How 10 industries are using big data to win big

Two and a half quintillion bytes or 2,500,000,000,000,000,000 bytes. That’s how much data humanity generates every single day. And the amount is increasing; we’ve created 90% of the world’s data in the last two years alone.

It should come as no surprise, then, that businesses today are drowning in data. That’s because much of that data is unstructured; it takes the form of documents, social media content and other qualitative information that doesn’t reside in conventional databases and is can’t be parsed by traditional algorithms or machine analysis.

But, thanks to new cognitive computing services, that’s changing fast.

New Tools, New Insights

Cognitive services not only cut through the deluge of data, but also bring meaning to it through human-like understanding of natural language queries. They’re helping businesses across a broad range of industries respond to the needs of their customers like never before, driving increased revenue while reducing costs.

The industries boosting bottom lines and setting new standards for customer service include telecommunications, manufacturing, fitness, retail, insurance, banking, finance, government, healthcare and the travel industry.

Here’s an overview of how these industries are making all of their data work for them:

1. Telecommunications

A major telecommunications service provider uses cognitive services to index thousands of documents, images and manuals in mere minutes, in order to help 40,000 call center agents solve customer issues more effectively. The company realizes a savings of $1 for every second shaved off the average handling time per call—or $1 million a year.

2. Manufacturing

A specialty sports manufacturer was challenged to fine-tune production in order to eliminate inventory overruns and create better products while saving money. Now, thanks to newly accessible data, the company produces 900 different kinds of skis to match customer personality, preference, and snow conditions—giving customers exactly what they want and meeting the company’s goals for lower inventory and expenses.

3. Retail

Data-driven, personalized customer experiences enabled by cognitive technology are helping a major clothing retailer not only provide outstanding service, but also drive revenue at more than 225 stores. To design a better in-store experience, the company uses sensor and Wi-Fi data to track who comes in, what aisles they visit and for how long. The company also analyzes social media data from millions of followers to improve marketing and product design.

4. Fitness

Cognitive services aren’t just for customer service agents and manufacturers; they can directly serve customers, too. A sports apparel and connected fitness company uses data to power the world’s first cognitive fitness coaching mobile app. The app collects data on users’ workouts, calories burned and more in order to act as a “virtual coach” to help them meet their health goals.

5. Insurance

An international insurance company uses cognitive services to reduce the time needed to process complex claims from two days to just 10 minutes. The company is also using data to identify and eliminate hundreds of millions of dollars in fraud and leakage. The result: a more customer-centric and profitable company.

6. Banking

A consumer banking chain in New Zealand is using data collected and analyzed by cognitive computing to more than double customer engagement online—from 40% to 92%, with a 30% increase in online banking. With a view into customer sentiment as well as data on revenue generation per product held, agents can provide more personalized customer service. Customers can log on to mobile devices to perform more than 120 functions, including applying for a mortgage. As a result, mobile usage is up 45%.

7. Finance

Cognitive technology is empowering the financing arm of a major auto manufacturer to develop insights about more than four million individual customers in seconds. The system combines unstructured content and conventional data from internal and public sources, then displays all meaningful information based on a the user’s job description. Agents can thus provide customers with more comprehensive information faster, and the company can maintain data security.

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8. Government

A U.S. state government is using data to enhance the services delivered to millions of its citizens. Cognitive services enable citizens to quickly and easily search hundreds of thousands of documents, including important new insurance requirements. This allowed the state to achieve its goal of helping citizen navigate more than 1 million pages, while saving tens of thousands of dollars in upgrade costs.

9. Healthcare

A large healthcare company is using data and cognitive computing to extract key insights from unstructured patient medical history—including physician notes and dictation—covering 1.35 million annual outpatient visits, 68,000 hospital admissions and 265,000 emergency room visits. The trends, patterns and other important information captured from this data help clinicians identify patients at risk for chronic disease, critical to both improving treatment and reducing readmission.

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10. Travel

An international airline has found a way to use cognitive services to significantly enhance the customer experience. Flight crews now use mobile devices to access customer data, including allergies, food and seat preferences and previous travel history to offer truly personalized service. To show its customers that it values their information, the airline has launched a first-of-its-kind customer insights program that rewards those who share data by offering them airline miles.

A Vital Competitive Advantage

As the data deluge grows day by day, it presents greater opportunities for companies to drive more personalized and customer-centered service while boosting revenue and efficiency. But these actionable insights will only be available to companies that leverage advanced data analytics and cognitive computing to collect and parse unstructured data. Those who fail to take advantage of cognitive services risk getting left behind.

To learn more about trends in data and cognitive computing download IBM’s Evolution of Enterprise Search Webinar series. 

