Challenges for Tech Leders in Collaboration with a Data Team

The amount of data we work with trends towards infinity. A little more, and we will freely operate with nonallons and octalons. To solve such complex problems, we will need complex data teams, consisting of data engineers, analysts, QA engineers and other narrow-minded stakeholders. Managing such a team, and most importantly, finding great specialists has become a key task in recent years.

While teams are increasingly moving towards self-organization, orchestrating work across different teams is one of the key challenges that tech leaders can face. These obstacles can come from a variety of sources, including:

  • difficulty in the interaction between different teams or team members 
  • incomplete understanding of team the data with which they work 
  • insecure data collaboration practices 
  • improper manipulation of data

Thus, the best way for the tech leaders is to work with data engineering teams closely to resolve these challenges and ensure that the project is successful. That is why they should:

  • be aware of the data engineering areas in which they may need help 
  • be able to communicate effectively with team members who are unfamiliar with the data domain 
  • have a clear understanding of how the software works and new technologies that are being used in the executing project, plus be able to provide technical support during its execution

Let’s start from the basics.

Data team cooperation

Specialists in a data team cooperate to ensure that the data collected is accurate and reliable. This collaboration is essential to guarantee the team can produce valid and useful data. 

By working together, specialists can effectively identify and correct any errors in the data before it is used in further analysis or decision-making. 

In addition, this cooperation helps to confirm that the team has a cohesive understanding of their data and how it can be used to improve their work. 

That is how we arrive at the first test, which we will discuss now.

Challenge 1: Difficulty in the interaction

With data architect

There can be difficulty in the interaction between a tech leader and the data architect. Often, the data architect is responsible for managing and maintaining a variety of data stores. In contrast, the tech leader is responsible for developing a tech strategy that will optimize and deliver value to users.  This can lead to disagreements about how best to handle specific problems. Additionally, the two roles may have different expectations about what constitutes a successful solution. If these differences cannot be resolved, it can lead to tension and discord within the team.

With data engineers

Since a tech leader and data engineers are from different disciplines and have different perspectives on how technology should be used, this can lead to difficulty in the interaction between the two groups. In order for both groups to be successful, they need to communicate effectively and work together toward a common goal.

With QA data engineers

There could also be difficulty in the interaction between the tech leaders and QA data engineers. This nuance arises from the fact that each group has its own specific perspective and tasks on different levels. A tech leader is responsible on a high level for technological vision, implementing technology strategies, and ensuring that the technological resources are aligned with the company’s business needs, while QA engineers are working in a field and are primarily concerned on test data products across a variety of business domains utilizing a modern tech stack. It can be challenging to reconcile these two perspectives, leading to tension.

With data analysts

The tech lead and data analyst are key allies. Their interaction is increasingly important for many areas of business. At the same time, their experience of reading data can be critically different, which leads to various challenges in their interaction.

With BI experts

To optimize the BI function within an organization, it is often necessary to work closely with the chief technology officer (CTO). This can be a complex relationship due to the different levels of knowledge and expertise that each party may bring to the table.

In some cases, the CTO may not be familiar with how BI tools work and cannot judge their efficacy adequately. Additionally, the BI experts may not have all the knowledge needed to make informed decisions about which tools to deploy or how best to use them. As a result, there can be difficulty in communicating effectively between these two groups.

With DevOps engineers

Interactions between tech leaders and DevOps engineers are often fairly complicated. Because CTOs are typically more experienced with traditional IT operations, while DevOps engineers are more familiar with developing and operating software in a distributed environment.  As a result, communication can be difficult because each party is unfamiliar with the other’s perspective. 

From the first challenge logically emerges the next one, which directly intersects with its predecessor.

Challenge 2: Help the whole team understand the data correctly

Undoubtedly, having centralized data is essential for effective development work. When projects are managed using centralized data, it makes it easier for developers to track progress and identify issues early on. This helps them to fix problems quickly, preventing any major disruptions to the project. Moreover, centralized data makes it easy for colleagues to share resources and collaborate more effectively. And the most critical is that it helps the whole team understand the data correctly. Overall, centralizing data is vital to ensuring that development projects are successful.

Challenge 3: Insecure data sharing methods

When working with complex data teams, tech leaders should think about how they can prove ownership of data sources in the event pipelines or models fail. A complex data team is a type of team that is used to manage and work with large volumes of data.  These teams are usually composed of experts in different areas, such as data warehousing, big data, machine learning, and artificial intelligence. They use their knowledge to help businesses make better decisions based on the data they have.

Data security is important to ensure that everyone understands who owns the data and knows what to do if a task fails.

A streamlined data workflow can help businesses identify approvers and executors for such things as:

  • data changes 
  • track requests 
  • approvals 
  • executions

It also allows us to collaborate by using notifications and comments in code and other data.

Challenge 4: Incorrect operations with data

Typically, members of the data teams complain about annoying factors such as:

  • speed of integrating new data 
  • big amount of data  
  • and manual tasks 

Therefore, businesses should pay attention to the automation of many processes. For example, the automation includes reporting and auditing.

Conclusion

People are a key resource, especially in fields such as data, where ultra-deep expertise is required. They help companies make informed decisions, take action and grow. But it can be difficult to create a data team that works together effectively. 

WANT TO EXTEND YOUR TEAM WITH DATA EXPERTS?

Learn more about our data development services that help our partners build successful solutions, solve data challenges and speed up the delivery.

Today we shared with you the four most common challenges that CTOs faced in their work during the team extension process with data specialists. But professionals from GreenM would make this journey much easier for you. We will be glad to power up your current team and increase the effectiveness of your projects. 

Feel free to contact us to get more details about the benefits of cooperation with us. Have a great day!

GreenM

GreenM helps healthcare tech companies to scale and accelerate the time to market by taming the data deluge and building secure, cost-effective, and easy-to-use Analytics platforms.

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