IBM Security Cloud
- McKenzie Carlile, UX Designer
- Frances DiMare, Design Researcher
- Amanda Hughes, Visual Designer
- Joshua Kramer, Visual Designer
- Tina L. Zeng, Design Lead
- With assistance from: Vickie Culbertson, Design Lead; Danielle Demme, Visual Designer; Wenjing Li, UX Designer
- With Design in Austin, we collaborated with our Offering Management team in the UK and Canada, and our Development teams in the US, Japan, and India. Yup, 6 different timezones....
- At the end of 2017, our design team had shipped Watson Data Platform and designed the key components of a data catalog, governance, and a unified UI. As both a reflection and next step, we were able to go completely blue sky on re-envisioning and re-designing what we had just built with the support of our Offering Manager, Jay Limburn.
In January of 2018, we were green-light-go on developing the blue sky concepts that our team came up with to be generally available/shipped for annual IBM's Think Conference.
Our users' main goal at the start of their workflow is to find safe, trusted, and accurate data for business analysis, predictive modeling, and active governance.
Based on user research with business analysts, data scientists, and Chief Data Officers, we identified the key users, artifacts, and the key tasks they needed to complete to get their job done.
We shipped 5 new user experiences for Watson Knowledge Catalog in 3 months:
- Smart search and suggest of assets powered by Watson
- Ingest and profile PDFs and unstructured data using Natural Language Processing
- Masking sensitive data with policy driven transformation
- Understand how all assets are connected together with a visual map of related content
- Document and share tribal knowledge with ratings and reviews of assets
1. let watson power your search
First, we designed smart search and suggest by Watson so that users can easily find data assets that they were looking for and discover assets recommended by Watson that weren't on their radar originally. Sometimes you just don’t know what you’re missing— Watson should help you with that.
Watson Knowledge Catalog uses Watson Machine Learning to derive a list of assets that users haven't accessed yet based on attributes common to the assets that they've viewed, created, and added to projects, such as tags, asset classification, attribute classifiers, data types, asset owners, and asset types.
Smart Search and Suggest
Sometimes you just don’t know what you’re missing— Watson should help you with that.
2. Leverage Natural language processing to profile unstructured data
From user research, we’ve heard that business analysts and data scientists rely heavily on each other to complete their journey from finding data to delivering an analysis for business decision making. This tribal knowledge comes with time working at the company and asking other colleagues about which data to use and where they can find it. We asked ourselves, what if we could aid in that process? The first of the social components that we introduced is rating and reviewing assets to capture this tribal knowledge and document it in the tool itself and not have it lost in Word files, emails, or Slack channels.
Profiling Unstructured Data using Natural Language Processing
Ingest unstructured data like PDFs and HTML files and the Catalog will convert it to a consumable form for you to clean, shape, and create models and reports with.
3. Mask sensitive data with the power of Policy driven transformation
Never worry about governance again because what you see is what you can get. The Catalog masks sensitive data automatically and gives access to users to more data. User can use the Rule Builder in the Policy Manager and enforce policies and rules based on writing conditions and assigning an action.
For example, If asset contains PII data, then deny access. Conditions use terms and operators to specify the relationship between data and users.
Up until now, governance tools enabled enterprises to document their business rules and policies regarding their data. With the impending GDPR enforcement date of May 25th, 2018, policy driven transformation in the Catalog can finally help enterprises actually enforce their policies and rules on the data rather than just documenting them.
This is innovation.
4. understand how it all connects together: Visual Map of Related Content
Through speaking to Chief Data Officers, we learned that not only are structured or unstructured data files are data assets, they also view the enterprises' policies, rules, and business terms as data assets. Governance teams want to know which assets are governed by which governance asset— what rules and policies are governing this particular asset? Additionally, business analysts and data scientists want to know if what other users have used this asset and if they can safely access this asset and if not, what policies and rules are blocking their access. Users can click on the related content tab in the overview page of an asset to view details about the associations of the selected data asset.
Understand how assets are all connected together through a visual map of related assets that reveal related policies, projects, rules, terms, and users.
5.Document and share tribal knowledge: Ratings and Reviews
From user research, we’ve heard that business analysts and data scientists rely heavily on each other to complete their journey from finding data to delivering an analysis for business decision making. This tribal knowledge comes with time working at the company and asking other colleagues about which data to use and where they can find it. We asked ourselves, what if we could aid in that process? Can we introduce a social component to the Catalog inspired by the "shop for data" metaphor?
Ratings and Reviews
Leverage the expertise of your colleagues by reading reviews of assets in the Catalog and contributing your own review to help others.
Shipped for IBM Think Conference 2018
Our team shipped four new user experiences for Watson Knowledge Catalog for the IBM Think Conference in March 2018. I worked with offering management and engineering leads to prioritize the user experiences that was GA and demoed at Think.
Recognized Leader in 2018 Forrester Wave
The Forrester Wave: Machine Learning Data Catalogs, Q2 2018 names Watson Knowledge Catalog a leader.