Personal Finance Application

Make Personal Finance fun again by automated segmentations, benchmarking gamification and automated machine learning budgeting tool.



ELA AS is newly founded startup with a clear mission to make use of data to benefit humanity. #DataForGood is our core value and a hashtag of our activities in social media. We have started with four projects, but we hope to continue with some more:  

1. Personal Finance Digital Assistant which is an add-on solution to your digital bank account that gives you better picture of personal finances based on bench-marking against predefined data set of your segmentation (ex: income range, family members, region you live etc.) and fully automatized data input for all segments: cost, income and balance. This will include a machine learning (ML) algorithm that will suggest you the best way to save and invest money, how to overcome a financial difficulty and how to perform in budgeting your economy the best way.  

We will soon be in Kickstarter and I hope that you will support our project!

Another approach to Personal Finance

Re-Inventing Personal Finance using Data Science



Existing software and new approach

Usually existing Personal Finance applications are boring, because they are all dependent of manually input of your data, in right segment, the right amount, just boooring. In addition, you can count on manual input fails together with the impossibility of live update your financial status, to make it even worst experience. These and many other reasons make the existing Personal Finance applications nearly useless.

To avoid manual input of data into your application, you need a live feed from your transaction data (credit card usage, bank payments etc...) and only manual input for cash amounts. However, cash is very small problem, as we tend to avoid it as much as possible and instead we mostly buy with electrons.

Most of the banks offer to their customers a digital bank account where all the transactions are visible and that can the best source to avoid manual input. So, why we do not ask for built-inn application that will serve as Personal Financial app with even more possibilities to serve you.



The solution

This application can save lives, can make you better at your personal finance, can avoid financial crisis and help banks get better understanding of you as customer. It is not only you as a person that benefits, but the entire society and even the bank itself. Bank can have much better credit scoring for their customers and can avoid risky loans, risky bank interests for a particular customer etc…

To build (in) this app we need to consider many things and specially the approach that Business Intelligence solutions can serve to us, but keeping in mind security and impersonation as we work with very critical data.

Therefore, I am delighted to represent you PFI that stands for Personal Finance Intelligence, which represents a non-usual approach to Personal Finance solutions existing in market today.

Personal Finance Intelligence (PFI) aims to be a built-in Business Intelligence application inside your digital bank service to serve you as personal finance and budget planner assistant.

Inspired by a Norwegian TV Show “Luksusfellen”, this Business Intelligence app approach may be a solution for all these who fail to maintain well their own economy, and for those who want to perform their economy, save more and last but not least the bank itself.
The fundament of this concept is a Customer Analytics Data Center that would have the power process data on the transaction level. The duty of the data center will be to collect, structure, clean, model and present the data to the bank customers as usual Personal Finance application do, but in addition, data will be updated automatically. This is the reporting (presentation) layer of your financial status (picture), but this application can offer you much more and here is why!

In addition to a standard PF application, this solution include also bench-marking against an standardized customer (Ola Norman) that represent the data set of Min, Max or Average segmented by customer's choice and properties, for example: How I stand against customers from 28-35 years old, from east Oslo, in buying food and beverage this month?
To have more control and plan well your own economy, targeting will be an integrated service inside application where users (bank customers) can put their targets (manually) for costs or income or can leave the application algorithm fill that with projection based on each customer historical data. You can activate a flagging service, so you are warned when approaching certain limits in your expenses and run algorithm to optimize the use of remaining budget and you do not get broke.

Big Data can help make it even better

Last technological findings can make this approach even more interesting and meaningful. Imagine what Big Data and Data Science can do by adding external data for customers that will allow opening of their social media accounts to the bank application. Social media behavior is very important and can bring very important segmentation inside customer categorizations.

Machine learning algorithms can help make the decision and budgeting much better based on other decisions and budgeting techniques.



Considerations

As I mentioned before, data impersonation and security are a showstopper as we are going to work with bench marking data sets that implies set of other customer’s data. Here we can have a potential data leak from one customer to another, so our system must ensure consistency in both sides and the bank system has everything under control. Transaction details of customers can make banks expose their ‘hidden’ costs and fees. Many banks will hesitate to offer this service to their customers just because of this; in other side customers have legitimate right to have such information.

Conclusion

Beneficiary to this approach are not only the customers and world economy, but also the bank itself in cases when they want to perform customer evaluation (credit check) and behave reaction to certain financial statuses. Today’s Credit scoring system lacks on better decisions because they miss important data.

I am on the way to build business concept and the technical architecture of this approach. My team and I would love to share this approach on details, including implementation, if any company, association or bank in the world is interested to offer this service to their customers. This can be the best preventive for World financial system to stay sustainable and not crush as it did before.



© Copyright All rights reserved to Besim Ismaili 03051982



Oslo, January 2015