Reuters, Financial Analysis Highly visual and intuitive to use, it is the ultimate set of financial analysis tools. Integrate multiple workflows, co-create applications and securely connect to other financial professionals.
I worked closely with the Reuters team of Business Analysts, UI designers and Business owners on this project. It took us close to six months to finish and test the prototype. For the sake of story telling and presentation it's laid out in day to day basis.
Day 1 Understand
I started with fully exploring the customer, the user, and their problems. After a thorough review of the existing product, industry, user research, and competitors, I dove into a series of activities to uncover the goals of the work ahead, riskiest assumptions, stakeholders, user journeys, and a clearly defined problem the team could all align around. These activities brought the entire product team together to fully understand and align on three key questions: Who is the customer? Who is the user? What problems do they face?Day 1 Takeaways
I started with fully exploring the customer, the user, and their problems. After a thorough review of the existing product, industry, user research, and competitors, I dove into a series of activities to uncover the goals of the work ahead, riskiest assumptions, stakeholders, user journeys, and a clearly defined problem the team could all align around. These activities brought the entire product team together to fully understand and align on three key questions: Who is the customer? Who is the user? What problems do they face?Day 1 Takeaways
I focused on four key users of the Data: Data Manager, Researchers, Traders, and Strategy makers. They outlined the users’ backgrounds, motivations, and current frustrations. Using these insights, the key problem facing users was identified: they were forced to use several different systems to complete their tasks and in doing so, were forced to jump back and forth between massive data sets on a scavenger hunt for the information required to solve their queries. This data hunt was time-consuming and frustrating, and the number one problem for all four user types.
Day 2 - Diverge
To set our minds in the right direction, we revisited assumptions from day one that related to the chosen problem, added new assumptions as needed, and then asked the team to prioritize the riskiest ones with a voting activity. Once the team was aligned on where their biggest risks would likely be, I asked each participant to craft as many job stories as they could come up with. These are not feature requests, but statements that outline their users’ needs mapped directly to the motivations driving them and the users’ desired outcomes.
We ran through several rounds of this process to ensure there were enough ideas to achieve the right solution to our problem, not just any solution.
To set our minds in the right direction, we revisited assumptions from day one that related to the chosen problem, added new assumptions as needed, and then asked the team to prioritize the riskiest ones with a voting activity. Once the team was aligned on where their biggest risks would likely be, I asked each participant to craft as many job stories as they could come up with. These are not feature requests, but statements that outline their users’ needs mapped directly to the motivations driving them and the users’ desired outcomes.
We ran through several rounds of this process to ensure there were enough ideas to achieve the right solution to our problem, not just any solution.
Day 3 - Converge
Using assumption tables, sketching, tough conversations, and ranking activities, one solution reigned supreme. The team believed a streamlined, unified workflow for scrubbing data was necessary, and that not only could present just the necessary information dynamically, but users would actually be more comfortable with seeing less, albeit more valuable data.
Using assumption tables, sketching, tough conversations, and ranking activities, one solution reigned supreme. The team believed a streamlined, unified workflow for scrubbing data was necessary, and that not only could present just the necessary information dynamically, but users would actually be more comfortable with seeing less, albeit more valuable data.
Day 4 - Prototype
Prototypes are not perfectly polished products, they are an interactive digital sketch, and contain just enough polish to test the idea with real users outside the client team. Day four was spent designing interactive screens which brought a theoretical data management workflow to life. Cutting out the messy spreadsheets and superfluous data, the prototype screens surfaced only what was understood to be the most desired data for their target users.
Prototypes are not perfectly polished products, they are an interactive digital sketch, and contain just enough polish to test the idea with real users outside the client team. Day four was spent designing interactive screens which brought a theoretical data management workflow to life. Cutting out the messy spreadsheets and superfluous data, the prototype screens surfaced only what was understood to be the most desired data for their target users.
Performance- with ability to compare unlimited series.
Ability to display complete balance sheets.
Asset Allocations with ability to do back testing.
Test
The results were definitive. Users marveled at the simplicity of the tool, and the ease with which they could find the data they needed, ultimately making them exponentially more efficient in their work. One rough calculation estimated the tool could save one hour per day per user. The enthusiasm was truly humbling.
The results were definitive. Users marveled at the simplicity of the tool, and the ease with which they could find the data they needed, ultimately making them exponentially more efficient in their work. One rough calculation estimated the tool could save one hour per day per user. The enthusiasm was truly humbling.
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