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Until recently, data operations — or DataOps — have mostly been handled by marketers with an aptitude for Excel. Nowadays, however, what started as a very obscure and manual discipline is slowly gaining more respect as people come to understand the crucial impact that effective DataOps has on a business's success.
In this blog, we’ll look at the definition of DataOps, what responsibilities they take on, and what challenges they commonly face.
What is DataOps?
DataOps, or Data Operations, is the function of either a person or team that’s responsible for finding the most efficient way to collect and clean data within the company.
They collaborate closely with the reporting function to understand what data needs the business has, and then build out the data architecture around those needs.
What does that mean?
The DataOps function is all about creating a single source of truth. On the surface, it might sound simple, but don’t be fooled — DataOps teams do much more than data processing. It’s the job of DataOps to understand what marketing teams need to deliver on, find the data architecture that is required, and ensure that it is implemented correctly.
In most large companies and agencies there will be a person or small team of people who pull the levers to get the data into a single source of truth, ready for querying, dashboard building, and slide decks. This is the DataOps team — they’re the ones who build the foundations on which data storytelling can become an easy and pleasant experience, as opposed to wrangling with unaudited data. This single source of truth allows a larger reporting team to use the insight and data to drive their decisions.
How did the DataOps role come to exist?
The volume and complexity of data available to marketers have snowballed over recent years. Reporting and analytics teams have struggled to fill the vacuum of the new responsibilities associated with understanding and collecting all this new data that would go on to make up the DataOps role.
By introducing data experts with backgrounds in computer science, the DataOps role has added rocket fuel to streamline data strategies and make marketing teams much more efficient. For agencies, this is particularly relevant, as they’re often dealing with data from a number of clients.
What is the DataOps team responsible for?
To better understand the discipline of DataOps, here are three things for which DataOps is responsible.
1. Understanding data
The first thing DataOps must do is understand the data needs of the business. They look at what data they have, at the scope of what that data needs to deliver, and where it needs to be sent. Understanding this is essential to finding a data architecture that supports all the data sources, and the team’s preferred visualization tool, data lake, or data warehouse.
2. Collecting data
Collecting the data means figuring out the quickest and simplest way to pull all the relevant data sources together, and configure it. In essence, this just means decreasing the number of clicks it takes to get the necessary data all in one place.
3. Automating data collection
Once the process has been figured out manually, the ops team will find opportunities to schedule automation. Now that they understand what data they have, what it’s being used for, and how it’s configured, they can build out automation around these needs. So it’s not just about scheduling that automation, they also need to understand the business reason why, for example, an agency requires Google data to run every day at 2 am.
How do they do it?
The DataOps role requires the rare combination of an analytical thinker with a data background and someone who also has a good understanding of marketing strategy.
Someone who works in DataOps needs to be tech agnostic, and able to work with many different tools — and this is especially true in agencies, where ops teams will be working with different data and technology stacks from client to client.
DataOps roles are constantly looking for ways to find better flexibility and control over their data, and often that means finding low-code, or no-code approaches to data collection.
What could possibly go wrong?
Data-driven businesses need to be able to trust their data, and ensuring quality is a big part of the ops role. DataOps are responsible for daily maintenance. This means ensuring everything is loaded correctly every day. Through their metadata, they can see when data was updated, and if there were import errors - and with so many different platforms and tools being used, there are plenty of errors that can occur. Here are a few examples:
1. Errors in naming conventions
It’s up to DataOps to intervene if they spot an error in the data that’s about to be loaded and pushed through to dashboards and other reporting tools. So, for example, if someone has mislabeled a campaign name with “Germany” instead of “DACH”, this break from naming conventions could lead to mismatched data. Conversions from this campaign might not be counted, or the labels on the dashboard might change.
DataOps can set up rules and workflows to spot errors like these and send them, and the stakeholders responsible for those data streams with a notification or an email.
2. Size of data sets
Through metadata, DataOps can see an overview of their data operations. Usually, if a data fetch is taking much longer than expected, this is a good indication that a mistake has been made, and too much data is being pulled.
For example, if a data fetch that normally takes a few minutes takes three hours, this might be because the fetch is mistakenly pulling years' worth of historical Shopify data rather than one day’s worth.
3. Data governance
The DataOps role is responsible for putting mechanisms in place to control who has access to what, and a big part of this is ensuring that data retention is compliant with privacy laws like GDPR. This means handling any GDPR-related data deletions straight away, and for many large companies, this depends on a daily historical data flush. If these don't happen, companies can be at risk of legal action resulting in reputational damage and massive fines.
Conclusion
With the advent of new channels and platforms, collecting and combining data into a usable format has quickly become much more complicated than many marketing teams anticipated. The DataOps role has unlocked new potential for businesses to take advantage of data in a much more methodical way.
