The challenges facing data team collaboration

Data complexity is rising exponentially, and so is the intricacy within data teams. My experience suggests that businesses and CDOs dealing with a wealth of data usually have a complex data team at the helm - data engineers, data scientists, data analysts, business experts, and stakeholders to ensure an optimal balance and diversity of insights. Depending on the model, data teams can be centralized or embedded. Both scenarios have data engineers devising methods to diverge and disseminate data to the appropriate channels instead of data analysts, who translate data insights into solid business decisions. On the other hand, data scientists develop predictive models and collaborate with stakeholders more in-depth. Contrarily, business experts have a deeper understanding of business requirements and can align these with analytics (their roles often intertwine with that of data analysts).

Although each team has its role, collaborative cooperation among data teams is essential to maintaining harmony and consensus. Managers should know what Data Engineers are doing to calculate the ROI of data projects more accurately. Developers must understand what Data Analysts are doing to collect more relevant data. The challenge lies in the complex collaboration among different data teams.?

Let's take a closer look at it.

Lack of data access control

A question that every data-driven company and CDO should consider is, "How can I make our data discoverable and understandable to our entire internal team?". Bringing data from multiple data silos into one place is essential [1]. For instance, data teams trying out a new idea may decide to store the data they need for that use case outside of a centralized location, resulting in unorganized and disparate information that makes it challenging to collaborate effectively.

With a centralized data governance tool, different data team members could request various data sources, and administrators could limit the menu display of policies you could manage per role through the administrative interface; there would be no confusion or ambiguity. Businesses should look for a solution that establishes policies for data access based on factors such as IP addresses and access time.

Unsafe data collaboration practices

CDOs should ask themselves, "How do I prove ownership of data sources in case pipelines or models fail?" when working with complex data teams. Working with unregulated data at scale can lead to a commotion of non-hierarchical processes, no clear guidelines regarding ownership, and questions about "What to do?", "How to do?", and "What happens when a task is complete?".

By managing data workflows in a streamlined manner, businesses can select approvers and executors for data changes, track requests, approvals, and executions, and collaborate with notifications and comments. Implementing such practices into your organization can significantly improve day-to-day operations and support efficiency. Data teams can track changes to files while leaving comments and tags to enable deep-level monitoring using a centralized SQL editor with version control history and permission types based on SQL commands (DML, DCL, and DDL).

Inadequacy in handling data

According to a Qubole [2] report from 2018, some of the significant challenges faced by data teams include "too many manual tasks," "volume of data," and "speed of integrating new data." When I give product demos to our clients, I frequently notice that they are very interested in a solution that allows them to reduce their manual tasks.

Businesses could focus on more strategic initiatives by automating auditing and reporting practices, such as viewing user SQL executions and database authentication logs. CDOs and companies need a data governance provider to manage SQL event details and uncover user behavior and security concerns to manage this volume of data. In addition, cloud integration tools that automate ingestion, cleaning, and blending of data from any source should be prioritized, as they can dynamically transform an organization's entire data structure by providing a single, centralized source of truth.

The bottom line

Companies can ensure that all team members communicate and demonstrate their ideas, criticisms, and insights by meaningfully collaborating. As silos get broken down, the data team's structure allows seamless collaboration without compromising security. Collaboration among data teams is the glue that holds pragmatic thinking, innovation, and organizational growth together.

Data teams are responsible for developing a strategic data vision and enhancing self-service access to insights that will assist the company in achieving its goals. Data-driven organizations' business and IT data tasks increasingly overlap; with the data team in the cross-hairs of this overlap, these organizations should invest in assets that empower their data team.