Leveraging device data analytics for business growth. Jayant Prabhu General Manager & Global Practice Head Information Management Wipro Technologies

www.wipro.com

Leveraging device data analytics

for business growth

Jayant Prabhu

General Manager & Global Practice Head Information Management Wipro Technologies

Table of Contents

03

Leveraging device data analytics for business growth

04

Creating new business models and customer delight

05

Device data can add value to a business

Non-traditional data collection trends (% respondents)

Data to

From

_insights

The world has already woken up to data. Now it is being nudged by analytics. While business has recognized data as an asset, it is now unlocking the secrets to higher efficiencies, ways to lower costs, improve customer service and define new products through analytics. There is a sizable excitement around data. However as new sources of data are tapped every day, the excitement around analytics is turning feverish.

Conventional enterprise data sources (SCM, CRM, HCM and so on) are now being supplemented by machine data. Everything it seems is producing ferocious volumes of alerts, signals, behavior characteristics, records and numbers. This includes sensors on oil rigs, aircraft components, medical equipment, mobile devices, network logs, elevators in buildings, traffic monitoring systems, video cameras in retail stores, computer logs, GPS systems, online clickstreams and even

03

seemingly mundane things like the washing machine and the microwave in your home. In an age of shortages and severe constraints, it is ironic to note that there is no dearth of device generated data.

This invisible flood of numbers generated by machines holds a treasure trove of information. Now, there is mounting evidence that correlates high-growth firms with data usage. A 2013 Economist Intelligence Report called `The Data Directive' commissioned by Wipro suggests that 40% of CXOs feel insights from machine generated data will be beneficial for their companies for taking strategic decisions. Already 51% of companies in the study showed they collect machine generated data (Figure 1). What remains to happen is using analytics to transform this data into business intelligence and usable insight.

Leveraging device data analytics for business growth

Syndicated data from third-party data providers (e.g. market data, weather, etc.)

Open data (e.g. data released by governments)

Staff data (e.g. Emails, calendars, instant messaging, etc.)

Machine generated data ( sensors, smart grid, RFID, network logs, telematics, etc.)

e.g.

Location-based information ( GPS, mobile logins, etc.)

e.g.

Social media ( Facebook, Twitter, YouTube, blogs, etc.)

e.g.

Contact center data ( audio conversations text chats, customer emails, etc.)

e.g. 41 22.5 31.4 5.1 42 51.1 17.7 12.7 36 29.2 4.4 7 62 11.1 23.4 3.5 65.5 10.8 18 5.7 70.3 9.5 15.5 4.7 72.2 12 11.7 4.1

Source: Economist Intelligence Unit survey

1

2

3

4

5

6

7

Collects Plans to collect Does not collect Don’t know

Creating new business

models and customer delight

A Boeing 737 engine creates 10 terabytes of data every 30 minutes in flight. A six hour flight from New York to LA on a twin-engine aircraft produces 240-terabytes of data. To put this in perspective, a single terabyte could hold about 300 hours of good quality video or 1,000 copies of the Encyclopedia Britannica. Oil rigs generate about 30,000 data points per second. Less than 5 % of that information is used. The waste of this data – which is an asset -- is indefensible. The engine data holds information on performance that can create new and transparent business models to lease the engine. The data from oil rigs can create better maintenance schedules, reducing down time.

A recent example of breakthrough research indicates the wealth of information that can be uncovered from machine data. The European Organization for Nuclear Research (CERN) discovered the God Particle (the much discussed Higgs boson) by analyzing data produced by smashing particles in a 17-mile tunnel. CERN handled 15 to 20 PetaBytes of data annually and now claims to be on track to produce 30 PB of data for analysis.

