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. 

Laptop for data science

What are the laptops which are most suited for data scientists and analysts?

As we deal with heavy computations and also need to generate visualizations, something which can take the toll of it, would be recommended.

Would be preferred if it can help in handling Big Data analytics too.

Even though the analytics is done in the Map Reduce framework (or distributed computing), yet the computations are heavy and time taking and also slows down the laptop in most cases.

So, a laptop with features and OS which is most suited to handle such things gracefully is recommended.

[Price not an issue]

As I am pretty much in the same situation, here are what I look for:

SSD: since you'll likely perform many I/O on large data sets. 1 TB is my bottom line.
RAM: since it's often more convenient and much faster to keep data sets (or part of it) in memory. 16 GB is really bottom line.
GPU: Nvidia is sometimes preferable over AMD as it tends to be more supported (e.g. for neural network libraries). I had to get a MBP M2014 instead of M2015 because the latter had AMD while the former has Nvidia, and I need to use Theano.
OS: Linux tend to have more libraries (but since it doesn't have any decent speech engine software I personally use Microsoft Windows, using Linux in VM or in server).
CPU: hasn't evolved much over the last few years... some i7 3rd or 4th generation is standard.

As it's often cheaper to add SSD and RAM oneself, I tend upgrade mid-spec laptops.

If price isn't an issue, you can have a look at those overpriced Alienwares. For people who are more budget conscious, just check to what extend the laptop is upgradable (e.g. max RAM + number of SSD slots). In the US, I like Xotic PC as the max specs are clearly defined.

The Data Science Process 1/3

Congratulations! You’ve just been hired for your first job as a data scientist at Hotshot Inc., a startup in San Francisco that is the toast of Silicon Valley. It’s your first day at work. You’re excited to go and crunch some data and wow everyone around you with the insights you discover. But where do you start?

Over the (deliciously catered) lunch, you run into the VP of Sales at Hotshot Inc., introduce yourself and ask her, “What kinds of data challenges do you think I should be working on?”

The VP of Sales thinks carefully. You’re on the edge of your seat, waiting for her answer, the answer that will tell you exactly how you’re going to have this massive impact on the company of your dreams.

And she says, “Can you help us optimize our sales funnel and improve our conversion rates?”

The first thought that comes to your mind is: What? Is that a data science problem? You didn’t even mention the word ‘data’. What do I need to analyze? What does this mean?

Fortunately, your mentor data scientists have warned you already: this initial ambiguity is a regular situation that data scientists in industry encounter. All you have to do is systematically apply the data science process to figure out exactly what you need to do.

The data science process: a quick outline

When a non-technical supervisor asks you to solve a data problem, the description of your task can be quite ambiguous at first. It is up to you, as the data scientist, to translate the task into a concrete problem, figure out how to solve it and present the solution back to all of your stakeholders. We call the steps involved in this workflow the “Data Science Process.” This process involves several important steps:

• Frame the problem: Who is your client? What exactly is the client asking you to solve? How can you translate their ambiguous request into a concrete, well-defined problem?
• Collect the raw data needed to solve the problem: Is this data already available? If so, what parts of the data are useful? If not, what more data do you need? What kind of resources (time, money, infrastructure) would it take to collect this data in a usable form?
• Process the data (data wrangling): Real, raw data is rarely usable out of the box. There are errors in data collection, corrupt records, missing values and many other challenges you will have to manage. You will first need to clean the data to convert it to a form that you can further analyze.
• Explore the data: Once you have cleaned the data, you have to understand the information contained within at a high level. What kinds of obvious trends or correlations do you see in the data? What are the high-level characteristics and are any of them more significant than others?
• Perform in-depth analysis (machine learning, statistical models, algorithms): This step is usually the meat of your project,where you apply all the cutting-edge machinery of data analysis to unearth high-value insights and predictions.
• Communicate results of the analysis: All the analysis and technical results that you come up with are of little value unless you can explain to your stakeholders what they mean, in a way that’s comprehensible and compelling. Data storytelling is a critical and underrated skill that you will build and use here.
   

So how can you help the VP of Sales at Hotshot Inc.? In the next few emails, we will walk you through each step in the data science process, showing you how it plays out in practice. Stay tuned!