The great wall of Data

The rise of data-driven decision-making has led to an increasingly complex data stack. This can be a great thing, as it offers more options for solving problems. On the other hand, it can also be a challenge. Go-to-market teams - those focused on product, growth, sales, marketing, and customer success - are the primary beneficiaries of an effective data stack. However, the buying decisions for these tools and technologies are often made by data teams, leading to potential misalignment between the two. To ensure successful outcomes, GTM and Data teams need to work together to ensure that the data stack meets both parties' needs.

Non-data teams are those who use data to gain insights and act on those insights, but don't operate on data themselves. They usually work in Go-To-Market (GTM) teams. Data teams are those who actively work with data- they can write queries, perform ETL or even write R/Python code to do predictive modelling.

In between, these two there are ‘bridge teams’- they usually are semi-technical, having a unique ability to understand technical jargons in business context. They are usually very important to gather business requirements and convert them into functional requirements for the data team. At the same time, they can draw insights from data and explain in a way non-data teams would understand. They usually work in Sales Operations, Business Operations, Marketing Operations, Customer Success Operations and Revenue Operations. Sometimes, you may also find them within the Finance teams.

These three teams often work together within the analytical workflow and the projects they undertake are collaborative, but traditional data tools are not! Let’s first understand what non-data, bridge and data teams really want.

What Non-Data Teams need?

At the very least, GTM wants to be able to quickly visualize how the product or service is being used — where are the gaps or opportunities that impact revenue — using this information they can be empowered to personalize their outreach to customers and construct informed strategies.

GTM teams need-

• An easy and quick way to record and communicate data requirements to ‘bridge teams’ or ‘data teams’- Often times, these requests are ad-hoc and time sensitive

• Once the required data has been shared, they need an easy way to add context to drive a certain narrative for why things are the way they are

• If they need more information or have follow-up requests, they need an easy and traceable way to go back and forth with other teams

• When its decision time, they need an easy way to collaborate within their teams or outside to either share results or validate their decisions/next steps together

What Bridge Teams need?

Bridge teams often work on the interface of Non-Data Teams and Data Teams- they are ‘semi-technical’- working as both, operators and consumers of data. They are actively involved in requirement gathering and presenting insights to help GTM teams make better decisions.

They translate vague business requirements into functional data requirements (for the data-teams) and convert raw data into presentable insights for GTM teams to consume and make decisions. In their wider scope of responsibilities, they also work on building and maintaining business processes as per the changing business needs.

Bridge teams need-

• An easy way to gather and refine requirements from Non-Data Teams and communicate them to the Data Teams

• A single page where they can connect to various business systems and create reports themselves, add context and present data in an easy to consume format

• Help business teams make data-driven decisions efficiently

What Data Teams need?

It’s important to acknowledge that Data Teams caters to the needs of all teams and not just GTM teams — their scope of work is much larger- which includes setting up the right data infrastructure and pipelines.

Data teams need-

• they can make data available for analysis and activation

• that connects to their data warehouses and existing tools where they have already collected data from all possible sources, written SQL to transform or model all types of data for all purposes.

Data teams are generally averse to tools that don’t neatly fit into their existing workflows and require them to build and maintain additional data pipelines or do double work for every data request.

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Let’s break the wall

Today, the modern revenue tech stack is saturated causing fatigue amongst non-data teams when it comes to the number of new tools they need to adopt to.

Reporting on these tools is complex and requires hours and hours of onboarding and training. Every tool has a different semantic layer that is technical enough and almost always ends up being used by an ‘admin’ to generate reports for business teams. This often introduces bottlenecks and frustrations when it comes to delivering insights to decision-makers who are non-technical.

Insights from different revenue tools need to be un-siloed in a way that non-technical teams can operate and derive their own insights from different tools all in one place without having to go back and forth with admins or data teams.

Airbook is ‘notion-like’ that lets you connect to any tool existing in your tech stack and lets you derive custom data insights all on one page.

One page that brings data teams closer to non-data teams as well. Insights that are sophisticated to derive and require writing SQL to various data warehouses such as BigQuery or Snowflake happen either in BI tools such as Looker today. A familiar SQL IDE for data wizards to write SQL allowing them to break down analysis into re-usable SQL logic blocks and add their commentary is essential.

Airbook is the single point of contact between non-data teams, bridge teams and data teams.

Data requests are usually are lobbed over from the non-data teams to bridge teams or data teams. A lot of ad-hoc projects, where there is an ask or problem to be solved from the business which may or may not be outside the technical domain of the requestor. These requests can come from anywhere across an organization- whether that be the sales teams, leadership, operations, product, CX or marketing.

The quicker teams can explore the data, answer the questions, and share out the results, the faster organizations can make better decisions.

Previously, analysts would be writing disparate SQL queries on BI tools or elsewhere, copy-pasting charts, and trying to make them reproducible using Git for analytics storage. Where as Non-data teams would constantly go back and forth over emails/slack or create endless siloed reports across tools that are difficult to synthesise into a bigger picture.

Today, we can accomplish much of that workflow in Airbook which allows to move much faster as a data organization.

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