That, of course, is a scientific experiment. But machine data is becoming equally invaluable to business. As an example, a manufacturer of automobiles can venture into after sales and open a long-term, sustainable revenue stream using data from telematics devices in vehicles. The data can help identify the components a vehicle will

require at the time of service, effectively taking the `probability' of a component being required out of the equation. This spells the equivalent of a revolution in servicing as the automobile company can accurately manage its supply chain for the exact requirement. Some automobile manufacturers are going a step further and setting new standards in maintenance, quality and customer service by analyzing vast amounts of vehicle performance, diagnostic, billing and warranty data. The data is collated from enterprise systems, on-board vehicle telematics equipment and during scheduled vehicle service.

The power of machine data is apparent in related instances. A vehicle fitted with telematics devices can shorten insurance claims cycles. Insurance companies report that receiving the first notice of loss within 30 minutes of an accident reduces settlement time and loss adjustment costs (sometimes by as much as US$ 800). A vehicle insurance company could even ask itself, “Can I offer lowered insurance premiums to drivers who demonstrate safe and cautious driving patterns? And make those who live life on the edge pay more?” The questions make business sense. Until a few years ago, they would have evoked mirth and possibly a wistful look at the future of insurance. That future is now here. To the utter delight of customers, device data from vehicles can produce accurate answers to questions on adjusting insurance premiums. We are fond of an example that often comes to mind when discussing device data. Imagine a lady in her 80s who lives by herself. The data from her coffee machine shows that every day she switches it on at 9 a.m. The day it doesn't go on at 9 a.m. could mean one of several things: she is faced with a medical emergency, the device has failed, the power has failed, and who knows, maybe she is switching over to tea! Each one of these possibilities points to a business opportunity. Of course, the dear lady could just have decided to stay curled up in bed longer, sending everyone into a tizzy. But the point is this: device data, even at its most simple level, can throw up useful information. When combined with other data, it can deliver actionable intelligence (see Figure 2: examples of Device Data and Business Impact).

Historically, machine or device data has not been viewed as a strategic source of information. But with a softening of the economy, businesses are asking themselves, “Does this stack of data contain something I don't know? How do I get to that nugget of information that can change my business? ” Now that everyone owns an ERP system and knows there are limits to addressing inventory optimization, understanding the customer, managing cost and covering risk, there is a clamor for device data as a differentiator. Organizations are realizing that device data should not be ignored.”

Examples of Device Data and Business Impact

l

Insurance:

Pay as you drive insurance schemes

l

_Automobile:

Service discovery, better inventory management

l

Aero:

Reduced equipment downtime, accurate equipment state assessment for improved

performance

l

Computing device companies:

Improved customer satisfaction, innovation

l

Medical devices:

Reduction in field failure rates, enhanced product quality and compliance

All industries:

Improved reliability of IT systems, automation of IT infrastructure

management

l

Oil & Gas Utilities:

Reduction in field failures, predictive asset

City Planning:

Public transport planning, route optimization

Use of

Telematics Data

Use of Remote

Sensing Data

Use of Device

Logs

05

Growth through new revenue models, understanding customers, service discovery and increasing consumer spends to deliver better business results. An interesting way to look at vehicle telematics is from an advertising agency's point of view. The data from several hundred vehicles can be collated and mined to understand driving patterns and the areas where vehicles tend to slow down. The information can be used to drive outdoor advertising campaigns or increase/decrease billboard rentals, there by driving business growth. Similarly, data coming in from vehicle toll collection can record the make and model of the vehicle, combine it with the traffic pattern, and optimize digital signage around the toll point.

Device data can enable new revenue streams for businesses that have very little understanding of the customer because of restricted customer interaction. For example a washing machine manufacturer who does not interact frequently with the customer can offer remote preventive diagnostic services based on machine data. The same manufacturer can use the data for up-selling and cross-selling to customers.

