MINING DIGITAL GOLDHOW COMPANIES ARE FINDING NEW

WEALTH IN THEIR DATA

Consider the mammoth valuations given to companies like Facebook and Snapchat, or the current M&A boom driven by businesses scrambling to get their hands on as much customer data as possible.

In Europe alone, M&A activity hit $215.3bn in the first quarter of 2017 according to Thompson Reuters, fuelled in large part by established players looking to gain access to more customer data and add market-leading algorithms to their portfolio.

The number of data sources available to businesses is still multiplying. Our smartphones, energy meters, kitchen appliances, and even our clothes all leave breadcrumb trails of digital insight that companies are using to inform their strategies and tailor their offerings.

Data is also changing how businesses operate at a fundamental level. Research from Forrester1 reveals many organizations have reduced their bottom-line by examining their operational data and uncovering ways to work more efficiently.

This is typically the first stage of a data journey – the next steps are to collect all data in its many forms, analyze it, and then use it to deliver top-line benefits to both customers and the wider business. What do these benefits look like? In this report, we explore how businesses are using data to their advantage.

From manufacturers and utility suppliers that embed predictive maintenance into their processes so they can pre-empt failures, to governments that use smartphone geo-data to build smarter traffic models,

the possibilities of modern analytics are coming to light across virtually every industry.

And we are only just getting started.

1 Going Big Data? You Need A Cloud Strategy, Forrester, January 2016

Following digital breadcrumbs

Data is the currency of the future. Physical assets like oil, precious metals and cash may still account for a large part of today’s economy, but data is proving to be the real gold for business.

Oracle’s big data strategist, Paul Sonderegger, speaking to The Economist

Data will be the ultimate externality: we will generate it whatever we do

Today’s manufacturing businesses collect a wealth of data from Internet of Things (IoT) sensors installed in their products and across their factories, and are now embedding algorithms into their processes to uncover the warnings signs of costly failures before they occur. For now, this is largely limited to analytics on production quality.

Companies are collecting a range of data to build correlations between variations in processes and product quality. With enough historical data, they will soon be able to create models that can accurately predict failure.

Smooth operators

One of the quickest wins for analytics has been in the field of predictive maintenance.

This simple analytics-based demo of a chocolate factory shows how predictive maintenance works, whereby a system is designed to spot and pre-empt the deterioration of industrial components before they cause a complete failure.

View demo

IoT Sensor

Analytics

DatabaseInsight

15.38

GASHOME SETTINGS

02.06.17

The impact of predictive maintenance isn’t limited to manufacturing.

Researchers at CERN, one of the world’s most ambitious and costly scientific endeavours, understand that any fault could lead to unreliable results and expose them to massive public scrutiny. That is why they rely on predictive algorithms to ensure

the Large Hadron Collider, and every industrial component involved in running it, operate smoothly.

For their part, utility suppliers are in the process of rolling out smart meters, effectively turning the energy grid into a network of IoT-connected nodes that constantly generate data on how electricity is flowing across the network.

Just as manufacturers use IoT sensor data to better manage their processes, utilities will be able to use smart meter data to spot potential outages before they occur and ensure they deliver a reliable energy supply to their customers.

The use of analytics to predict failures is also benefitting consumers, particularly drivers.

General Motors tracks telematics data from its latest model vehicles and flags impending service issues to drivers before they develop into bigger problems.

One of America’s largest dealership groups monitors vehicle health through IoT sensors and proactively schedules service appointments.

ELECTRIC HISTORY

Retailers have been quick to benefit. Most brands tend to sell through third-party shops and e-commerce sites, which means the customer data they collect comes in multiple formats and is difficult to work with. Today’s analytics platforms allow them to consolidate all forms of structured and unstructured data so they can build an accurate customer view that is constantly updated.

CaixaBank works with a centralized view of all its customer data to gain a better picture of how people interact with its services in-branch, online and on their mobiles. This allows them to provide customers with more tailored and relevant services across every platform.

Nespresso collects data from IoT sensors in its coffee machines to see which capsules each customer favours and gain insight into their drinking habits. With this information they can then personalize their offerings to each person’s preferences.

Telecoms and media providers are also taking advantage of analytics to personalize their offering. Companies including BT, Virgin, and

Telefonica now build a 360-degree view of their customers based on network and social media data so they can tailor their services and offerings to each person’s usage habits and preferences.

This complete customer view also allows companies to track customer satisfaction levels, which is crucial to retention. De Persgroep has been using advanced analytics to predict customer churn with 92% accuracy, which has helped the company to understand the root causes of people’s dissatisfaction and address these.

Utility providers track energy meter data to help customers make smarter energy decisions that will also help them save money. In markets like these, where service differentiation is hard to prove and customers are notorious for switching providers, being able to build strong customer relationships is the key to retaining market share.

There are some industries, like banking and insurance, where advanced analytics is helping businesses to avoid fraud, or at least react more quickly to fraudulent activity.

Getting closer to customers

Gaining a deeper understanding of customers is arguably the number one priority for any sales organization, and data analytics is ideally suited to this endeavour.

Cracking down on fraud

This not only benefits their bottom line but also ensures their customer’s sensitive information is kept safe.

Traditional fraud detection systems were built on simple rules and could only work with straightforward structured data, making them rigid and ensuring they would quickly become dated. In the case of an insurance company, not being able to integrate geo-location data or social media activity in their analyses today limits their ability to verify claims.

Insurers can now analyze all forms of data together and spot correlations that were never before visible. For example, they might notice that a customer who is already under suspicion has a tendency to call the same phone number after making a large claim, suggesting their activity should be watched even more closely.

