In the technology industry, collaboration across disciplines is essential. However, such teamwork often faces obstacles, mainly technical issues, and differences in operating culture.

As it happens, data plays a key role in all modern day decision making. Companies collect data on everything to provide better services, better understand customers needs and expectations, to control all processes within the company, to improve efficiency, and to maximize their profits. Data-driven decision-making is a key factor that contributes to the success of companies these days.

Everything sounds great on paper, but it is much more difficult to implement it in real life the practical workflow. There is usually a huge amount of data stored in various databases. Professionals with little technical background, such as doctors, engineers, accountants, and managers, may have to work with the data. For them, database querying is just a tool to solve other tasks with.

For example, if we run a small veterinary clinic successfully, we will need to collect the patients’ personal data, their pets’ clinical history, a record of their appointments, prescription history and payments. Additionally, we will keep a record of all the staff’s duration of employment, their salaries, bonuses, vacations, and sick leave. We will also have a small marketing database with various promotions, mailings, and SMS notifications. All of the above is data. Obviously, some of this data set is manipulated by vets, nurses, administrators, accountants, marketers, system administrators and directors, each for their own tasks. Even in such a small company, you need to think of a secure, transparent, and clear data dealing process. Imagine what happens in large companies, where the processes are more complex and there is a lot more data to go through.

But where can the issues arise? Can we blame it on non-ideal technology or communication difficulties? Well, it is not that simple. One of the root causes is the different expectations, requirements and risks of the data management when multiple professionals with different tasks are involved. The good news is that these issues are not unique, and understanding the issue is half the solution. Let’s look at different challenges and how professionals deal with them.

 

Administrators

Administrators are the people who organize the infrastructure to work with data. We are not just talking about database administrators but all those who are responsible for ensuring the system is up and running for all other stakeholders. For example, this role might belong to a technical manager or a high-level technical person.

Issues and solutions:

The primary concern of any administrator is protecting sensitive data.

Role-based access control can help to solve this issue. Ideally, the distribution of different levels will protect data from unwanted actions. For example, this approach is relevant for the internal system in a bank. It implies setting up and constant monitoring of possible violations, as well as the readiness of specialists to process large amounts of data. This requires a dynamic system that can quickly adapt to these changes, reducing the burden on administrators and reducing the likelihood of errors.

As the organization expands, the complexity of managing user access rights increases.

To help users adapt, administrators create an intuitive and user-friendly interface and conduct regular data protection training. For example, when a bank introduces a new internal customer asset management tool, administrators can develop a comprehensive guide and organize workshops to ensure a seamless integration for employees.

It is equally important to ensure that all users, irrespective of their technical background, can use the system effectively.

To achieve this, administrators strive to create a system that is both secure and user-friendly, and that minimizes manual configuration and constant monitoring. Automating routine tasks and using intelligent systems to detect anomalies can make their jobs much easier.

 

Programmers

These are specialists who use data to develop and improve third-party systems. They both build new systems and support and adapt ready-made solutions internally. For example, they could be software engineers, backend developers or business intelligence developers. It is their job to develop robust, scalable, and compatible applications with the different technologies used by the different departments and their teams.

Issues and solutions:

Programmers often need help with system compatibility and data format mismatches.

To solve these issues, programmers can create APIs or middleware that allow data to be translated and transferred between systems without losing accuracy or functionality. For example, in a logistics company, the people overseeing the supply chain also need to know how to work with data, as do the more technically inclined employees.

In multi-disciplinary teams, programmers must often bridge the communication gap between technical and non-technical stakeholders.

To address technical skills gaps, developers should link complex technical concepts to practical business unit challenges, for example, through accessible guides for specialists in different areas. In addition, they need to regularly solicit feedback on the demands and constraints of different departments.

 

Data users

In fact, data users are the largest category of users. They are often overlooked when automating a company’s data processes. These are all the people who enter data into the database, analyze the data and make decisions based on the data. For example, these could be managers and analysts. Such specialists bridge the gap between the technical capabilities of the system and its practical application in different departments when working with data. That is, while the previously mentioned developers make data more accessible for work, these users in turn seek to put data into practice.

Issues and solutions:

Users are often faced with the need to interpret complex data and use it to make strategic decisions. They need systems that can provide accurate and actionable data without requiring in-depth technical knowledge.

A suitable solution to simplify the user journey may be to turn the tables that typically hold the data into a more visual form. For example, in a retail company, these users may have direct access to data that they need to interpret for their business needs. Why retail? Because large volumes of data on sales, shipments, and demand for specific products enable management to forecast strategies. Managers can be comfortable navigating visualizations of data to make informed decisions.

Data users are typically focused on the end customer of any product. They must navigate between the technical jargon of engineers and the practical needs of the company, ensuring that the notional application or platform is technically sound and viable.

To address their issue, a feature that collects detailed information about the most frequent actions of users to understand what they need would be beneficial. For example, in a retail company, data users desperately need information about their customers’ shopping habits. The ability to work with such data could lead to better customer service and sales strategies.

Conclusion

The key to success with data is for all three users to speak the same data language. They will understand each other’s issues, try to improve efficiency and reduce risk. Building and debugging data workflows is a long and complex process. It is good if there is a tool to work with data at all stages that is understandable, safe and convenient for all users.

An example of this is DBeaver Team Edition, our data product for teams. It offers administrators enhanced security features and the ability to distribute roles among different specialists. Programmers can take advantage of its data integration and system capabilities. Data users will find that it offers visualization tools and user-friendly interfaces to help them properly understand and analyze their data.

We hope that the issues of interaction between employees around data will affect you to a lesser extent. But if they do – now you know how to deal with them.