Chevron | Data Marketplace Experience Design

Chevron | Data Marketplace

Lead Product Designer

A Data Marketplace that consolidates multiple data portals and Marketplace experience to streamline data governance.

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Data Marketplace

A Data Marketplace Design: From User Research and Strategy to MVP

Engineers, data scientists, managers, and stakeholders currently struggle to find and utilize data effectively due to the existence of multiple data management portals. The goal is to improve this by creating a more unified access point, enabling seamless data discovery and utilization across the organization.

Daniela

@PM

I need to be able enter key words or terms into a global search bar so that I can discover and find resources on the Marketplace.

Mani

@Engineering

I would like to see the most recent version of a resource by default so that I don't have to make a decision about which version to examine. I need to easily identify the source of the data.

Lauren

@Data Scientist

I would like to contact the resource owner directly (via Teams or email) if the resource is in development so that I can discuss my needs and provide input.

Dann

@Sales

I want my experience with the Marketplace to be localized so that I can find resources more efficiently and find help that is relevant to my work environment.

Davo

@Management

I want to filter search results to find what I want (e.g., filter by Business Unit, Asset, Platform, etc, by resource type CSV, PowerBI, API, metrics, status or deployed.)

Parker

@Engineering

I expect to see terminology defined so that I can determine the meaning (e.g., what criteria are used for a metric like 'most used' -- is it used the most because it is linked to an application that refreshes daily?

Eric

@Product design Manager

As a user, I need to easily identify the source of the data.

Fekry

@Sales Manager

I want to be able to filter the meta data to facilitate my ability to evaluate the resources and determine which products will suit my needs.

Miguel

@Product Owner

I need to be able to determine if a resource is a standardized, corporate resource (IT Foundational Data Set) or a BU-specific resource so that I can make a selection

To identify the core user problems, I conducted regular user interviews; I synthesized key findings by creating interview snapshots capturing high-level information, key facts, insights, and potential opportunities.

Through this process, I identified the following four critical pain points to address. Recognizing this gap, we saw this as a massive opportunity to help users regain control of what their phone says about them.

Design Hypothesis

By incorporating data creation or modification date, ratings, data ownership and usage information, and the number of users who have interacted with the data, that can enhance the user's ability to assess the data's reliability.

Creating a streamlined and Marketplace experience presents a significant opportunity to drive greater internal user adoption and data utilization. By making it easy for engineers, scientists, analysts, and other Chevron employees to navigate, understand, and access high-quality data, we can foster a more data-driven culture, leading to more efficient workflows, enhanced collaboration across teams, and the generation of deeper insights that can optimize operations and drive strategic initiatives.

Keyword Search. Matches the user's query with exact terms found in the dataset's metadata like names, descriptions, tags, etc…

Mani

@Engineering

I specifically used the term 'secondary recovery,' which is industry standard, but the top results were all about well integrity and pressure maintenance. While related, it's not what I need. I had to wade through several pages before I found a dataset tagged with the less common term 'enhanced oil recovery' which was exactly what I was looking for. It wasted a good 15 minutes of my time. The system doesn't seem to understand the nuances of our technical language.

Lauren

@Data Scientist

I'm still getting familiar with the data available here. When I searched for 'machine learning for maintenance,' I got thousands of results, including presentations, reports on pilot projects, and actual sensor data. It's overwhelming! There's no clear way to filter down to just the raw, time-series data I need for model training. Maybe if the system understood I was looking for data rather than documents, it would be more helpful. I feel like I'm guessing at the right keywords.

Semantic Search. Uses natural language processing (NLP) to understand the meaning and context of the user's query. It returns results based on semantic similarity rather than exact keyword matches.

Parker

@Environmental Scientist

The semantic search not only found datasets specifically tagged with 'methane emissions' and 'Permian Basin,' but it also included datasets on flaring activity and fugitive emissions, which are closely related. It even prioritized the most recent data and datasets with high spatial resolution. It seems to understand the broader environmental impact I'm interested in, not just the exact keywords I used. This helps me get a more complete picture.

Parker

@Geologist

Quickly understood my conceptual query about subsurface data in the Bakken! It found both seismic and core analysis data, and even suggested a relevant stress field dataset I hadn't considered.

UX Decision

Feature

Filtered Search

Semantic Search

Precision

High precision

High relevance

Speed

Fast results

Slower Processing

Flexibility

Low

High

Context Awareness

None

High, Understand intent

Complexity

Low

High

To build trust and boost adoption, I turned datasets into reliable products with clear owners and quality indicators. I made them easier to find with smarter search and tailored suggestions, and improved usability by simplifying the interface and workflows.

