- Global and Strategic Marketing Research Centre
The exponential growth of data is making it a hot topic at the moment. For all the work we do with data, rather than just thinking about how we can collect data and what we can collect it on, the question that we should really be considering is “is my data valuable?”
What is value then?
There are different types of outcome from using data which can have different values:
1. Optimisation – The new insights can allow us to identify the inefficiencies in the current processes. For example, knowing the estimated arrival times of a taxi and a train for a specific journey at this moment in time can allow us to optimise the journey time.
2. Elimination – Many maintenance processes are carried out to minimise risks of failures due to the lack of real information about the current health of a machine. New data from sensors can eliminate processes and improve the client experience.
3. Transformation – processes and services have evolved over many years based on the best systems and knowledge that existed. However, new data can allow those processes to be transformed to achieve new levels of performance. The faster payments is a very good example here - the reconciliation processes typically took five working days for monies to transfer from one bank to another. Digital representations now allow this process to be transformed and allow payments to complete within a few seconds.
Below are some examples of how data has been used to add value in different sectors:
We work with a rail company in the US that runs large trains along the East to West coast. The biggest issue with running these trains is derailment, and the biggest cause of derailment is cracked wheels.
Derailment doesn’t just cause disruption – it destroys valuable goods and can potentially kill people.
Previously, an engineer would use a hammer to tap on the wheels and listen out to hear if the wheels are likely to crack on a 1,000 mile journey and then make the decision on whether the train was safe to leave the station or not.
We investigated ways we could make this testing more efficient, collecting and analysing data. We put sensors that capture the noise from the train on to all the carriages. The noise is analysed through very complex systems which understand whether the noise is due to the wind, the engine, other factors, or whether it is the noise of a cracked wheel.
This helps not only predict the likelihood of a wheel cracking but can also inform the driver if the train needs to stop due to the cracked wheel increasing the risk of derailment.
Big data can also help stop crime before it happens. Using social media data, crime statistics and data from different agencies (such as home land security and the precinct) the New York Police Department is able to use this information to determine where and when crime is likely to happen so that they can increase the police presence in those places.
By mining the data which was used to inform the behaviour of the police officers, it brought down crime rate in New York by a massive amount.
Working with ASB bank in London, we were tasked with reimaging the way information is captured about customers wanting a loan.
A competition was set up on Facebook asking participants to write why they want a loan. They were then encouraged to get friends to “like” the post on Facebook. The more “likes” the post received, the lower the interest rate ASB offered. The person who received the largest number of “likes” got the lowest loan.
Every month, ABS receive around 30,000 stories of why individuals want a loan. The winner potentially gets a loan for the next three years, interest free and ABS receives a massive amount of valuable data. The cost for them to offer this is minimal, but the value from getting those stories is phenomenal. You would never get that depth of detail from marketing research. This is an example of how the data industry is starting to transform the way we think about the value of the data.
Data isn’t new – if we go back to the 17th century, Galileo looked down his telescope and started to gather data on the solar system. He used this data to not only map the stars and the planets, but to reimagine the world.
Skipping forward to the modern day, we should still be using data to allow us to understand the world differently. Often, we don’t think about the way in which data becomes valuable. It’s only when we start to process the data that we understand it and can make use of it. Starting with the raw data you capture, you then do some analysis in the form of logic or algorithms. This then provides information which can be seen as valuable.
Data processing: Slide taken from Rashik Parmar’s presentation on “Is my data valuable?” at the Big Data conference in Leeds
This is where IT and similar industries, such as manufacturing, have been for the last 50 years or so. Just like the process of taking raw materials and manufacturing products, data has to go through multiple stages of transformation before it becomes valuable. In the past we relied on the knowledge or experience of an expert to be able understand the implication of the information and determine the right actions. Advances in IT have created a new class of systems referred to as context systems. An electronic representation of a roadmap is a good example here.
Combining information with context gives us insight, but insight alone is still not value. It is only when you add actions to the insight do you get a valuable outcome.
We’ve previously been reliant on humans to discover the insights from the information and in a particular context. However, this process is starting to become more automated which creates much higher levels of productivity.
Results: Slide taken from Rashik Parmar’s presentation on “Is my data valuable?” at the Big Data conference in Leeds
Thinking about the value of data also requires us to be ethical as well as innovative. For example, there is a famous case where the Target group, a discounted retailer in the US, identified a teenager as being pregnant and sent pregnancy material in the post to her home, which her parents (who were unaware of her pregnancy) saw.
As another example, a lot of people have their mobile phones on whilst travelling in their car. That data is being used by tele companies to understand where you are. Some of the tele companies are selling that information as traffic data to GPS providers. That data was subpoenaed by the Amsterdam Government to decide where to put speed-cameras. So how long is it until our Government uses our mobile phone data to determine whether we’re speeding or not and automatically issue a fine?
To get the most value out of big data, we need to continue to be innovative. We need teams of people with mixed skills and characteristics to help create valuable outputs from the data:
· Gatherers – to capture the data
· Visionaries – to understand the big picture and how our world can be different if only we could capture the data
· Theorists – to develop the algorithms to manipulate and transform data
· Engineers – to build the frameworks and solutions
· Righteous workers – to question and evaluate whether it’s the right thing to do.
Using the right kind of people and right kind of skills, we can continue to find innovative ways to not only collect data, but to apply context, gain new insights and combine this information with actions that result in something valuable that benefits business and society.
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The views expressed in this article are those of the author and may not reflect the views of Leeds University Business School or the University of Leeds.