According to Morgan Stanley2, a more advanced analytics approach can help insurers improve the detection rate of fraudulent claims by 30%.

2 Insurance and Technology Evolution and Revolution in a Digital World, Morgan Stanley and Boston Consulting Group, September 2014

Other industries are following suit. StubHub has reduced online fraud by 90% with advanced analytics capabilities. The British National Health Service (NHS) has implemented a billing and identity fraud system that helped it uncover roughly £100 million in potential savings in just three months.

This approach is particularly valuable for telecoms providers, who can lose vital revenue to leakage and fraud. Turkcell, Turkey’s leading phone operator, upgraded its analytics systems

and can now detect fraud among prepaid customers in real-time. This includes the detection of suspicious mobile money transfers used for money laundering, a task which previously took the company up to four hours.

In today’s connected world, any loophole in a telco network can be misused by millions of customers and lead to dramatic revenue leakage. The ability to score customer and network events in real time is therefore invaluable in detecting, preventing and quickly

adapting to ever-changing fraud scenarios.

Mobile operators are even taking their data one step further and helping public sector agencies to save lives. By providing law enforcement with a combination of rich network data and social media and video surveillance data, operators are putting them in a position to detect and prevent crimes or acts of terrorism.

Telecom providers are also helping businesses to secure their intellectual property and

data integrity. Data security and governance have never been more important, particularly with the new EU General Data Protection Regulation soon placing much harsher requirements on the way businesses must handle and protect EU customer information.

A robust fraud detection and monitoring system will help companies stay one step ahead of any threat.

£100m

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The future of analytics is about openness and the sharing of data between industries. Just as telecoms providers share user information with law enforcement agencies, retailers have a great deal to gain from real-time insight into customer habits. Today, they use geo-location and customer footfall data to position their stores and plan sales around people’s shopping activity.

This more targeted approach also reduces the cost of media acquisition. The exchange is mutually beneficial, as it allows the operator to curate the right content for its own customers and provide them with a better user experience.

The democratization of data is also serving a higher societal purpose. Cities are beginning to use geo-data from telecommunications networks

to inform and build better traffic models and public services. As smart meters are rolled out more widely, utility suppliers will be able to monitor car telematics data to better manage electric vehicles, which are expected to place major pressures on the grid as they become more popular.

Even emergency services can look at how telematics data can help

them respond to accidents more quickly. This simplified model race car demo shows how response systems can be linked to in-car telematics to instantly see when and where a crash has occurred, how many people are involved, and how serious the damage is. With this information, they can dispatch rescuer teams right away and ensure they arrive with all they need to help those involved.

Data exchange and monetization

Telefonica provides anonymous audience insights to advertisers and content providers so they can better tailor their content to individual prospects.

Telefonica has now captured 30% of Spain’s lucrative digital media and advertising market. By comparison, most telcos only contribute to roughly 2%3 of the advertising value chain.

3External Data Monetization: CSPs Should Cautiously Invest In New Service Offerings To Increase Revenue, Analysys Mason, June 2016

30%

The success of any analytics-based program will depend on the quality and relevance of data being used. It is not enough to simply collect information – all the data in the world has little value if it is just sitting idle in spreadsheets or in a database.

Big data analytics presents a range of new opportunities, but while companies collect an exponentially larger volume and variety of information they still struggle to consolidate and convert it all into a useable format. Forrester Consulting believes ‘this bottleneck renders the Big Data opportunity a mirage.’4

This begins with data preparation. To run relevant analyses, companies need to feed accurate and relevant information into their processes. Encouragingly, businesses have recognised this and are investing in data preparation technologies. 66% say they have implemented a data preparation or wrangling solution to manage a growing volume of data, and 56% have done so to help them work with multiple data sources.

The ownership of analytics projects is also moving away from the IT department to line-of-business (LOB) heads. According to Forrester, LOB leaders are now the primary decision-makers in the selection and use of data preparation solutions.

However, they still rely on IT for their data preparation expertise and, due to departmental silos, it still takes weeks for IT to turn around requests. This is another advantage of data preparation systems: they help organizations to dislodge this bottleneck. Indeed, 92% of agree their preparation technology has contributed to improved collaboration between lines of business and the IT department.

Finally, there is a strong regulatory driver for companies to gain more control and oversight of their data. When the EU’s General Data Protection Regulation goes into effect in May 2018, companies will face harsh penalties if they are not transparent about the way they collect, use and share customer information.

It all starts with data preparation

4 Data Preparation Accelerates Self Service, Forrester, August 2016

66%

56%use preparation to handle more data sources

To run relevant analyses, companies need to feed accurate and

relevant information into their processes:

use preparation solutions to managea growing volume of data

Many businesses are still adjusting to the shift from tangible wealth, which is easy to quantify and exchange, to data-based wealth, which must be teased out of their processes and manipulated into valuable insights.

However, across financial services, insurance, telecommunications, utilities, retail and many other industries, leading companies have set the early pace for success in a data-led market.

The pace of progress will only quicken as more organizations tap into the potential of their information, and the benefits to businesses, people and society as a whole will dwarf even the major wins we have seen to date.

The world runs on data

From data scientists and analysts, who work closely with company data each day, to business leaders exploring new ways to improve the way they work, Oracle has a set of rich integrated solutions for everybody in your organization.

For more information on how Oracle’s Cloud Platform for Big Data helps companies uncover new benefits across their business, read our eBook at:

www.oracle.com/goto/transform-with-big-data