Enhanced customer experience to improve loyalty and reduce customer attrition. The quest for data on customer behavior is intense. However, there are categories of products that have limited interfaces with the customer. The cable and satellite (C&S) service provider serves as a good example. C&S providers are often unable to determine the exact reasons for customer attrition. One such provider engaged us (Wipro) to stem the tide of customers moving over to competition. We looked at the problem and discovered that the solution was in monitoring the rich data being thrown up by the set top box. The data was critical in understanding the customer through the health and state of the device, every click of the remote, signal quality, picture quality, time-of-day for viewing, etc. Using this data, we could diagnose when the set-top-box would go down and proactively ensure that a call was made to the user before service disruption. Using the

data, the C&S provider could increase ARPU through better upsell. An understanding of viewing habits also ensured that relevant advertising could be aimed at the customer for pay-per-view offerings as well as upgrades to more appropriate channel bundles. The customer saw

___

attrition rates drop from 2% to 0.2% this, in addition to improved ARPU.

Drive down costs. Device data is changing the very concept of product, systems, equipment, machinery and plant maintenance. One of the reasons for ineffective maintenance is the lack of timely and factual data that defines the required maintenance. Periodic preventive maintenance schedules are based on specious average-life statistics or mean-time-to-failure data. This is also a dominant reason why managements tends to think of maintenance as a cost. In extreme cases, this may drive management to adopt the run-to-failure mode of maintenance leading to expenses associated with high spare parts inventory levels, overtime costs, machine downtime and production disruptions. Instead, using the continuous stream of device data, organizations can adopt event-driven preventive maintenance that lowers the cost of maintenance and create a win-win situation for customers and manufacturers. As an example, air filters in a vehicle are replaced during servicing after a fixed number of miles. There may be no real reason to replace the filters until the quality of air passing through the filters degrades – a metric that can be accurately picked by sensors, ensuring that filters are replaced only when necessary.

Manufacturers have for long invested in service crews that provide on-site maintenance. When medical equipment, for example, fails, users request for immediate servicing. Valuable time and money is lost when the maintenance crew turns up, diagnoses the problem and often needs more time to acquire the right spares to service the request. Device data could help ensure better problem diagnosis and enable spares requisitioning even before the crew reaches the faulty equipment. In our experience, such systems of proactive maintenance have shown a reduction in field visits by 30% and improvement in field service engineer productivity by up to 25%. Similarly, accurate preventive maintenance using device data can help contain product warranty costs.

There are three areas where device data can add immediate

value to a business:

Organizations are tantalizingly close

to finding the answers to tough

business problems. They know the

answers are cocooned in data.

The Future: Next Steps

Systems can be smarter. Businesses can be smarter. It is possible to do this by creating the infrastructure required to handle vast amounts of device data. Information systems, algorithms and discovery technologies can then sift through the data, explore it and present the proverbial needle in the haystack.

To do this, organizations must invest in people, processes and technology. However, this is only part of what makes a successful data management strategy. Most organizations fail because they lack the commitment required from management to create a holistic data-centric organization. Managements appreciate the need to mine traditional customer and transaction data. But when its machine and device data, there is a lag. The lag is understandable. The change being forced on organizations by this new breed of data and shifting technology is admittedly difficult to manage.

These modern data systems call for considerable expertise. They need data scientists who understand the complexities of managing real-time data; they require industry and domain experts for hypothesis creation and to write the result sets. Without these, data can often be of no practical value. Worse, it can prove to be misleading.

Organizations must have the focus and energy to adopt the new processes and methodologies. It is necessary for them to integrate information management frameworks and models with technologies such as distributed computing, in-memory computing, Big Data and BI platforms in order to extract value from device data. These technologies allow data to be indexed on the fly. They extract real-time insights using sophisticated analytical engines.

Organizations are tantalizingly close to finding the answers to tough business problems. They know the answers are cocooned in data. For many it is the first steps to be taken that are confusing. “Where do we begin?” is the question we hear most often from those that have woken up to the possibilities presented by device data.

