Bridging the Gap Between Technical and Non-Technical Data Users

Hoshang Mehta
October 4, 2023
Opinion

Today, we have observed that ultimately everyone in a company is expected to be ‘data-driven’- whether they are purely from a business/revenue role or is technical or knows how to code/query.

Whether, its a sales rep trying to meet his quota or a data-scientist trying to make sure his/her predictive model is highly accurate- data is at the centre of every conversation.

Although, everyone is expected to be ‘data-driven’ in their role, are we given an equal opportunity to use data without having any barriers?

Not today- although traditional data solutions were designed for self-service, many deployments of these solutions ended up being “report factories” where the semantic layer was used by technical folks or analysts to create reports for the non-technical folks. This often reintroduced the bottlenecks and frustrations that self-service analytics was supposed to get rid of.

Let Dora explore

At Airbook, we believe that everyone should be given an equal opportunity to be ‘driven by data’ and be successful in their role.

Ofcourse, being ‘driven by data’ is much more than being able to crunch numbers or create charts, but I am writing in the context of giving everyone an equal opportunity to explore data regardless if they’re technical or not.

Let’s unpack the situation today:

  1. You have drag-drops or pivot tables for revenue teams who wants to create quick reports on their CRM/ERP applications e.g Salesforce
  2. An analyst who wants to make sense out of data from two different sources will require ‘joins’ and will eventually go to a SQL IDE
  3. For further complex analysis e.g. predictive modelling- a data scientist may come to rescue and use R/Python to run predictive models on data e.g. sales forecasting.

But you know what the problem is?

All of these interfaces to query or explore data is present within different tools and therefore users have to go tool-hopping almost all the time, while they’re in the flow of exploring data.

This results in chaos- disparate screenshots and URLs lobbed over between teams, the explanations around numbers and how they got there is all lost within numerous slack/email messages- ultimately we see one person bandaging all the pieces on a pretty looking power point presentation.

Data projects are collaborative, but today’s data tools are not!

Why not offer ‘Dora- the data explorer’ all the weapons required to make his/her job easier, in one place?

Interface Agnostic

Airbook is designed ground-up for collaboration and being agnostic to interfaces used to explore data has been one of our core offerings.

Our team are fans of Notion and wildly love their ‘/ command’ approach to easily call tables, texts, headings etc- this does not break our flow of writing and is super duper easy.

Similarly, you can use ‘slash commands’ on Airbook to call for the interface you like-

  • Drag & Drops
  • NLP (coming soon)
  • SQL IDE
  • R/Python code
Airbook is interface agnostic

Not only that, you can ‘drag-drop’ to create a reports and your analyst can pick it up from there by referencing the underlying SQL query that was created.

Airbook allows drag-drop, SQL, NLP or R/Python to explore data

Use cases

Quick Analysis (Ad-hoc)

Business/GTM teams usually live across multiple tools for their various needs (CRM, ERP, Financial Systems, Sales Engagement tools and more) where they create multiple ad-hoc reports and have important metrics/reports/dashboards all lying in different tools (silos).

Airbook provides a unified interface for business teams to quickly access and analyze data from multiple sources. It enables users to create reports with drag and drop functionality, collaborate with other team members, and make time-sensitive decisions.

Examples:

  1. What are some of the top deals/leads my team should focus on?
  2. How have we performed YoY/MoM/QoQ
  3. What does our pipeline look like?
  4. How many meetings did we book this month?
  5. How many leads converted into opportunities?
  6. What are my campaign ROI results?
  7. How is my team doing against Quota
  8. What are some high value opportunities due to close in the coming months?

Exploratory Analysis

For slightly complex analysis and deep-diving sessions that are mostly investigative/exploratory in nature, GTM teams can collaborate with their operations analysts (’Bridge Teams’) to help them create calculations, define metrics and report on important cross-functional KPIs to understand business performance and improve customer experience.

Bridge Teams help Non-Data teams create calculations, define metrics, and report on KPIs to understand business performance and improve customer experience. These calculations may be done through simple calculated fields or code, such as SQL.

Examples:

  1. Are my free users showing growing product usage?
  2. Are new products being used by clients?
  3. How are marketing campaigns performing?
  4. What are monthly/daily active users?
  5. Which marketing campaigns should I run to which customers based on their most valued features/products?
  6. What are some upselling opportunities we can identify based on product usage?
  7. What are my conversion rates across the funnel?
  8. How many customers have not payed their invoice?
  9. How many customers have requested an upgrade?
  10. What are my customers’ NPS score and feedback?
  11. Which marketing campaigns converted more leads into deals? What is the ROI?
  12. How many opportunities do we need to close to hit our Quota?
  13. What is my first meeting to deal conversion rate?

Predictive Analysis

For more complex analysis, where extensive use of code is required to do forecasting or using machine learning models to extract insights.

Data teams that involve data engineers, data scientists and/or analysts come together to write code and answer business questions. Data is often stored across tools, data warehouses where as models are written majorly in Python/R. Airbook gives the flexibility to write python/r programs or deploy already existing models.

Examples:

  1. Based on the current scenario, what is my sales forecast for next month? (Forecasting)
  2. Which of my high value customers are at a risk of churn? (Churn Analysis)
  3. Based on product reviews, what is the overall sentiment of my customers? (Sentiment analysis)
  4. Which region should I expand my business into based on demand?
  5. What is the most optimized pricing we can offer to our customers? (Dynamic Pricing)
  6. Which leads/opportunities have a higher probability of converting into a deal?

Conclusion

Airbook is designed to give everyone an equal opportunity to explore data without having to switch between different tools. It allows users to use 'slash commands' to access drag & drop, NLP, SQL IDE, and R/Python code interfaces, and to collaborate with others on data projects. It can be used for quick analysis, exploratory analysis, and predictive analysis.

FAQs

What is the main challenge with traditional data solutions in today's companies?
What is Airbook's stance on data-driven roles and opportunities?
What are some of the challenges with the current data exploration interfaces?
How does Airbook address the challenge of using multiple data tools?
Can you provide some examples of use cases for Airbook?
What makes Airbook unique in the data exploration landscape?
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