By Hannah Evans
Big Data Week London 2015: a festival of social data around the globe. Fantastically organised and executed by all, the event brings together 400 speakers in 40 different global cities to provide a series of workshops, training, user groups and conference to business and technical audiences in this unique week-long celebration. This year, the main conference in London was split into two ‘tracks’; a business and tech track. Recurring themes were threaded through many of the presentations; here are my reflections from the business track. Look our for part 2 for reflections from the tech track too.
1. Data = THE BIGGER THE BETTER
“Big” data is all the rage. But what if you don’t have enough of it? John Abbey from customer science giant Dunnhumby explored the different methods of increasing the datasets of your organisation. You can either collect it yourself, partner with another organisation or acquire another company entirely. However each method has associated benefits and challenges. If you collect it yourself, you own the whole relationship with the data and customer and have the power to evolve the data in the future, however this takes time, investment and skilled resources to do successfully. Alternatively, you can engage a data partner – this is a fast way of accessing unavailable data, however can dilute margins and cause inefficiencies along the way. Or why not acquire another company? This is quick and can give increased competitive advantage, however is also high risk, high cost and poses integration challenges.
2. Innovation = FAIL FAST, SUCCEED FASTER
Once decided you could benefit from more data, you need to find innovative technologies to collect it. Jim Anning from British Gas highlighted that big data is all about the technology decisions you use to handle your datasets; and these technology decisions need to be innovative. He provided an in-depth and fascinating insight into their “Connecting Homes” initiative. The Hive thermostat, designed by Apple and owned by British Gas, was designed by an ‘innovation’ team space in a basement in Soho. The project, of which the Hive Thermostat is a part, allows users to control and understand heating consumption using their mobile phone. Anning emphasised that it is essential to experiment often and consciously fail and urged organisations to create a culture where it is actually acceptable to fail – the alignment of innovative data scientists and artful data engineers is a key aspect of achieving this.
3. Visualisation = HIDDEN INSIGHTS
Collecting big data in innovative ways is great; but only if you also do something great with it afterwards. Technology platforms are allowing data scientists to unleash creativity from large datasets – allowing technical stakeholders to present huge insights to their business counterparts.
Roland Major, Enterprise Architect from Transport for London (TFL) presented the recent success of the Urban Traffic Data Hack. TFL are collecting sensory data to produce visual maps which assist urban traffic control – providing evidence of vehicles on roads at any particular time, when you are able to get a seat on the tube as well as the useage of stations to understand how they operate in combination.
Andy Cotgreave from Tableau demonstrated a visual analytics tool which allows them to iteratively explore different views of data to refute or validate predictions. Andy used an example of mobile phone meta data – using a visual analytics platform, a number of assumptions about a particular mobile-phone user could be concluded. He reinforced the idea that people are smart – but computers are tools that should be used to augment our intelligence and our creativity.
Nick Henthorn from Telefonica presented with Mick Ridley from Exterion Media. Telefonica has assisted Exterion to produce targeted advertising campaigns based on their customer profiling metadata. From this, Telefonica and Exterion have, in collaboration, built a novel visualisation tool to ensure advertising campaigns are effective in reaching the largest target audience possible.
4. Personalisation = INCREASED CONVERSION
Visual analytics are empowering organisations to understand customer profiles in great detail. Once insight has been gained from these visualisations, organisations need to tailor their services accordingly to enable a valuable, customer-centric experience.
Chandran Rajah, Head of Group Analytics at Shop Direct spoke about the role of predictive analytics in retail which allows them to profile customers and provide a more personalised experience, to ultimately increase conversion. Techniques including regression, what-if and deep learning are used to target bespoke products to distinct customer segments. He spoke about the change in retail strategy over the recent years – businesses have transitioned from being survival-led, to business- led to being truly customer-led.
Ronald Van Loon echoed this sentiment – 80% of companies today compete based on product, price and location. In the digital age, it is increasingly difficult to compete in this way when consumers have the power to compare product information at their fingertips. He enforced the idea that companies should be doing something special to create loyalty with their customer base. Big data can help companies to compete, allowing them to become more relevant and generate sustainable relationships with their customers. The first step to becoming more customer-centric is to fully understand the customer journey by collecting data at each point along this path.
5. Availability = SOCIAL BENEFITS
The “Internet of things” phenomenon has allowed vast amounts of data to be collected. An ongoing theme was that this data should be readily available to all on the open market. However this has ethical and legal implications. Ioana Hreninciunc from conference sponsor Big Step referred to data as a “diamond mine” – we are all sitting on something precious, but need to leverage its benefits ourselves.
Kenneth Cukier, Data Editor at the Economist agreed and emphasised the social benefits that can be realised if data becomes open source. He put forward a compelling argument for pushing change in privacy laws for which we should “avoid a knee jerk respect”. Interestingly, Cukier compared the distribution of data to the multiplier effect of the financial world. The premise being, the more money put into circulation, the more value it holds. Similarly, we need to make data available to release optimum social benefit. There is a tendency towards an “Alice in Wonderland” world where everything remains private. Both Ioana and Kenneth concluded that a change in values is required for people and leaders to embrace a data sharing culture – the benefits of sharing data would outweigh the possible negative implications.
6. Machine learning = THE ROBOTS ARE COMING TO GET YOU
Forget Big Data – Think machine learning. Referred to as the ‘Russian Google’, Jane Zavalishina from Yandex Data Factory predicted another ‘industrial revolution’ in the years to come, where skilled workers’ jobs are replaced by machines which automate their daily tasks. Zavalishina stressed that data insights that help you make decisions only bring a fraction of value. The true economic value of big data is when you use machine learning to automate decision making. That said, machines cannot yet define your strategy, which is why there is still a need to keep traditional BI methods rooted in the 21st century workplace.
Bigger the better? “Big” data is only useful if you do something useful with it. One theme rang loud and clear – organisations, data scientists and data engineers alike face a challenge in changing the status quo with regards to privacy law and the way in which data is collected and circulated around the world. Organisations have an moral and ethical responsibility to do the right thing and this is an issue that anyone involved with the collection of data, in particular the internet of things, will have to think about going forward. It is also no use having technology for technology’s sake – it needs to align closely to organisational processes, values and culture.
Will you use your data to be the disrupter or the disrupted?
Engage data wisely.