Data Insights Hub
Analytics are an important part of making insightful decisions that are backed up by a proven and reproducible model of data collection. Today at DISH, we sometimes make these decisions in a data-poor environment that negatively impacts all aspects of our business. The Digital Insights Hub represents a future-forward vision of our solution to this problem.
Disclaimer: Any numbers or data you see in the featured designs are placeholder and may not be accurate to that of the business.
August 2023 - Present | Enterprise
THE CHALLENGE
This product is a bit unique because there are three target audiences: Executives, Product Managers/Business Leads, and Individual Contributors (Data Analysts, etc.). The challenges that existed for those audiences differed ever so slightly.
Executives
There is no easy way to evaluate KPIs and OKRs, and data is always out-of-date. Running reports felt like a constant firefighting drill and executives were looking for clean, easy-to-digest analytics at the tips of their fingers.
Product Managers/Business Leads
Numerous dashboards display different or competing information, and the lead time for data acquisition is very long. We don’t have one source of truth to understand the impact of the products we are designing or managing.
Data Analysts
Data collection requires too much manual work and there is always a large backlog. Our analysts are spending too much time on the small, less impactful exercises and not enough time doing deep dives to understand our analytics.
TLDR: Create a data-obsessed (but in a good and healthy way) culture at DISH by creating a product that aggregates data from a multitude of sources while using AI to track trends and generate interesting insights.
THE PROCESS
Utilizing an agile Scrum framework, our team engaged in iterative cycles lasting two weeks each. The team followed our traditional design process from discovery to user flows to design studios to interactive wireframes to full high-fidelity prototypes. The overarching goal was to create a data visualization tool that effectively meets user needs, improves data comprehension, and enhances decision-making through visual representation of data.
Competitive Analysis
The team started this project by conducting discovery research that involved a competitive analysis of different data visualization tools. We knew Tableau, Adobe Analytics, and Google Analytics had mastered the art of data orchestration and visualization, but we wanted to expand outside of the traditional data viz tools. What products capitalize on data and metrics to create data-obsessed individuals who are constantly competing to better themselves or their positions or at least monitor and benchmark where they’re at? Industries that we thought accomplished this were FinTech such as stock and banking apps, Sports such as player and team stats as well as sports betting, and Health and Fitness such as overarching health metrics as well as weightlifting, cardio, and exercise metrics.
Some of the key findings included
A high-level data overview
Simplicity in presentation
Interactive charts
Customization options
Seamless drill-down
Understanding Data Visualization Best Practices
What are some of the best practices on how we visualize different KPIs and metrics?
Clarity and Comprehension: Effective data visualization enhances clarity and comprehension.
Reduced Misinterpretation: Selecting an inappropriate chart or graph can lead to misinterpretation of data.
Efficient Communication: Well-designed visualizations allow you to convey complex information quickly and succinctly which is valuable in business presentations and reports.
Comparison and Analysis: Understanding chart types enables better data analysis.
Contextualization: The choice of visualization should take into account the context and purpose of the data.
User Journeys
We created a visual roadmap that depicted how users would navigate through the tool, from initial login to data exploration and insight generation. This user flow became an indispensable tool, serving as our guide in ensuring a user-centric design that promises to make data exploration an intuitive and enlightening experience.
Design Studio
This initiative was one of the first projects in our new dedicated squad models which allowed me to coach the designers and researchers on how to effectively work with product managers. Through collaborative ideation, we explored ways to craft an intuitive and user-friendly experience that would empower users to unlock the full potential of their data on the go.
This design workshop was a pivotal moment in shaping our vision, driving us closer to delivering a transformative data visualization mobile tool.
Usability Testing
Because this was an internal tool and our stakeholders varied greatly, we were able to continuously gather feedback as we progressed through the journey. We worked with executives, product managers, and data analysts to gather feedback and make design iterations based on the problems they continued to face. The team was able to gain more experience conducting in-person and with high levels of leadership testing.
MY CONTRIBUTIONS
I managed the team of Product Designers and UX Researchers who led this large initiative. I used my Data Analytics background to help coach them on interpreting and manipulating data and designing for the “roll out of bed and check our stats” executive. This product was complicated and one that was challenging to design for if you weren’t an expert in data visualization and synthesis space, but the team came together to use common patterns when it comes to data digestion and create a successful and highly desirable product. We are currently in the evangelizing and tech-scoping phase of the initiative. The ask of this initiative was relatively greenfield and required the team to think far outside the box of how to start integrating AI and data visualization into the product experience.
One of the challenges identified by designers, product managers, and the brand team was not knowing which products were the top performing and key products to highlight in the eCommerce, on-site sales, or marketing materials. The team identified a way to easily showcase the products that are selling well and through various channels.
One of the top features of the Data Insights Hub is it's a true aggregator of all data from all data sources. Employees wouldn't need to learn and understand multiple data visualization tools. Using APIs, the data is gathered from the source and presented in a visual and digestable presentation layer no matter the source. This alleviated the problem of having to access multiple tools some being out of the box and others being completely custom to gather the information you're looking for.
At the core of the experience is the integration between generative insights and access to information. We wanted to create a front and center look at insights that may be important to you whether flagged by you or using trend based user data.
As ChatGPT continued to gain popularity, leadership was keen on integrating some of their technology to give users of our tool immediate answers to any generative question they may have about the business.
The flow can be broken down into 3 views: high level overview of the enterprise, overview of the line of business, and individual digital and channel analytics. The screen featured is the breakdown of the DISH line of business' metrics.