Locate, Define, and Secure Data
Saas Feature Design

My Role and Focus

Project Lead, UX Designer

Client: ALTR Solution Inc.

Team & Timeline

Supporting Designer: Kate McCarter

Project Supervisor: Jacob Wagh

Lead Engineer: Kevin Rose

Dec 2020 - June 2021

Tools

Figma

Confluence

Jira

The Problem

Companies today are able to leverage flexible and powerful cloud data storage tools such as AWS, and Snowflake to harness the power of data in ways never thought possible. 

However, with this increase in power and ubiquity of data access comes increase risk of bad actors gaining access to highly sensitive, personal data.

The Solution

In order for companies to reap the benefits modern cloud data solutions without compromising their data security, they need to ability to quickly identify all sensitive data within their cloud databases, and go on to secure that data.

Results

- Used as a primary selling point of the product experience
- Aided to the acquisition of the companies largest enterprise client in a deal worth over 500k ARR

Research

I began my process with gleaning and understanding as much supporting and contextual information as was neccessary for this project. I met with internal stakeholders, talked to users, and researched what was already out there on the market.

01
Stakeholder
Interviews
I met with Marketing, Product, and Engineering leadership to further scope the project from multiple different lenses
View Insights
Marketing Key Insight:
There is no other data governance company that allows for a one stop location for classification and protection, and there is a strong desire in the market for that framework
Product Key Insight:
Users currently have to manually look up their data and add it to ALTR one column at a time; a solution that doesn’t scale well - gaining classification would fix the issue.
Engineering Key Insight:
The feature was to be built using the Google DLP classification service, which comes with it’s own nomenclature, and API infrastructure as a design constraint
02
User Interviews
I asked current and prospective users a number of questions regarding data management and discovery to better understand their pain points
View Quotes
“I can run my database through a third party classification service, but there is no simple way to manage or govern that data once it’s classified”
-data architect,  lending, 
ALTR customer
“ We can write the code to set up Snowflake object tags, but it’s incredibly time consuming, and it’s not easy to govern once configured”
- chief data architect, city government, ALTR Customer
“I simply don’t have time to manually create an accurate data governance model in Snowflake.”
- data engineer, health and wellness startup, prospective client
“I want to be able to have confidence in my governance model; I need to know that I can govern all of the sensitive data in my org”
- senior security officer, ebanking, ALTR Customer
03
Competitive Analysis
After scoping the project, and understanding user pain points, I looked at similar classification products for interaction design inspiration
View Insights
Imperva
Imperva is a direct governance competitor that provides a drill-down style solution from high-level metrics, all the way down to column level views.
Transferable Features :
Drill-down from broad metrics to individual columns
Microsoft Azure
Azure is an indirect competitor that  provides visualizations for aggregates of classification.
Transferable Features :
Pie chart breakdowns of data,
High-level classifications metrics
Immuta
Immuta is a direct competitor that allows users to classify data in their cloud data warehouses upon injestion into their portal.
Transferable Features :
Allows users to create custom tags in order to categorize data by use case

Ideation

I analyzed the current user journey and product experience, and used Fig Jam to map out the new additions to the journey. This allowed me to better understand what connective tissue would be necessary to allow the feature to function within the product.


I went on to mock out several different visualizations, and configurations to later be refined through feasibility discussions and usability testing. Below are a few impactful samples.

Usability testing and stakeholder feedback allowed us to determine that this feature configuration was the most intuitive.

Refinement

I built-out several flows in the prototype used to conduct contextual inquiries. I observed users through screen sharing on zoom, prompting them to navigate through the flows and recorded their reactions along the way, as well their comments about the overall experience.  Based on user and engineering feedback, I was able to refine my wireframes into a high-fidelity prototype.

Delivery

The last phase of the project was the delivery from product to engineering, which involved the following four steps.

1) Specing:  Outlining hover and button interactions, spacing, color, font, error states, loading states, responsive breakpoints and interfaces,  other feature dependencies, and various interaction flows
2)  Writing of acceptance criteria:  Using Jira, together with the engineering team we broke the product epic down into stories, and wrote explicit acceptance criteria for both the UX and technical aspects of the build
3)  Q/A:  When front end components of the build were complete, I was able to access the developer environment, and mark-up any interface and experience issues where the build diverged from the designs. Issues were interated on until I gave my seal of approval
4)  Soft and hard launch:  Once the build was complete, we would beta launch the feature in a test environment for one week to “power use” the feature and ensure there were no major unforeseen issues before the official launch

Conclusion

The feature went live in May of 2021. Marketing and Sales demo’d the feature as a core aspect of the value proposition between 2021 and 2022. This aided in  the acquisition of enterprise contracts as large as 500k ARR within an individual contract. 

Future iterations of this feature will include the ability to create custom classifications, and bulk add-data to ALTR to allow users to scale-up their governance models.

Get In Touch

Need some help with a project or just wanna chat technology, design, or mindfullness? Text me @ 512•853•0338

jon@ezell.guru
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