In order to develop this new product that touches upon user & business needs, the design process uses heavy amounts of design iterations, user feedback and testing to validate our direction, because of this I used the double diamond approach to both discover needs and define the design.

To kick start things, I developed a 12 week plan to start design and research. The 12 weeks will uncover user needs, desirability & market fit through user research and design iterations. User testing sessions involve a series of moderated testing, 1-on-1 user interviews and surveys to assess the customer’s needs, understand their pain points and areas of frictions. The focus for Design is to discover areas of improvements in the overall experience and to develop alternative design iterations, generate new ideas, and to validate assumptions with high & low fidelity UI designs.

I created a How Might We statement to focus the design efforts into a question to be solved, this turns the problems into opportunities for generative thinking on how to best solve the problem discovered during my user research. 

Designing Around Our Areas Of Opportunities & H.M.W

After reviewing the analyst’s journey, I identified a few areas in which we can enhance their workflow and create a better experience. These areas of opportunities were then validated with end-users to further enhance their process. Going forward, I used our ‘areas of opportunities’ and the ‘how might we statement’ as a bases for developing designs. The wireframes below is the first phase in my design process and used to understand user needs.

The first set of wireframes are based on opportunity #1 (Enhance the iteration process & help develop faster analysis) using the Periscope look & feel. At this point in time, both Sisense and Periscope Data did not have a clear direction for how the product should be merged. Because of this ambiguity, I used the Periscope look & feel to jumpstart design and the testing phase. The wireframes illustrate the concept of adding multiple charting blocks allowing data analysts to see multiple views of their data. As a product group, we assumed this ability would aid analysts in developing faster analysis but our assumption proved to be wrong.

2nd Round of Wireframes

Taking the feedback from the last set of wireframes, the main focus here is the ability to add many blocks to enhance the code iteration process. These blocks give analysts a way to brake their code into smaller parts and to test their results in 1 area. The wireframes illustrate the ability to 1) create code blocks, 2) write code within a block and 3) run the code block to see the results. Each code block can be ran independently from one another, giving the analyst the power to see and understand how their dataset progressed into a finalized outcome. In addition, blocks can be used for text, images and different forms of visualizations to create a narrative around a dataset. Our customers responded very positively to this new direction of adding content blocks, and proved to be an important key in laying out the foundation of this new product. This new ability of adding code blocks to test and iterate upon a dataset solves our user’s frustration of writing and testing large datasets found in traditional code editors and makes the iteration process easier and intuitive. By the end of this testing phase it was determined that this product should fit within the Sisense look and feel in order to promote the merger of Periscope Data into Sisense.

Finding The Best Solution

In this design iteration, I placed the add more content buttons on the actual content blocks. After user-testing this method 5 out of 5 analysts were able to see the buttons and understood that new content is added to the bottom of an already existing block. Because of the linear progression of writing code from top down, the bottom placement on the content area allows analysts to see and create another block with ease. This new design is able to maximize the area for code while allowing analysts to see and create many blocks for an iterative approach to answering complex business questions. Analysts using this method can now brake large datasets into smaller pieces, run individual parts of a dataset to see if their hypothesis is correct, write new code and retest until they arrive to a final result. By giving the user the power to add multiple blocks became the key factor in solving our user’s need to have a tool that supports an iterative process of writing, running and retesting datasets.