Need Help To Optimize Your Data In Panoply?
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You may have recently invested in Panoply to improve your team’s ability to access data. Panoply’s automated ELT process makes it really easy to integrate data from multiple sources including Salesforce, Workday, Asana, QuickBooks, and so many other tools into your company’s data warehouse.
The simple GUI based integration process makes it very easy to connect to all of the various data sources and cuts away a lot of the heavy lifting associated with connecting to APIs and databases with code.
However, there is a problem.
What do you do once you get your data into Panoply?
In this article we will discuss some of the problems new users have with Panoply and how your team can solve this.
Data Transformations And Melding
It’s great that you didn’t need to write any code to get your data into Panoply, but the data is far from perfect.
Oftentimes when you pull in data from tools like Salesforce, the data might have duplicate rows or other data issues.
In addition, you might want to meld this data together with other systems that might have data at different granularity levels or just need some general planning as far as how you will join it together.
All of which might not be in you or your analysts wheel house.
This is because complex data transformations require a good amount of both SQL and data modeling knowledge.
Dashboards And Metrics (KPIs)
On top of transformation, many teams and companies using Panoply will often want to create dashboards and metrics to help them better track performance of their key business processes.
There are loads of dashboard tools that can make developing dashboards simpler than it was two decades ago. Despite this, between the time that it takes to develop dashboards, as well as the fact that it is still not that easy to develop dashboards if you’re new to the idea, business owners and start-ups still struggle to push these dashboards out.
In fact, Tableau courses and certifications range from close to a hundred to nearly a thousand dollars. This of course doesn’t even take into account all of the other possible dashboard tools your team might consider using like Looker or Quilkview.
This is because building a dashboard is much more than just dragging and dropping charts around. We do cover some tips for developing dashboards in a previous article. However, overall, much of the finer points require a general understanding of UI/UX as well as dashboard best practices(on top of the technological challenges).
Another issue you might run into is actually developing metrics. Some industries have key metrics defined, but not all, and perhaps not for what you are trying to measure. In turn this further adds challenges when it comes to developing easy to use and understand dashboards.
Solution To The Challenges You Face When Using Panoply Or Similar Products
Here is a great way to understand the solution.
Small businesses likely pay other specialist like accountants, lawyers and salespeople to provide experienced know how in niche skill areas.
In the same way, your team should hire experienced data professionals to manage your Panoply instance, data transformations, dashboards and analytics.
However, there is a problem here.
An expert data engineer easily makes somewhere form $100k-$180k a year depending on whether they work at a small company or big tech.
Generally speaking, small and medium sized companies can’t take this kind of a cost on.
That’s where our team comes in.
We are experienced at helping small and medium sized companies get started with data. We offer services that can help your team get started with data without taking on an entirely new employee.
This year alone we have already helped multiple companies in the healthcare start-up, transportation and Saas industry develop and implement dashboards, machine learning models and data warehouses into their workflows.
We can do the same for you.
Contact us today for a free consultation on how our team of expert data science and engineering consultants can make sure you get the most out of your Panoply instance.