Initial Interaction: Setting the Stage

Initial View Upon first access, the user is greeted with a clean and welcoming landing page.

Theme Choice with Light Mode or Dark Mode. This caters to user preference and accessibility. The options will be visually distinct and easily selectable with a clear label.

No Forced Login Users can explore the high-level categories and perhaps a few featured datasets without immediate login.

Navigating and Discovering Data:

——— The Search Experience

Prominent Placement A highly visible and centrally located search bar is paramount. It should be the primary entry point for users who know what they're looking for.

Autosuggest and Autocomplete As the user types, the search bar will provide intelligent suggestions based on dataset titles, descriptions, tags, and potentially even categories. This speeds up the search process and helps users discover relevant data they might not have explicitly searched for.

Did You Mean?" Functionality: If the search query yields no results or few relevant results, a "Did you mean?" suggestion will help correct potential typos or offer alternative phrasings.

Categorical Browsing

Well-Organized Categories Datasets will be logically grouped into clear and intuitive categories

Sub-Categories Within each main category, users can drill down into more specific sub-categories to refine their exploration.

Visual Cues Clear icons alongside category names to enhance visual recognition and navigation.

Filtering and Faceting

Refine Search Results On search results pages and category landing pages, a robust filtering and faceting mechanism will be available in a sidebar or expandable panel.

Dynamic and Content-aware Filters, based on relevance of datasets displayed.

Clear Indication of Applied Filters allowing users to easily remove individual filters or clear all filters.

✨

Connect to Content

Add layers or components to swipe between.

Dataset Preview and Details

Concise Search Results Each search result will display key information at a glance: dataset title, a brief description, relevant tags, the last updated date, and potentially the data format.

Detailed Dataset Page Clicking on a search result or dataset card will lead to a dedicated dataset details page.

✨

Connect to Content

Add layers or components to swipe between.

Managing Data Selection: The Cart Experience

Clear "Add to Cart" Button Prominently displayed on dataset cards and the dataset details page.

Visual Confirmation Upon clicking "Add to Cart," clear visual feedback will indicate that the dataset has been added (e.g., a notification, a change in button state, an update to the cart icon)

Adding to Cart

✨

Connect to Content

Add layers or components to swipe between.

Managing Data Selection: Cart Overview

Persistent Cart Icon A cart icon in the header will provide a visual indicator of the number of items in the cart.

Cart Drawer/Page Clicking the cart icon will open a drawer or navigate to a dedicated "Cart" page.

Ability to Remove Items An easy and intuitive way to remove datasets from the cart.

✨

Connect to Content

Add layers or components to swipe between.

Managing Data Selection: Cart Overview

Clear List of Selected Datasets The cart will display a clear list of the datasets added, including their titles and potentially a brief summary.

Daniela

@Product Manager

I need to be able enter key words or terms into a global search bar so that I can discover and find resources on the Marketplace.

Mani

@Engineering

I would like to see the most recent version of a resource by default so that I don't have to make a decision about which version to examine. I need to easily identify the source of the data.

Lauren

@Data Scientist

I would like to contact the resource owner directly (via Teams or email) if the resource is in development so that I can discuss my needs and provide input.

Dann

@Sales

I want my experience with the Marketplace to be localized so that I can find resources more efficiently and find help that is relevant to my work environment.

Davo

@Management

I want to filter search results to find what I want (e.g., filter by Business Unit, Asset, Platform, etc, by resource type CSV, PowerBI, API, metrics, status or deployed.)

Parker

@Engineering

Eric

@Product design Manager

As a user, I need to easily identify the source of the data.

Fekry

@Sales Manager

I want to be able to filter the meta data to facilitate my ability to evaluate the resources and determine which products will suit my needs.

Miguel Bruna

@Product Owner

I need to be able to determine if a resource is a standardized, corporate resource (IT Foundational Data Set) or a BU-specific resource so that I can make a selection

Hypothesis

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Highlights

Discovering and accessing data quickly.

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Shifting to an operating model that treats data as a product significantly enhanced data accessibility and discoverability. This approach streamlined data access and utilization, ultimately reducing the overall time required to execute data-driven use cases.

Reusable & Reliable data

Create, discover and access data products across multiple use cases to minimize duplicate efforts and reduce costs through a reliable internal data marketplace.

Share data products

Enable large-scale sharing of data and analytics products sourced from disparate source systems, such as data bakehouses and data catalogs.

Governed data sharing

Embed governance mechanisms to provide assurance that data products are shared and used in a compliant manner across all use cases.

Automated data lifecycle

Gain efficiencies in how data products are packaged, shared and consumed by automating and managing data as a product across the lifecycle.

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