Friday, 29 April 2016

Creating new business models through data

By Gloria Lombardi

Big data is, of course, not a new concept. Yet, it is a loose, often confusing, grab-bag term that encompasses many manifestations. Today, it also seems to disappear in favor of SMART Data.   

But, author Bernard Marr beautifully translates this complex subject into something that anyone can understand. In “Big Data in Practice,” he offers insight and real-life examples from some of the most successful businesses of the 21st Century. Through the stories of 45 leading companies, Marr makes the case for delivering extraordinary results out of data. Ultimately, transforming the way businesses work.

With so many use cases, the choice of which story to review was not the easiest to make.
I opted for LinkedIn and Uber since they offer excellent clues on the future of work and communication. Was it worth? Let's see together.

LinkedIn 

I particularly liked the LinkedIn story. Marr wonderfully captures both the benefits and the challenges of the company in leading to big growth through analytics. Big data is key to the way the largest professional social network in the world works. They track every click, page view and interactions of their 414 million members! This, is necessary in order “to ensure their site remains an essential tool for busy professionals, helping them become more productive and successful,” explains Marr.


The company uses machine-learning techniques to make better suggestions for its users, such as “people you may know”. Marr offers an example: “Say LinkedIn regularly gave you suggestions for people you may know who work at Company A (which you worked at eight years ago) and Company B (which you worked at two years ago). If you almost never click on the profiles of people from Company A but regularly check out the suggestions from Company B, LinkedIn will prioritize Company B in their suggestions going forward.” Ultimately, enabling people to build the networks that work best for them.



LinkedIn uses real-time stream processing technology to offer the most updated information. For example, notifications on who started a new job, or useful articles that contacts liked, shared and posted.



Inside LinkedIn


The author gives us an interesting picture of the internal environment that LinkedIn built. They have an impressive team of data scientists who work to improve LinkedIn products and solve problems for members. These employees also publish at major conferences and contribute to the open-source community. And the company encourage them to pursue research in many areas - from  computational advertising to text mining and sentiment analysis.


Marr also describes the changes in the organisational structure. “From a company that employed fewer than 1000 employees five years ago, LinkedIn have grown to employ almost 9000 people. This places an enormous demand on the analytics team. Perhaps in response to this, LinkedIn recently reorganised their data science team so that the decision sciences part (which analyses data usage and key product metrics) now comes under the company CFO, while the product data science part (which develops the features that generate masses of data analysis) is now part of engineering. As such, data science is now more integrated than ever at LinkedIn, with analysts becoming more closely aligned with company functions.”

Marr considers the hiring challenges too. In 2015, the company were looking to hire over 100 data scientists, a 50% increase from the previous year. But, competition is tough even for a giant like LinkedIn. And, “although more people are entering the field [of big data analytics], it's likely this skills gap – where demand for data scientists outstrips supply – will continue for a few years yet.”

Transparency of communication

Throughout the book, the author makes the case for transparency of communication when using individuals' data. For example, he describes the privacy backlash that LinkedIn faced last year. “In June 2015, the company agreed to pay $13 million to settle a class action lawsuit resulting from sending multiple email invitations to users' contact lists. As a result of the settlement, LinkedIn will now explicitly state that their “Add Connections” tool imports address books. And the site will allow those who use the tool to select which contacts will receive automated invitations and follow-up emails.”

Indeed, we can see this example as a frontier for hope. LinkedIn offer a key lesson to businesses to be clear about what data they gather and how they intend to use it.

Uber

How is big data impacting on the workforce? The Uber story is a fine observation on the changing nature of work. In particular, the on-demand economy.

Big data is at the centre of Uber's disrupting transportation business. And, Marr believes that “without their clever use of data the company wouldn't have grown into the phenomenon they are.”


Uber hold a vast database of drivers in all the cities they cover, which allows them to instantly match a passenger with the most suitable driver. Their algorithms check traffic conditions and journey times in real-time. This allows rides' prices to adjust as demand changes. And, it "encourages more drivers to get behind the wheel when they are needed – and stay at home when demand is low,” explains Marr.

Data drives Uber's detailed rating system too. Passengers can rate drivers, and vice versa. This should build trust and let both parties make informed decisions about who they want to share a car with. But, the author points out that drivers have to be conscious of keeping their standards high. In fact, “falling below a certain threshold could result in their not being offered any more work,” he writes.

Another work-related metric is the “acceptance rate.” This is the number of jobs that drivers accept versus those they decline. They should maintain this above 80% to provide a consistently available service to passengers.

Challenges

Indeed, the author acknowledges the controversies that Uber had and still is facing. Most notably, regular taxi drivers who claim they are stealing their jobs. And, concerns over the lack of regulation of the Uber's drivers. Uber responded to taxi drivers' protests with the attempt to co-opt them. They added a new class to their service called UberTaxi. This lets people use a licensed taxi driver in a registered private hire vehicle.

There are also legal hurdles for the company to overcome. Their service is banned in some parts of the world. And it is receiving scrutiny in other regions. But, their popularity across the world is a huge incentive for them “to press ahead with their plans to transform private travel.”

Is it worth reading?

It is refreshing to read a book whose author simply puts the big data hype into practice. Ultimately, it offers a comprehensive narrative of why and how data is transforming the way businesses operate.

What makes the stories of LinkedIn and Uber particularly interesting, thanks to Marr's detailed narrative, is just how complex the relationship between work and data looks today. Backed up with some indiscernible fusion of technology, humanity, business, and society.

But, those are just two examples. After diving into “Big Data in Practice” you will gather diverse insights. Somewhat differently, all the companies that the book lists have created new business models. And, they gained a competitive advantage.

Indeed, today we are seeing the emergence of other related innovations such as the Internet of Things (IoT), Artificial Intelligence (AI) and robots. These technologies are already impacting on the use of data, perhaps making the phenomenon even more important.

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Picture of Linkedin office courtesy of photopin 
Picture Uber Taxi courtesy of Pedro Caramuru