Client Background

Overview
Stone is a video bibliographic tool for journalists and other researchers.

It allows users to capture, annotate and share their journeys through digital and physical space, producing verifiable logs and generating monetizeable video highlight reels that can be embedded in digital and other media – showcasing key moments and telling the story behind the story.

Our mission is to address distrust and disinformation with transparency and authenticity, while simultaneously tilting the information ecosystem in favour of quality original work.

Research is valuable. Make it Visible.

Write In Stone.

Website
http://www.writeinstone.com
Company size
2-10 employees
Headquarters
Blackheath, New South Wales
Founded
2017
Specialties
Research Transparency, Trust, Video Content, Journalism, Proof Of Work, and Bibliographic Standards

Project Objective

  • Working on Microsoft Azure Analytics Services
  • Verifying that indicators are being gathered in an intended manner, in line with GDPR provisions
  • Building and analyzing dashboards and, specifically, conversion funnels

Project Description

  • To determine whether the already implemented indicators in are in intended fashion (separated by where these indicators are placed in the currently constituted funnel)
  • Implement New Indicators
    • Research Logged
    • Average Number of Highlights per Project
    • Total Hours of Content Produced
    • Total Hours of Content Watched
    • Daily unique visitors engaging with Stone, including the landing page, public research page(s), and the research portal
  • Assess the dashboard set up in Azure, refine the existing dashboard, and determine whether an alternative is preferable. 
  • Review, refine, and optimize the WIS conversion funnel(s)

Our Solution

Built a Power BI dashboard as per the requirement. Also built a separate dashboard for the metric data from Azure.

Project Deliverables

Power BI dashboard which contains indicators funnels, new indicators(Research logged, Average number of Highlights per projects, Total hours of content watched etc), visualizations  extracted from metric data.

Tools used

  • Power BI
  • Azure

Language/techniques used

  • Power BI
  • DAX
  • Kusto Query
  • Azure

Skills used

  • Data collection
  • Data Analysis
  • Data cleaning
  • Feature engineering
  • Querying
  • Visualization

Databases used

  • Azure database

Web Cloud Servers used

  • Azure

What are the technical Challenges Faced during Project Execution

Difficulty in data collection.

RELATED ARTICLESMORE FROM AUTHOR