Data Provisioning
Simple Drag and Drop Interactions
Create Complex Data Models in Minutes
Find Key Insights from Multiple Data Sets
The Data Provisioning project was developed to allow users to create complex data models using a simple and easy to use web interface. With the data provisioning application, users can upload their big data tables, cleanse their data sets, and then join those data sets into a data model. With the data model, users can find key insights in their data, create comprehensive visualization dashboards, and support complex web applications. The Data Provisioning application was released in 2016 as a part of the Magellan iHub product suite.
Design Tasks
Background
As the rise of big data began to sweep through businesses and corporations, users of big data applications began to shift from technically minded data scientists towards business users and marketing professionals. These users wanted to be able to work with data sets to find insights into their specific market segment, but were often reliant on a handful of data professionals, or third party individuals.
The Data Provisioning application was designed to open the world of data modeling, blending, filtering, and more to all users. Key stakeholders can use the data provisioning application to discover hidden relationships, patterns, profiles and trends to make fact-based decisions.
User Personas
Jean – Marketing Professional
“Give me an edge into the market.”
- Looks for key market segments
- Desire for data driven information
- Moderate technical proficiency
- Strong communicator
Sarah – Chief Human Resources Officer
“I need the right information at my fingertips”
- Oversees entire Human Resources department
- Needs accurate and easily understood data
- Working towards equitable company demographics
- Avoids manual processes
Easy Data Modeling
Adding Data
The first step in the process is to add data to the application. Users can easily upload data tables from their local machine, or use existing data that lives in their iHub environment. Cloud data sources would be added as a future enhancement, giving users the ability to blend data from virtually any source.
Data Cleansing
An important step in creating complex data models is the data cleansing process. Good data is vital to ensuring that insights are accurate. The data provisioning UI allows users to review their data sets, edit column names , edit data and analysis typing, create filters, construct computed columns, and more.
Data Joining
The final step in the data modeling process is to join and blend the data sets together. Users can easily create data models, from their most simple to their most complex, in minutes.
Prototype and Mockups
Adding Data
Drag and Drop
Import Data
File Queue
Cleansing Data
Edit Columns
Users can redefine their columns, remove columns, and rename columns for any data table.
Filter Data
Users have the ability to filter their data tables, allowing them remove noise and focus on the segment of data that matters to them.
Computed Columns
Users can create computed columns, combining the values of multiple columns within the same data set.
Joining Data
Drag and Drop
Users can drag and drop one or multiple data sets to create joined columns.
Join Data
The join modal allows users to define the type of join that they want to create.
Reception
The data provisioning feature was developed and released for OpenText Magellan iHub. The feature was well received by existing customers and new customers as it immediately opened up data modeling to users with varying levels of technical expertise. The Data Provisioning application helped kick start the adoption of web-based big data applications throughout the market.
Special Thanks
No project is completed alone. A very special thank you to all of my teammates who helped turn this project from an idea to a reality. Their hard work and dedication to improving the product is what makes projects like this possible.