In this guide:
Why create data notebooks?
Data drives decisions, but to use the data in a meaningful way, teams must perform complex manual data operations. Usually, business teams would depend on the data team to solve these tasks for them. As a result, the data team would need to handle many repeated ad-hoc requests, delaying the work of different departments. Neither side of this process is getting the best deal.
If you touch data or are responsible for making data-driven decisions, you have likely faced one or more of the following situations at work:
You have built a pretty sophisticated python routine that ingests data from numerous sources, runs some operations on it, dumps the data into a spreadsheet, emails the spreadsheet to someone, and records the results in a database. The routine runs on your computer in a Jupyter notebook, which means that people are always bothering you to run the newest results. You wish you could train the non-technical users of the final spreadsheet to run the python routine themselves.
- You have created a formula-rich Excel spreadsheet that performs a series of lookups, pivots, and aggregations to ultimately generate a set of graphs that the executive team relies on to make data-driven decisions. You run this first thing every morning. Unfortunately as the scale of the business grows, your spreadsheet slows down considerably.
- You have built a machine learning model for your sales team to be data-driven which leads to prioritization. The accuracy is great! However, your sales team is too busy closing deals to learn python in order to run the machine learning model. You host your model for them and build a user interface to score leads. Unfortunately, after a couple of weeks of using the interface, the sales team now wants to send the predicted lead scores to Salesforce -- which means you now have to not focus on your core data competency, but rather on figuring out connections to Salesforce, and productionizing the integration.
In each of these cases (and others), you would have benefited from data notebooks. You would have been able to focus on your keen sense of going from data to insights without having to worry about productizing operations or building fancy user interfaces. With data notebooks, you would have built your workflow once, and then empowered your business colleagues to independently make data-driven decisions. Finally, you would have also rested knowing that your colleagues were always using data workflows you had developed and signed off on, ensuring accuracy.
Who needs data notebooks?
You can build data notebooks for various use-cases. It can be a data notebook that powers your reporting, or a data notebook that analyzes your referral rates and sends you a notification every Monday morning. To understand how different departments are using them, let's look at some examples from our customers.
For Data Analysts
Working as a data analyst means you get a lot of data requests from other teams. Automating these routine tasks and concentrating on more productive work is crucial. Intersect allows data teams to design and build data notebooks that are easy to use. This way, analysts focus on high value work while empowering business teams to do more with their data.
Marketers rely on several channels to run campaigns, so they need to combine data from multiple sources in order to analyze and understand the marketing funnel. With Intersect, marketers can easily combine data from multiple platforms and visualize it into beautiful reports that can be triggered on schedule.
As data becomes abundant, executives need to make better decisions while measuring, and hence knowing, how their business is doing. This requires them to develop an organization-wide, data-driven culture. Top executives embrace data notebooks as a way to empower a variety of teams to be more productive with data, as well as to succinctly measure KPIs and visualize success on their board meetings or investors' pitches.
For Sales ops
Sales professionals must analyze all the moving parts of the sales pipeline. Sales ops use Intersect to create automated reports, visualize charts by request, and forecast sales revenue.
For Customer Success
The highest priority for the customer success teams is to understand the customer journey. From onboarding flows and usage data to analyzing retention and forecasting customer churn, data notebooks power customer success teams to do all of that and more.
For Product teams
To improve and analyze the effectiveness of a UX change or a feature release, product teams work with large amounts of data. The ability to self serve on this data and make timely decisions is crucial. The data notebooks on Intersect are extremely intuitive, so creating visual charts to analyze adoption or inform product roadmap decisions has never been easier for these teams.