Dashboards in banking are pivotal in gaining a deeper understanding of the business as it opens the door to the large amount of data being generated daily. Banks have always used data more meticulously than other industries because of the need from different customers, the risk involved, and regulatory commitments. Complex banking scenarios may require advanced analytics such as machine learning and AI – however, we should never overlook the power of simplicity – dashboards with the right amount of information, in the right hands, at the right time can empower organizations in unimaginable ways.
However, most dashboards meet a dead-end as users don’t feel a personal connection to the end product either because the information isn’t meaningful, challenging to use, not actionable, or adding any value.
This article will look into making your dashboards personal and avoiding dead-ends, keeping in mind a few things when you build your following dashboard.
Gather the proper requirements and be clear on what actions the dashboard should drive
Asking your users and stakeholders the right questions and gathering accurate requirements will set a solid foundation for developing your dashboard.
Who is your intended user base/audience?
Find out who is going to use your dashboard. For example, are you going to build a strategic dashboard for senior management or a tactical monitoring dashboard for operations? The amount of data you show and the level of abstraction will need to be tailored as per the user type to make the dashboards more personable.
What would your users like to see, and what action would they like to drive?
Flesh out what your users would want to see on the dashboard. Then, ask them what problem they are trying to solve and what action they want to drive to fix it through the dashboard.
What views of the data would your users like to see?
Ask your users what they would like to see on the first page, which sections require a drill-down, and how the data should be broken down and filtered. A summarized view may be sufficient for some, but others may prefer to drill down and explore further down. It should be designed to follow the natural curiosity and logical questioning – e.g., starting with an overview, breaking it down into essential dimensions, and then providing granular details.
Ideate before going into solution mode
Once you have gathered the requirements, it is time to put more thought into the final product. It is good to break your project into manageable chunks, making it easier to delegate tasks and synthesize everything into one product.
Come up with simple mock-ups so that everyone involved gets to see what the product will look like. This ensures that everyone is in alignment and changes can be made well before hours are spent developing the dashboard. Wireframing may need a couple of brainstorming sessions with your team, users, and other stakeholders, but it is entirely worth it as your dashboard development will be a lot smoother.
Think and document in detail the technical requirements of the dashboard. Include KPI calculations, data assumptions/inclusion/exclusions, data sources (core, peripheral and external), and the automation process. Get this endorsed by your key stakeholders to make sure that everyone is on the same page.
Now that we have an idea of what the end product looks like come up with a few key questions your users may want the dashboard to answer and see if those can be answered and what actions they would like to drive by answering those. If not, iterate your mock-ups accordingly.
Design your consistent dashboards
While building the dashboard, adhere to your organization’s style guide – if your company has predefined style and branding guides, stick to it as it helps to ensure a continuous brand experience. It means that no matter how, when, or where someone is using your dashboards, they are experiencing the same underlying traits. It’s this consistency across every single dashboard that you build that will building brand loyalty and familiarity.
Test and QA your numbers before sending them out
Once you have finished the build, you must do mandatory quality checks before sharing the dashboard with your users.
Validating and sense checking numbers
This exercise is critical to building confidence and trust in the dashboard. A dashboard published with funny numbers once is enough to stop users from using it. This layer is the only layer that talks to business, and if it is filled with erroneous data, your business may make wrong decisions.
If you have used features like hierarchy, actions, filters, navigation buttons, etc., you must test these features before pushing the reports to your users.
Send supporting documentation along with your dashboard
Remember that not all users are tech-savvy – using BI tools such as Tableau and PowerBI may be onerous for some, especially those who are not exposed non-Excel based reports. Attaching supporting documents such as user guides and factsheets to your dashboards may help in its take-up. e.g.-
- Factsheet about the dashboard – what’s the purpose of the dashboard, where it can be found, and whom to contact in case they have any questions or need further information
- User manual – how to navigate and use the dashboard
- FAQs – Should cover information on terminologies used, how frequently the data is updated etc.
Launch, Adoption & BAU
You have everything you need to launch your product at this stage, and it’s time to let your potential users know that the dashboard exists. After the launch, you also need to make sure that the dashboard does not meet a dead-end. Have an implementation plan ready that covers the following:
Are you building your products iteratively?
Communicate to users that the dashboard is being built iteratively – let them know what extra features will be available in the next version. Always try and create your products iteratively and be ready for continuous improvements.
Hard versus soft launch
Go for a soft launch approach if your organization is at an early stage of its analytical maturity cycle. Users can find the shift to new data products overwhelming if they are not guided correctly. A soft launch will not only act as a cushion but also opens opportunities for feedback and change.
Show your users how to use the dashboard
Conduct 1:1 or tutorial sessions with your users, gather feedback and gauge their interest in using the product. Then, speak to your users on how they can use the dashboard to derive actionable insights.
Track Usage and identify dead-end dashboards
- Identify dashboards that are becoming less popular is a great way to identify a potential dead-end dashboard. First, take stock of dashboards that are most and least popular.
- Are there any dashboards that were popular before but aren’t anymore? Check if these dashboards cover a problem that the business has already solved. Reach out to the users to identify how it helped. Maybe there stopped using it because it is not relevant anymore
- Which dashboards are used by only a tiny section of the intended audience? Low usage might indicate that some users haven’t been appropriately trained in using the dashboard. On the other hand, it could mean that some users have different questions that the dashboard does not answer.
The accurate measure of how much value dashboards bring is dependent on the actions they can drive within a business. Your data provides very little utility if your dashboards are overwhelmed with too much noise instead of giving clear insights and directions. As you apply these tips, you will see a shift in how the users engage with your dashboards to inform better decisions. With a little more preparation around the design of your dashboards, you can considerably enhance their actionability and ensure they don’t bite the dust!
Krish is a data analytics, visualization and dashboarding expert who has worked across a range of industries such as financial services, telecommunication, public sector and tech start-ups. His core specialties include customer and marketing advanced data analytics. He is Tableau Certified professional and has built over 30 production dashboards including the backend data modeling of very large datasets.