Our suggestion is to use a simple framework to begin the journey. This is what we call the 3B Framework for Adoption:

If you don't have a business case, you don't need to invest in device data capture, management and analysis

Evaluate revenue generation models and cost optimization models

Identify business processes that will benefit from the device data Develop models for ROI and pay-back period

Ensure the foundation is strong before undertaking the data journey

Identify and leverage internal data sources

There is a lot of information all over the place – figure out what is relevant to your business

Identify external and new device data sources to plug gaps Baseline data quality and institutionalize data governance Ensure strict adherence to data privacy rules and regulations

Without the right tools, you will flounder. Ensure investments in the best of breed technology

Identify gaps in your current technology landscape

Identify technologies for data processing and storage, ETL/ELT, visualization and predictive analytics

Carry POTs and select technologies

Leveraging device data to enhance business is inevitable. No business can hope to remain competitive and survive without seriously building on its capability to acquire and mine data. The challenge is to ensure that the journey begins early enough.

Build a business case:

?

? ?

Base-lined data processes: ? ? ? ? ? ? Best-of-breed technology: ? ? ? ?

Disclaimer:The material in this document is provided “as is” without warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, title and non-infringement. The material are subject to change without notice and do not represent a commitment on the part of Wipro. In no event shall Wipro be held liable for technical or editorial errors or omissions contained in the material, including without limitation, for any direct, indirect, incidental, special, exemplary or consequential damages whatsoever resulting from the use of any information contained in the material. The materials may contain trademarks, services marks and logos that are the property of third parties. All other product or service names are the property of their respective owners

07

About the Author

Jayant Prabhu is Wipro Technologies’ General Manager and Global Practice Head responsible for practice management, business & competency development and thought leadership in the areas of big data, data appliances, MDM, enterprise information architecture, data governance, data quality, information lifecycle management, etc. Jayant spearheads solution development in the IM area and provides thought leadership to all IM initiatives. He is also responsible for developing the practice strategy and forward looking initiatives. Jayant leads a team of high performance individuals who are focussed on the leading edge of technology, patent creation, and evangelization. He has over 15 years of IT experience including information management consultancy, data architecture and implementation road map, process modelling, and design and implementation of BI & IM solutions around leading product suites for organizations like Target, Nike, PepsiCo, National Grid , GE, etc.

Jayant also is one of the founders of the Wipro MDM leadership & advisory council, a thought leadership platform that brings together organizations that are or looking at implementing MDM to drive business transformation.

Jayant has also spoken at leading industry forums organized by Gartner, MDM Institute, SAP, IBM and SAS on leading edge topics in the Information, Business Intelligence and Analytics area. He has also co-authored whitepapers in the Information, Business Intelligence and Analytics area.

About Analytics and Information Management Services

Wipro is a leading provider of analytics and information management solutions— enabling customers to derive actionable business insights from data to drive growth, enhance cost management and strengthen risk management. Wipro works with customers to develop end-to-end analytics and information strategy leveraging process assets and solutions based on analytics, business intelligence, enterprise performance management, and information management.

For more information, please visit www.wipro.com/aim

About Wipro Technologies

Wipro Technologies, the global IT business of Wipro Limited (NYSE:WIT) is a leading Information Technology, Consulting and Outsourcing company, that delivers solutions to enable its clients do business better. Wipro Technologies delivers winning business outcomes through its deep industry experience and a 360 degree view of “Business through Technology” – helping clients create successful and adaptive businesses. A company recognized globally for its comprehensive portfolio of services, a practitioner’s approach to delivering innovation and an organization wide commitment to sustainability, Wipro Technologies has over 140,000 employees and clients across 54 countries.

DO BUSINESS BETTER

NYSE:WIT | OVER 140,000 EMPLOYEES | 54 COUNTRIES

North America South America Canada United Kingdom Germany France Switzerland Poland Austria Sweden Finland Benelux Portugal Romania Japan Philippines Singapore Malaysia Australia WIPRO TECHNOLOGIES, DODDAKANNELLI, SARJAPUR ROAD, BANGALORE - 560 035, INDIA TEL : +91 (80) 2844 0011, FAX : +91 (80) 2844 0256 ©Wipro Technologies 2013. No part of this booklet may be reproduced in any form by any electronic or mechanical means (including photocopying, recording and printing) without permission in writing from the publisher, except for reading and browsing via the world wide web. Users are not permitted to mount this booklet on any network server.