The amount of data each one of us is creating, in this world of increasing connectivity, is enormous. Although, we are aware that big data is poised to transform businesses and even our lives, but companies are gradually struggling to find a way to translate all that data in a way which actually benefits the business.
We’ve reached a stage in the field of technology where consumers are no longer attracted to access to data, as data availability, even our own, is increasingly the norm. If you are wondering what exactly are consumers looking in products and services, the answer lies in the usability of the data.
So what are successful companies doing to stay ahead in the game to woo consumers? It is Design Thinking. By employing human centered design thinking techniques to something inhuman such as big data, companies are continually engaging with customers in an emotional way and outsmarting the competition in the market.
No matter how much the world has come to revere Big Data, data scientists don’t hold some magic formula that’s going to save the world, radically transform businesses, or eliminate poverty. Problems cannot be resolved just by accumulating large amount of data or setting a bunch of nerds loose on a pile of data. Solving a problem not only requires a high-level conceptual understanding of the challenge, but also a deep understanding of the nuances of a challenge.
Let’s take a quick recap of the design thinking process:
Getting back to how big data companies can leverage design thinking, data scientists could benefit by starting with asking right questions like who for? Who are? Instead of applying frameworks and algorithms right away to the data, begin with a stakeholder mapping exercise. Understand and clarify relationships and potential gaps, being sure to include both internal and external stakeholders. This will help data scientists understand who is the target user and who will benefit most from their future innovation.
Design relies heavily on empathy and it is the cornerstone of ground breaking products.To utilize design thinking methodology, understand your consumers or users and develop insights and opportunity statements to act as a springboard for ideation. Look at how key stakeholders use (or don’t use) information currently, concentrate on individual experiences, invite stakeholders into the process as much as possible, whether through contextual observation in the early stages or collaborative workshops as your process evolves. Forming a deep customer empathy allows data scientists to understand the consumers, the environment and the criteria of a good solution. The end-goal of any product is to be consistently used by all users, not just power users, and the only way to accomplish this is to make it as simple as possible to discover the relevant insights.
Design thinking has given a rise to a new mantra- Fail fast to succeed faster. Sooner you identify that something is failing, the quicker you can fix it and the faster you will ultimately succeed. A vital component of design thinking is a prototype, which conveys a realistic impression of the new problem solution as early as possible. Developing prototypes allows to obtain more informed feedback from users, and combining this information with additional research, iteration, and brainstorming lays the pathway to reach the final goal.
As technology is evolving at a rapid pace, enterprises are facing new challenges every day which is only growing complex with time. When design-thinking is applied to analytics, sheer magic is born. Big data in combination with design thinking can be revolutionary by virtue of the value it creates for organizations. Thinking from a human level, paying attention to the human factors that contribute to or motivate success, is the surest way to for organizations to unlock new opportunities, build empathy for users and pave the way to exceptional experiences that are truly human-centered.