In large organizations, data is a critical component in planning business strategies and measuring success.
As a company grows, though, their data needs and technology stack become increasingly complex, requiring technical teams to maintain all of the moving pieces. Very often in these situations, digital marketers can find their request to handle and manipulate data stuck in the list of to-do’s by their more technical counterparts.
While data teams do their best to help digital marketers improve performance, long backlogs are a common occurrence. This can lead to inefficient workflows between the teams.
Microsoft-dominant businesses typically have slightly more technical data workflows, which is due to them often being larger in size.
In organizations like this, marketers rely on technical teams to manually pipe their data into Power BI. There are a range of ways to accomplish this, for some data teams it means building one-off connections to achieve what the marketers request, and in others it means exporting from a data warehouse into a visualization tool.
Build-your-own solutions require strong technical skills, lots of time, and (more importantly) maintenance.
If we zoom in on the building phase, it starts when marketers request data to be added into their reports – whether it is new channels, new metrics or new dimensions. Technical teams must then add these requests to their backlog. This is the first example of inefficiency in your data pipeline.
Next, is the iterating phase. Although marketers often have a good idea of the data they want to pipe into their visualization tool, it isn’t always easy to know exactly how that data will look. This means marketers need to iterate on their reports and potentially request manipulations to their data.
It’s important to remember that a marketer’s needs when the data connection is first made will likely change over time as a result of iterating and honing their understanding of the data. Which also affects the next phase.
In the maintenance phase, it is likely that marketers will want to regularly update the data they want to work with in an effort to keep up with the ever-changing digital landscape. This means more requests are added to the backlogs of data teams.
Additionally, martech APIs aren’t always stable and reliable. There are often technical issues that require maintenance on the connection. In these instances, marketers will experience breaks with their dashboards and reports while putting more strain on data teams with stressful requests to fix connectors so they can access their data.
During all these phases, data teams are put under stress and marketers are frustrated at how long things take.
So, data teams want to ensure data integrity, and marketers want to perform analysis faster. So, how do you find the right balance?
Marketers need a data hub they can work in easily. One that doesn’t require coding like a data warehouse, but is also more powerful and scalable than Google Sheets.
Data teams want to give a reasonable amount of autonomy to marketers so they can offload all the ad-hoc requests that take so much time.
The data hub needs to be capable of collecting data from all of the sources a marketer typically works with. To be more scalable than Google Sheets, it needs data cleaning capabilities so marketers can manipulate their data as they please.
The reality is that marketers want to get stuck into their data far more than they did just five years ago. While some Microsoft companies will have easily shifted to provide the marketer with more data autonomy, others are still stuck with inefficient data pipelines that don’t empower the marketer and hold back data teams by bogging them down with repetitive requests.
Funnel’s new Microsoft Power BI integration is here to change that. Data teams and marketers can work together to set up efficient data pipelines, starting with the automated collection of marketing data from all platforms, followed by stable connection to Power BI to facilitate the creation and automated maintenance of advanced dashboards and reports. All bundled with a scalable, easy-to-use transformation layer that empowers marketers to slice and dice their data and get it ready for analysis – all in one marketing data hub.
