data

sources

• Baseline data quality

• Ensure adherence to

regulations

Invest in technologies for:

• Data storage and processing

• Visualization and predictive

analytics


Conclusion

Tough business problems can be handled using untapped data

sources – machinery, sensors, mobile devices, etc.

Sophisticated analytical engines are required to extract value

from vast tranches of data

Strong organizational focus is needed to ask the right questions

and combine insights from several concurrent data streams.


For more details please visit the link below:

http://www.wipro.com/retail-big-data-revolution/docs/Wipro-

analytics-leveraging-device-data-analytics.pdf

http://www.wipro.com/retail-big-data-revolution/docs/Wipro-analytics-leveraging-device-data-analytics.pdf




















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