The current state of data analytics is broken.
Data professionals have to react to endless requests, bottlenecking data-driven decisions. Repetitive data tasks are done by hand over and over. Data is scattered across multiple sources – painful hours are lost pulling it together.
Today, all that changes.
Data workflows today
As data becomes pervasive across organizations, the focus shifts to methods of utilizing that data to make intelligent decisions. Currently, such methods – if they exist – often exist in the form of R/Python scripts or Excel spreadsheets with complex formulas.
As a result, more often than not, this means that the analysis must be run by someone on the engineering/data team, and must run locally – which means that these methods of analysis are non-reproducible, and non-scalable.
So what happens when “other” teams want to utilize data to make decisions?
Let’s say you run the sales ops team. Once or twice a week you nudge your colleague, the data scientist to pull down data from the CRM, combine it with information from a spreadsheet you maintain, run some aggregate statistics and generate a couple charts to display trends.
This task is boring and laborious, and always needs you to rely on your data scientist colleague.
What happens when you have a lot of sales leads and the analysis freezes up her computer?
What if she goes on vacation for a couple weeks?
Such data processes are ripe for automation. In an ideal world, the data scientist would set up the data workflow once, turn it into a delightfully easy to use interface for you. And every week, you could just upload your spreadsheet and let the workflow handle the rest.
Well, the ideal world is here!
Since we launched in 2019, our goal has always been to make data-driven decisions easy for all teams. Today we launch Intersect Labs 2.0, a platform that will help you intuitively build data workflows and turn them into easy to use interfaces; in other words, Intersect Labs is your one stop shop to build data apps.
How do I build a data app?
Components of a data app:
At its core, any data workflow is a combination of the following components:
- Import data
Bring data in from one or more sources (e.g., database, spreadsheet, CRM etc)
- Process data
Run some computation on data (e.g., add two columns together, combine datasets, run aggregation metrics, make predictions using a machine learning model)
- Trigger action
Use the data to drive an external action (e.g., update Facebook Ad spend, notify inventory manager in a warehouse)
Turn the data into a graph to succinctly present a set of insights or conclusion
- Export data
Update some datastore with processed data (e.g., add new rows in a database table, update field in CRM)
With this in mind, data apps can be assembled on Intersect Labs by using building blocks in these categories.
Out of the box, Intersect Labs comes with about 60 pre-built data compute blocks, called transforms.
Want to multiply two columns? Add the “Multiply Columns” block, done. Want to calculate the difference between two date/time columns and express results in hours? Add the “Date/Time Difference” block, done. Want to group by two columns and calculate the sum, mean and median of another column? Add the “Aggregation” block, done.
Once you pick a block to add, you will be able to configure it using a mad-lib style form. Go ahead, say it out loud; it’s like you are explaining what you need done to your colleague.
If your computation needs are not satisfied by the available transforms, just let us know – we will create that transform for you. If you are okay with it, we will add it to the public gallery. If the computation is proprietary, no worries; give us a heads-up and we will make it available as a private block only to you.
We get it. Your data is in many places, and you like it that way!
On Intersect Labs, you can bring in your data from any source. The sources supported currently are:
- Excel spreadsheets
- Google Sheets
- Facebook Ads
Why Intersect Labs?
Build once, reuse over and over
After being built, a data app on Intersect Labs can be published and made available to other teammates. Once published, you can run an app at the click of a button, or set it to be triggered on schedule.
True data-driven decisions are made only when multiple stakeholders participate in the process. Intersect Labs is built with a strong opinion that analytics is a team sport.
Easy sharing means complex workflows can be built by data professionals, and after publishing can be used by personnel on other teams. Everyone rests easy – the data team knows that data is only analyzed using processes they have blessed, and users don’t have to worry about implementation details.
Powering data apps built on Intersect Labs is an insanely fast and infinitely scalable custom built compute engine. Whether your data has a few hundred rows or millions of rows; whether it has dozens of steps or hundreds of steps, it will always run reliably.
Data analytics almost feels incomplete without drawing a chart to show the outcome. Soon, you will be able to turn your processed data into graphs and charts as a part of your data app.
The integrations on Intersect Labs can currently be used to pull data. In a not-too-distant future, you will be able to send your results to a database, or update a field in your CRM right from your data app.
Start building data notebooks
The future of data-driven decision making is here. If you use Excel/Jupyter notebooks/Python scripts to run the same analyses every day/week/month, this is a complete no-brainer.
We cannot wait to help you make intelligent, truly data-driven decisions.