Metrics Store Core Principle — Design for Everyone
Metrics Store in Action #4–2-minute Tech Tok for 2 years’ Implementation
In case you’ve missed the previous articles, here is where the story begins.
I believe now is a perfect time to share Pandora’s Design Concepts with you prior to introducing advanced AI features. Hope this will help you fully understand why this platform was built this way.
Core Principle — Design for Everyone
Universal design, as defined by Andrew Maier, is a design methodology that “describes a set of considerations made to ensure that a product, service, and/or environment is usable by everyone, to the greatest extent possible, without the need for adaptation or specialized design”.
by Marsha Chan, from Universal Design for Learning
Pandora’s mission is to enable EVERYONE to become a Data Scientist, regardless of age, data literacy, and technical skills. To make the seemingly impossible possible, the ONLY way is to keep it as SIMPLE as possible - but no simpler. Thus, Pandora leverages Intuitive Design and the power of AI to make the mission impossible possible.
Here are three HOWs…
#1 Extremely Intuitive User Interface
From Day 1, Pandora focused on making an extremely intuitive user interface to allow non-technical folks to complete the entire data analytics workflow in a 100% self-service manner. In fact, before the project kicked off, the team researched the solutions existing on the market and found that most implementations are deeply influenced by DevOps culture. This means it is a bit techy for business users who need to learn to use YAML files from scratch. Therefore, they did not go down the same path. Remember, Keep it Simple and Design for Everyone!
#2 Reverse Data Discovery
This is a term I borrowed from the concept of Reverse ETL. Similar to that term, Reverse Data Discovery is a feature created to assist metrics and insights in finding and selling themselves to target business analysts. Unlike the association between people and data defined in Data Discovery — people proactively explore data, Reverse Data Discovery reverses the direction — data discovers people.
Metric Recommendation is the virtual implementation of Reverse Data Discovery. It is a fantastic feature to help boost data literacy across the company and make knowledge sharing even more effortless for everyone. It’s just like e-commerce companies kept “bothering” you with similar stuff after you bought some items there 😉
Poor data literacy is ranked as the second-biggest internal roadblock to the success of the CDO’s office, according to the Gartner Annual Chief Data Officer Survey.
#3 <Actionable> Advanced Analytics for Everyone
An advanced analytics process comprises 4 phases: descriptive, diagnostic, predictive, and prescriptive analytics. Each of these phases is more complex than the previous one but provides more value. Most dashboards or business reports provide you with phase 1 analytics, only figures and facts. Those data points are meaningless for the target audience because they rarely drive actions. To trigger people to act on data, the data itself needs to, firstly, tell people what happened or what is happening right now — Descriptive Analytics; and secondly, reflect on the past, why did it happen — Diagnostic Analytics; thirdly, predict the future, helping the audience get prepared and manage risk— Predictive Analytics. ( Read this blog for why & how — The Dark Secret of Making Data More Influential and Actionable )
Pandora product team firmly believes the value of data is not in data itself, but how people react to it. To make insights from the metrics store more actionable, they came up with some AI features to deliver the first three types of advanced analytics: SMART Attribution — analyzing and visualizing what factors impact the fluctuation in metric values, Predictive Analytics — predicting the near future values of each metric, and Metric Monitor for anomaly detection.
Back to the point, those features were initially built for everyone to apply advanced analytics without knowing how to develop complex machine learning models. Simply looking at the final outputs of pre-trained ML models is good enough for them to take actions.
This is how Pandora democratizes data science and helps build data science literacy for non-data scientists across business functions. By executing the concept of Design for Everyone, Pandora is ready to create a business impact at the early stage of their company’s Data Analytics Marathon.
Quick Links
Enterprise Metric Platform in Action #5
Enterprise Metric Platform in Action #4
Enterprise Metric Platform in Action #3
Enterprise Metric Platform in Action #2
Enterprise Metric Platform in Action #1
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