Hey Dashboard, you look cool but can I trust you?

Introductions

If I were in a relationship with my dashboard, I’d have major trust issues. The tiniest doubt can take away the peace from your mind, and the connection will not be as fulfilling as you expect.

Once a dashboard is shared, this is how we usually start spiralling down-

“Something is fishy, can I trust this data?”

The next thing we check is-

“Who built it?”

“Did it change recently?

“hmmm, it somehow doesn’t look right? “

“How is this number calculated?”

“Why did we choose this logic?”

“Oh maybe it’s still referring to the ‘new_arr’ field we discontinued using from last week”

Dashboard behind 'bars'!

If there are trust issues, the dashboard is already in the process of being sentenced to death! This leads to a series of investigations between revenue and data teams to find out the proof for it’s innocence (or not). The investigations are majorly an endless chase across countless tools, spreadsheets and emails to prove the business logic leading up to that number.

Typically, the convicted dashboard is given one more chance where the data teams present the business logic behind those charts. These proceedings take time, a lot of time and happen over meetings, emails, slack messages, phone calls (all lost in transit and nearly impossible to trace back). If the business logic, which was once valid is no longer valid in the changing business context- the poor dashboard is beheaded with a sharp edged sword called ‘irrelevance’.

Often, the amount of time teams take to verify if the number is right is far more than the time they take to debate what data-driven business decision to make.

That’s when I realised that we had serious trust issues — some of the major reasons-

Dashboards offered very little freedom to view the complex underlying business logic (i.e code) that was used to show a chart, every person has a different definition of what the metrics should mean and sometimes they just didn’t trust the data entered in their CRMs (this is a separate problem in itself!).

How do we build data trust?

Encapsulating business logic into data movement and presentation is a critical part of a stable information management strategy. Too often, though- business logic is abstracted or is built and added late in the process making it difficult to build trust in the data presented.

We build trust in data by exposing the business logic, empowering people to validate data together, and allowing the author to add commentary to explain what the data means. This helps to ensure that data is accurate and up-to-date, as well as to create an understanding between the data and revenue teams.

This type of transparency helps to build trust in the data, as well as create a culture of collaboration and accountability. Additionally, having access to the underlying business logic allows for better insights into the data, as well as more accurate and timely decision making.

By exposing the business logic to everyone concerned, we are able to ensure that data is trustworthy and reliable. This helps to reduce the time spent on investigations and debates, and enable more efficient and effective decision making. Additionally, it helps to foster a culture of collaboration and accountability, which is essential to building trust in data.

Building confidence in data with Airbook

We are building Airbook with data trust in mind -making it possible for everyone to collaboratively see the underlying business logic behind numbers. This helps to ensure that the data is accurate and trustworthy, giving users the confidence to make sound decisions.

Airbook allows users to easily get answers to the following-

• What is the business logic used behind queries or calculations?

• Where is the data coming from?

• What do the calculated metrics mean?

• The underlying SQL code

• Is the query using the correct or the recently updated fields?

• Who is the author?

• The author’s commentary to answer the ‘why’s’ and ‘how’s’ behind the numbers?

• When was the data last updated?

Airbook's intuitive interface allows users to collaboratively explore the data, understand how it is calculated, and identify any discrepancies. With the ability to quickly share insights and feedback with other users, Airbook helps teams stay up-to-date on their data and make decisions that are backed by data.

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