Common Obstacles in DataOps
How These Obstacles Slows Down the Innovation of Modern Data Teams
As a Data Platform Engineer, I can vouch for the many benefits of DataOps: better collaboration & coordination, automation, fewer errors, security, best practices, reusing existing materials, self-service capabilities, and data democratization. Unfortunately, before we see these benefits, we have to implement DataOps and overcome the following obstacles.
Lacking Data-Driven Culture
Missing Leadership Buy-In
Ineffective Leadership
No DataOps Advocacy
Lacking the Skillset
Finding the Right Tool
In this article, I will explore these specific obstacles and their implications for a data team.
Lacking Data-Driven Culture
Before considering how data-ops cannot progress without a data culture, let's examine what a data-driven culture is.
A data-driven culture is a corporate culture that prioritizes the use of data and evidence in decision-making processes. This means that decisions are based on facts and data rather than intuition or personal experience. In a data-driven culture, data is used to inform strategy, identify opportunities and problems, and measure performance.
If the culture does not support data, then anything related to data is also not supported. This is what most often becomes a problem in DataOps.
Missing Leadership Buy-In
Just like any other data project, DataOps is a practice and culture. This means that it has to be driven by the head of the organization.
Without the full support and commitment of leadership, it is difficult to achieve success in any endeavor. Even though there may be enthusiasm and motivation from within a team, if the leadership is not fully on board, it can be difficult to make meaningful progress.
Ineffective Leadership
As a leader, the main goal is to build out a data strategy. A data strategy is a plan that outlines an organization's vision for managing and analyzing its data. It defines the technology, processes, people, and rules needed to use data to make informed decisions. Developing a data strategy can be a complex process, requiring the involvement of multiple stakeholders from across the organization. It is important to consider how the strategy will be implemented, monitored, and adapted over time.
What happens, then, if leadership fails to develop a successful data strategy? Without a well-crafted data strategy in place, the team is unable to prioritize and focus on data-driven initiatives or ensure that everyone is on the same page when it comes to data-driven initiatives. This can have a detrimental effect on the organization, resulting in lost time and money due to a lack of direction and focus. It can also lead to a decrease in productivity, as team members are unsure of their data-related roles and responsibilities
No DataOps Advocacy
DataOps requires change from within the team. Buy-in from leadership, we still need to get buy-in from the rest of the team. What that means is we need a group of DataOps advocates within each part of the team (data analytics, data engineering, and data science). What exactly does it mean to be an advocate for DataOps?
Being a DataOps advocate within the data team requires taking a proactive stance in order to ensure that the use of DataOps is successful and effective. DataOps means the implementation of a lot of foreign tools that at first will add an extra layer of complexity to the team, but in the long run, will be beneficial.
For example, imagine that we want to add a data build tool (DBT) to our tool. DBT will provide us with insight into the tables that are being created, and add data quality checks, and lineage in the long term. However, in the short term, many might oppose it because it is an additional tool to add that they have to learn. We should show how they can implement this tool to an advocate of each team so that they can showcase it to their team and in the process become an advocate.
In short, being an advocate for DataOps is about making sure that the data team is able to use the most effective practices and tools to achieve the desired results.
Lacking the Skillset
DataOps requires a distinct set of skills and capabilities that go beyond what data engineers already know. While they need to have a deep understanding of data architecture, coding, and data analytics, they will need to go beyond that and learn how to build out continuous integration and deployment systems and other tools that most data engineers are not familiar with. In addition, they will need to be well-versed in the principles and practices of DevOps, including the use of automation and orchestration tools to ensure reliable and consistent delivery of data-based services. To make DataOps part of the data team, many will have to learn the skills that are needed. And that of course will take time.
Finding the Right Tool
After the missing skill sets have been identified within the team, the last and probably the most important step is finding the right tool. Of course, there are several factors to consider when selecting the right tool for your team, such as the cost of the tool, the size of the team that will be using it, and the number of integrations you will need to make it fit into your team’s data stack. What's more, you'll also want to consider the user experience of the tool, as it is essential to ensure that the tool is intuitive and easy to use.
Defining the requirements and choosing the right tool is a major obstacle to implementing DataOps.
Final Thoughts
DataOps can be an invaluable solution to many of the data-driven challenges organizations face today. However, in order to be successful and truly unlock its potential, organizations need to ensure that data is at the heart of their culture and have buy-in from the leadership team. A robust data strategy needs to be implemented to ensure the right tools and skills are in place to support the team. By doing so, the team can achieve a holistic approach to their data stack and truly capitalize on their data.
To learn more about how to address these challenges, stay tuned for the next article!
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