Reflection – Learning Launch for Data Product Design Thinking

Challenge:

I am a member of a team that provides business intelligence and analytical solutions to the advisory  services team of a financial services firm. The advisory team published a list of recommended preferred  securities every week that is accessible to almost 4,000 financial advisors.

The challenges faced by the advisory team was that their members had to spend considerable amount  of time every week in collating, sorting and formatting data from Bloomberg before publishing the  recommended preferred securities list. Also, it was cumbersome for financial advisors to view and filter  preferred securities from the basic published Excel file as per particular criteria such as coupon rate,  yield to worst, tax considerations, ratings and outlook categories.

Selection:

Learning launch tool enables engaging potential customers in the creation, design and testing of new  ideas as soon as possible; and learning as you go, iterating constantly for improvement.

Our team used the learning launch tool as it is the ideal technique to ideate, create prototypes and test  various solutions in several cycles that will be created for the advisory team as well as the financial  advisors. This seems to be the best approach in applying design thinking for data products.

Application:

For the first learning launch, we underwent two rounds of discussions with the advisory team. This  helped our team to be aware of the weekly updating process in detail. We also created a survey and  distributed it to a sample of 200 financial advisors.  The survey helped our team in understanding in  detail the pain points faced by the financial advisors in accessing the published files.

For the second learning launch, our focus was to understand and collate the concern areas faced by the  members of the advisory group individually in their weekly tasks. We also distributed a second round of  survey to a sample of financial advisors, this time to gather various preferences/wish list in terms of user  experience.

Insights:

By applying the learning launch tool, we identified the need to create an advanced search tool solution  for the financial advisors. This search tool will enable the financial advisors to view preferred securities  information in detail as per their selection criteria. The criteria can be rating or outlook categories, bond  prices or tax considerations. The insight will help our team in developing the advanced search tool  prototype. This incorporation of insight will help in future launch designs in order to improve the user  interface and user experience. There is a scope of saving 60,000 hours annually by implementing an  advanced search tool solution.

The application of learning launch tool has also been beneficial in understanding the issues faced by the  advisory team in their weekly process of handling preferred securities data from Bloomberg. We have  identified the need to create an interactive uploader tool that will help in re-engineering the complete  manual processes of the team and save considerable time every week. The business process re- engineering will potentially save 250-300 hours every year for the advisory team.

Approach:

Learning launch seems to be the most appropriate tool for the mentioned project challenge. After  enough research has been completed through the learning launch, we can assure ourselves of the sound  fundamentals of the new idea and move to staging a roll-out of the idea.

Our team will assess the insights for the two-fold solution from our learning launches and gather  feedback to further the solutions in terms of user experience, user interface and other benefits. The  learning launch tool can also be further implemented to create a similar solution for the Equity research  team as well. Also, learning launch approach can be used for some of our future Big Data solutions too.


Reflection – Learning Launch for Data Product Design Thinking Challenge : I am a member of a team that provides business intelligence and analytical solutions to the advisory services team of a financial services firm . The advisory team published a list of recommended preferred securities every week that is accessible to almost 4,000 financial advisors . The challenges faced by the advisory team was that their members had to spend considerable amount of time every week in collating , sorting and formatting data from Bloomberg before publishing the recommended preferred securities list . Also , it was cumbersome for financial advisors to view and filter preferred securities from the basic published Excel file as per particular criteria such as coupon rate , yield to worst , tax considerations , ratings and outlook categories . Selection : Learning launch tool enables engaging potential customers in the creation , design and testing of new ideas as soon as possible ; and learning as you go , iterating constantly for improvement . Our team used the learning launch tool as it is the ideal technique to ideate , create prototypes and test various solutions in several cycles that will be created for the advisory team as well as the financial advisors . This seems to be the best approach in applying design thinking for data products . Application : For the first learning launch , we underwent two rounds of discussions with the advisory team . This helped our team to be aware of the weekly updating process in detail . We also created a survey and distributed it to a sample of 200 financial advisors . The survey helped our team in understanding in detail the pain points faced by the financial advisors in accessing the published files . For the second learning launch , our focus was to understand and collate the concern areas faced by the members of the advisory group individually in their weekly tasks . We also distributed a second round of survey to a sample of financial advisors , this time to gather various preferences / wish list in terms of user experience . Insights : By applying the learning launch tool , we identified the need to create an advanced search tool solution for the financial advisors . This search tool will enable the financial advisors to view preferred securities information in detail as per their selection criteria . The criteria can be rating or outlook categories , bond prices or tax considerations . The insight will help our team in developing the advanced search tool prototype . This incorporation of insight will help in future launch designs in order to improve the user interface and user experience . There is a scope of saving 60,000 hours annually by implementing an advanced search tool solution . The application of learning launch tool has also been beneficial in understanding the issues faced by the advisory team in their weekly process of handling preferred securities data from Bloomberg . We have identified the need to create an interactive uploader tool that will help in re - engineering the complete manual processes of the team and save considerable time every week . The business process re- engineering will potentially save 250 - 300 hours every year for the advisory team . Approach : Learning launch seems to be the most appropriate tool for the mentioned project challenge . After enough research has been completed through the learning launch , we can assure ourselves of the sound fundamentals of the new idea and move to staging a roll - out of the idea . Our team will assess the insights for the two - fold solution from our learning launches and gather feedback to further the solutions in terms of user experience , user interface and other benefits . The learning launch tool can also be further implemented to create a similar solution for the Equity research team as well . Also , learning launch approach can be used for some of our future Big Data solutions too .