It’s 2 a.m. You’ve been staring at an Excel spreadsheet for five hours, trying in vain to understand how to take your raw CSV file and transform it into something that will actually tell you how many sales-qualified leads your outbound marketing campaign drove last quarter.
At your last company, you had a team responsible for handling all of this, but at your new company, you’re running the show when it comes to data. Second to an energy drink, or better yet, a good night’s sleep, you wish above all else that you had all this data in one place you could actually work with, like a Looker dashboard.
Even though your data pipeline is murky as a swamp, what’s abundantly clear is that your company’s siloed approach to data just isn’t working.
Here, three tell-tale signs your company should rethink its data strategy — and suggestions on how to fix it.
As the demand for real-time, actionable data grows, so does the cast of characters working with the data, including data analysts, data scientists, data engineers, and even data governance representatives. Although there is frequent overlap between these roles, each profession requires different expertise. If you’re just getting started with your data initiative, these distinctions can be hard to keep track of.
We often find companies first realize they need data to answer business questions such as:
Frequently, companies’ first move is to hire a data analyst to define and model the data. Hiring a data analyst is an awesome necessary first step, but also insufficient if your goal is truly to become a fully data-driven organization. It’s important to keep in mind that the role you’re hiring for doesn’t necessarily include the skillset required to do all things data, e.g. model the data, maintain the extraction and loading workflow, and execute the transformations required to arrive at those insights.
Very often, the ability to work with and understand data is reserved for a select few in the organization, and despite the rise of data analytics training and data science boot camps, it doesn’t make sense for every data user in the company to become expert data modelers.
Data democratization, a term coined by data legend Bernard Marr, refers to the ability of all data users in an organization to access and understand data. Without data democratization, there can be tension when employees don’t understand the work required to build and maintain data models or run queries, and escalate these complaints to their managers when the data team tells them how long a request will take.
One customer success analyst at a leading food delivery service told us that this “ad-hoc querying” was the bane of her existence. “
We find that these bottlenecks are often the result of both a communication breakdown between different teams involved with the data, as well as a lack of democratization of analytic skills that would enable all data users to understand and work with the data themselves.
One of the most common challenges companies face when investing in data is bridging the gap between data infrastructure and analytics. The modern data stack has enabled a new path forward when it comes to quickly setting up a strong technical infrastructure, but a lot of data infrastructure is inaccessible or inappropriate to a company’s use case, which means that investments of money and time never translate into anything that positively affects the bottom line.
A few other places where data tooling can fall short:
Together, these three signs — a lack of clear data ownership, a lack of data democratization, and inappropriate tools and technologies — paint an alarming picture of a company that isn’t set up for success with data.
The good news? It’s never too late to start treating data with the diligence it deserves and building a data-driven culture at your company. Here’s how.
