Design Thinking

Big data analytics projects are ubiquitous. According to one survey, 37.2% of executives report their organizations have invested more than $100MM on big data analytics initiatives within the last several years, with 6.5% of organizations investing over $1B. While 81% of executives qualify these efforts as successful, there is less certainty about these projects achieving measurable business value and widespread adoption. The same survey finds that only 37% of respondents report success in creating data-driven cultures. Other findings are less optimistic – Gartner estimates the failure rate of similar initiatives is closer to 85%.

Why do big data analytics projects fail so often? Although there are likely many causes, perhaps the largest factor is a poor understanding of the business use case. All too often, the tendency, when ramping up big data analytics projects, is to sift through the data to uncover problems. This approach may yield interesting results, but rarely produces a robust business case.

Instead of starting with a technology project and trying to produce business results, a better approach starts with the business problem and uses technology to solve it. This type of customer-centric approach, where you define the business problem up-front, allows you to understand how users will interact with and use the data insights. Only then can your data scientists design and develop a targeted solution.

This new paradigm – where the customer, rather than the product, leads – sounds simple enough, but how do you ensure that the customer remains central to your efforts? This is where design thinking comes into play.

What is Design Thinking?

Design thinking places the end user at the center of the design process and enables teams to collaborate and work more efficiently. Design thinking starts with a goal, rather than a specific problem and is particularly useful for poorly defined problems. Furthermore, it allows the designer to think about problems in human-centric ways, in order to focus on what is most important for the customer. This hands-on, user-centric approach inspires innovation; innovation then leads to differentiation and competitive advantage.

The design thinking framework, illustrated below, is composed of three phases (understand, explore, solve). Each of the three phases are then broken down into six steps (empathize, define, ideate, prototype, test, and implement).

Design Thinking Methodology

Applying Design Thinking to Analytics Use Case Development

How do we apply the design thinking framework to make the customer an integral part of the entire product lifecycle? Listed below are key design thinking practices to develop winning analytics use cases.


  • Understand

    How do you ensure that the customer will want to use your solution? In order to understand your customer’s needs, empathize with them. And the key to empathy is to observe them – how they think and work, what frustrates them, what motivates them, what data is missing from their view, and how would they use that data to make decisions and optimize their processes? A simple way of achieving empathy is to conduct in-person observation where the work is happening. The goal is to observe and understand without taking action. When you empathize, you put yourself in the customer’s shoes and ask the right questions.

    Next, engage with your customers to make them feel as if they are part of the solution. Seek their input in defining the problem, collect their insights, and identify common pain points. This ensures that you are focused on solving the right problem; many times, this collaborative approach redefines the problem itself.

  • Explore

    Once you have properly defined the problem, work with the users to ideate. Ideation involves brainstorming innovative solutions and exploring multiple avenues. This co-creation approach ensures that your stakeholders will feel more invested in the success of the solution. Likewise, it safeguards against developing a black-box solution; instead, your users will gain confidence in your data analytics solution, leading to greater trust and buy-in.

    Based on the groundwork laid in these early steps, you can then begin building iterative prototypes of the solution. Early prototypes can be paper-based while later prototypes should be more tangible and better reflect the needs of your users. Share your prototypes with different groups of users and get feedback each time. The primary goal during this step is to learn about what works and what doesn’t work through a well-established feedback loop. The goal is not necessarily to produce successful prototypes – failure is acceptable, as long as you learn from the experience. Using an iterative and incremental approach to building a prototype allows you to learn quickly and gradually design a solution that will delight your customers.

  • Solve

    Design thinking does not end with a prototype. Once you have developed a testable product, circle back with your customers to gain additional feedback. This step serves as a sanity check that you have developed a product that meets the customers’ needs and solves their problem. Moreover, any feedback gained during this step can still be incorporated into your final solution.


An ideal approach to implementing your solution is with an Agile framework. Combining the problem-finding nature of design thinking with the problem-solving ability of Agile product development produces a powerful effect. Design thinking focuses on user needs while Agile focuses on incremental delivery. Combining the two approaches ensures that user needs are addressed throughout the design and development process.


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