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Did Big Data Fail Nokia?
By Aditi Bhat
Data science has made its way into many aspects of our lives and businesses. Organizations rely on data for every major business decision. A large section of the world believes that Big Data has the answers to every problem that the mankind faces and might face in the future. The success stories of organizations and businesses that use Big Data analytics are filling up media content every day.
With the impact of data science being undeniable, companies and industries, irrespective of their size, are investing millions in Big Data for huge profit margins in return. But, how far can Big Data alone take them?
Tricia Wang, a global technology ethnographer and co-founder of Constellate Data, shared her insights about relying solely on quantifiable Big Data in a TEDxCambridge Talk. Using Nokia’s downfall for not getting on the smartphone trend because they didn’t see quantifiable proof that it was a trend which was here to stay, Ms. Wang emphasized on the need for human insights to help Big Data analytics. Her Ted Talk is an expansive insight into dealing with the constantly changing and intensifying world of data and innovation.
What’s the challenge with Big Data?
The Gartner Survey result shows that companies that invest in Big Data are on the rise, but most of them are not getting any potential benefits from using it. In her speech, Tricia Wang points out that73% of the Big Data projects that companies invest in, do not turn out to be profitable. When you have a vast amount of data, it may seem difficult to quantify the information in a way that enables the management to take better decisions. When statistical analysis of data is considered as an isolated project not connected with the company’s concerns, there might be difficulties in interpreting the real challenge.
When Nokia was the ‘super star’ of mobile phone industry with their Symbian models, the company failed to see the obvious shift of interest in customers towards smartphones. Nokia banked heavily on what quantifiable data showed them at that point in time, ignoring a more wholesome perspective and strong insights as to how the consumer base could trend.
When organizations begin to ignore the more complex features such as human intelligence to focus only on what is quantifiable, they begin to lose what could potentially make them unique and successful. The tendency to trust the quantified numbers more than anything else is natural, but dangerous.
Mere numbers can’t tell everything:
The need to keep up with market trends constantly forces transformation upon organizations. And these transformations are largely predicted and determined by Big Data. To reap quick and compelling development, Nokia too, invested in Big Data and focused on it from a single perspective. But how far can data alone take the company? What can it do for the business without the human expertise that is required in modeling information for decision making? Not much.
The most brilliant and unfathomable visions unfolded by Big Data have only done so using the human insights that brought out the data, in the first place. This is where ‘Thick Data’ comes into play.
Thick Data and why it will help:
Thick data is formed by using ethnographic user research methods that observe and analyze human emotions and their perceptions. This information is crucial to the very existence of businesses. The stories and lives of prospective consumers; of the products/services manufactured by the business are valuable. Such information cannot be gathered and analyzed by machines with the click of a button. It is achieved through constant research and by understanding ‘WHY’ exactly something is happening and ‘WHAT’ exactly needs to be done.
If Nokia had attempted to incorporate Ms. Wang’s insight even in a small way, instead of merely looking at the margin of difference between their Big Data sample and hers, they might have survived to fight bigger battles. This is proof that 100 people could be a strong suggestion as to where the market is moving next, if the thick data is substantial.
One of the most successful integrations of Thick Data and Big Data was when Netflix upgraded its algorithmic feature to incorporate users’ tendency to binge-watch shows. It not only made suggestions as to what the user would like, it went one step ahead, understood its consumers beyond just the numerical data and made suggestions that kept them binge-watching. A hop, skip and a jump away, Netflix is now more of a verb and an emotion than just a means of entertainment.
Thick Data + Big Data = Best Solution
It is suicidal to refuse change by not implementing Big Data for the betterment of your business. But if data is not used in a way that will address issues beyond what meets the eye, the destiny of your business will be the same as that of Nokia. The ideal approach towards this issue is integrating Big Data with Thick Data.
Thick Data can be used to fill the gaps left by mere numbers generated from statistical methods. The role of an ethnographer in such an industry is absolutely essential. The truth is that ethnographers with impressive analysis skills are hard to find.
Big Data scientists need to understand the importance of ethnographers and user researchers for synthesizing their numbers into actual business goals based on the company’s vision. It is also important to ask the right questions when the data is fed to the system. It must be very clear to the management why they are using Big Data analytics for their business. When Thick Data is clubbed with social media analytics and customer analytics, companies can make near-accurate predictions by simply understanding the context.
What you can learn from Nokia:
It is time businesses realize that merely investing in Big Data will not guarantee the change and upgrade they want to see. They must understand that without the touch of the human interpretation and information to set context; without the stories that give them issues to solve, Big Data will merely be data sets that give out ambiguous solutions.
It is necessary to take precaution and not to fall prey to the quantification bias, because Data has pervaded a multitude of industries. Forget commercial businesses, it is frightening to see that quantification bias could happen in the fields of Healthcare and National Security, where one unwholesome interpretation could turn things for the absolute, irreparable worst. It is distinctly possible for our world to slip into another world war just because some data was not analyzed correctly. So it is important to remember, Big Data without Thick Data is just a solution to an unasked or wrong question.
What is your take on Thick Data + Big Data integration? Do you think Nokia would have continued being the undisputed king of the market had the company banked on this integration? Tell us via comments!
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