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:
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:
Let’s start from the basics.
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.
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.
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.
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.
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.
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.
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.
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.
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:
It also allows us to collaborate by using notifications and comments in code and other data.
Typically, members of the data teams complain about annoying factors such as:
Therefore, businesses should pay attention to the automation of many processes. For example, the automation includes reporting and auditing.
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.
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!
Share with friends
Copyright © 2024 GreenM, Inc. All rights reserved.
Insights, useful articles and business recommendations in your inbox every two weeks.
