[VIDEO]: Using Your Fused Data
In this movie I’ll take you on a quick tour of your Fusion Warehouse and show you how to use it. Specifically, I’ll show you how to connect your Fusion warehouse to any business intelligence tool and query all your systems with a single join.
If you’re a SQL novice or a visual person, like me, don’t worry. I’ll also show you how Fusion opens the door to visualizations that were once impossible.
We’ll also look at how Fusion’s Warehouse keeps your data up-to-date and in a format you can use for any analysis or report.
A lot of BI tools will allow you to see your tables and drag and drop them on the canvas to make relationships without having to write a line of code. Here I’m using Yellowfin BI. And I can see all my tables -- fused, raw, and links -- right there on the left hand side.
In this example, I’ve dragged my fused_company, fused_opportunity, fused_contact, and fused_owner tables onto the canvas. This means I can run a query on all my companies, opportunities and owners from all my connected SaaS applications.
The power here is that I can get a view of an account’s entire experience with my company. What were once monstrous queries across multiple systems are now really simple.
And again, I could do this in any number of BI tools. Amazon Quicksight lets me select my tables on the left-hand panel and visualize the joins as well. I could run a count of records by industry and lifecycle stage, which I’ve done here in this visualization.
Where I’m returning values for my Marketing Qualified Leads, Customers, Leads, Subscribers, and Opportunities -- on the left. And on the bottom I have my industry -- from Textiles and Farming to Trucking and the Computer Games industry. At which point I could get rid of null values and tweak the visualization however I’d like.
And this means I can complete not just my visualizations super fast. I can also easily join any object by using extremely rudimentary SQL.
In this example, I’ve got the fused_company table selected and I’m just joining it with the fused_contact table by accessing the links_company_contacts table that binds the two together. Now this would return an at-a-glance view of my companies and contacts.
But I can also do way more than that if I want to.
Tickets, for instance, are historically difficult to report on. At Bedrock, we use HelpScout as our support system. And as great as HelpScout is, its data lacks a Company Object; its tickets are instead associated with Contact. With Fusion, I could just link Tickets to Contacts and Contacts to Companies, using the Contact_Ticket_Links, Company_Contact_Links, and Tickets tables. This I can do by selecting the tables and parameters, or running your own SQL query, filtering by date, and ordering by month and company name, using an inner join.
In Metabase, I could view the results returned either as a visualization -- a Table, Line, Area, Bar Chart, Scatterplot, Pie chart, Map, or Funnel, and refresh data in near real-time.
And here I’ve pictured my results as a line chart, by month, of the average count of tickets across all my accounts. I have an entire picture of everything. All the data I own is right here. And so I’m able to see that, actually, my average count has been holding steady over time at around 1.5 tickets.
So by joining these three tables I’m just accessing the fused records and binding them together with the links tables. Which has saved me a ton of time by automating the process of data ingestion and prep. And keep in mind that this data is as fresh as it gets. It’s in real-time. So I don’t have to worry about missing last month’s, or last quarter’s data. I’ve got everything I could ever possibly want right here in front of me, up-to-date, with bullseye accuracy.
So by now, I think you’re probably starting to notice a pattern that it really doesn’t matter which BI tool I use. I can use any that fits my needs. And in this example here, I’ve switched to Microsoft Power BI, and I’ve selected Contact, Company, and Opportunity data from my Fused Warehouse in the Manage Relationships modal window.
Notice here that I have the corresponding links tables tied to my fused tables. And so in my tenuous hand-pencil-sketch pad analogy, I can now begin drawing visualizations on the Power BI canvas. Like so.
And now let’s imagine I want to drill down into all my sales deals over time and break these down by business unit, in a similar way to what we did in Quicksight.
Well, we could drill down by year or by quarter. And if we care about months instead, in the Visualizations panel we can add whatever level of specificity to the Axis.
And now I could slice and dice this data a hundred thousand ways.
I could select the closed_date, amount, and territory, where the territory originates from the Company. All I’d need to do is drag these fields to visualize my deals by territory and break them down by whatever business unit I want (like engineering or marketing).
I could also use Power BI's drill down function and any adjustments I make to one chart will pipe into any corresponding charts I have on the canvas. I can also try to add types of fields (like business_unit or deal_stage) to see how they add dimensions to the data. I can then alter the Visualization to better suit the dimensions I add or subtract. I can also move the dimension from the Legend into the Axis field so that the field becomes yet another drill-down dimension. So if I’m interested in seeing how many of my deals are associated with each business unit, the visualizations could drill down to the different deal stages such as MQL, closed-won, and so on, within the different business units (like engineering, sales, or marketing).
And that, folks, is how Fusion cleans and combines records into an on-demand data warehouse which you can easily Connect to any BI Tool for quick analysis, either as a query or visualization. All with zero code or hardware.