How design thinking reveals the value of big data

A shitload of data but no insight?

A shitload of data but no insight? How design thinking reveals the value of big data.

When used properly, big data can be a huge asset to companies. Smart use of big data can help select the best job candidates (within the limits of new privacy regulations), set up the perfect marketing campaign or improve healthcare and public health. The biggest challenge however, is dealing with datasets; how to work with abundant data that can often be misleading, contaminated or badly structured? IBM data scientists use ‘the four v’s’ – veracity, velocity, variety and volume to analyse data. But we believe there is a crucial ‘fifth v’: value. How do you turn big data into valuable data?

Decisions made using big data algorithms are based on neutral facts and objective calculations. But, according to Harvard Mathematician Cathy O’Neil this in itself can be a dangerous assumption. In her book ‘Weapons of Math Destruction’ the biggest concern is that very few people can understand and monitor the algorithms used (O’Neil, 2016). Why? Because only people who work with and understand algorithms can judge them or the data that has been used.  In short, the veracity of the data becomes uncertain. How do you decide what data to use in a transparent way?

Case Study

The usefulness of big data can be seen in numerous practical applications. The control of public services by Dutch municipalities is a good example. Several years ago underground garbage containers were installed in city centers throughout the Netherlands to reduce the need for large garbage trucks to drive through the narrow streets. The goal was simple; fewer garbage trucks. However, if containers are not emptied on time, garbage is left on the street – a nuisance and issue for city cleanliness and the people living there.

In theory, this problem could be solved using big data. Sensors could be installed in the containers indicating when they are full. Using this data, a preventative maintenance model could be built to predict the appropriate emptying schedule – avoiding the overflow of garbage.

However, issues can arise with what seems to be a simple solution to the garbage problem. The data collection and analysis (often by a third party) itself can be an obstacle by presenting the municipality with a new set of problems such as variabilities of time, or weight versus volume. The result: many cities must still deal with regular occasions of garbage on the streets due to containers being full when the data suggests they should not be; leaving the initial problem of when to collect the garbage unresolved. Obviously, this can be discouraging for organisations relying on big data for problem solving.


One Problem, One Hundred Solutions

This is where design thinking comes in. Understanding the veracity, variety and volume of big data for specific cases is vital. However, when faced with data sets, how do we know which data is needed to create the best solution? How should it be collected? When should it be collected and who should it be accessible to?

Recently Ink Strategy organised a round table to engage municipalities in exploring, determining and addressing the problems around their use of big data. We effectively combined the municipalities’ domain knowledge with external academic expertise by Dexter and AMI on the latest developments on the topic.

Design session with Dexter, AMI and Dutch municipalities to create valuable use cases

By visualising the problem, process and proposed solutions we provided quick, clear answers to the above questions. Together we addressed ‘the four v’s’ of big data by designing a clear vision and resulting visual. Therefore, addressing the crucial ‘fifth v’: creating a clear path to real value with big data.

So, do you want to create value with big data through design thinking and visualisation? Contact us to discuss further.