Musketeers of the Data World

There are two concepts that have been on the agenda of professionals working on data management for a while.

Data Governance ve Data Mesh.

When we look at it in very general terms, the agenda of today's data world, especially in our country, is focused on topics such as data warehouse, data lake and recently data lakehouse. Or it is completely focused on analytical solutions or reporting and BI solutions.

This is of course perfectly normal. Focusing on technologies that touch business outputs and produce tangible benefits and producing studies on these issues has become a necessity when we consider today's economic conditions.

The two concepts I will discuss in this article are a bit more difficult to explain and to make people accept. It can be challenging to explain the indirect benefits, especially to company management. Let's try to elaborate on these concepts a bit more.

Data Governance
In my previous articles, I have talked extensively about the importance of data governance and how we have to apply these principles if we want to create value from data. Here, I will try to provide some more summary and concrete information.

You can think of Data Governance as the traffic rules of the data world. Just as there are rules that everyone must follow in traffic, similar principles apply in data management.

This approach includes:

• Data definitions and standards  (i.e. everyone in the organization should speak the same language, everyone should understand the same thing about any concept or business term)
• Data quality  (our data should not be corrupt or incomplete)
• Data security  (we don't want anyone to steal data)
• Data ownership  (who can access which data, who is the decision maker about which data)

The important paradigm shift here is that the people who should make decisions about our data should no longer be IT, but business units. IT should be responsible for managing the infrastructure here. For example, when calculating a turnover, the relevant business unit will be the best one to decide which criteria to consider (for example, is sales including VAT, is e-commerce sales included, etc.). Therefore, the relevant business unit should assume this definition and ownership. In this way, we will have created a standard for many of our business terms, and everyone in our organization will understand and calculate the same thing for a business term. In this way, we will not reach multiple and different KPIs.

The great thing about data governance is that it keeps everyone on the same page, which means more consistent and accurate decisions, better data quality, and easier legal compliance.

Data Mesh
Data Mesh is actually a concept that is thought to be implemented by companies that have reached a certain level of maturity in data governance. It is important to have been implementing data governance for a certain period of time and to do this throughout the organization. The starting point of Data Mesh is to be able to produce a solution for issues where Data Governance is not sufficient.

It is impossible to mention the name of Zhamak Dehghani, who came up with this concept during his time at Thoughtworks, in such an article. We can say that this concept, which was first mentioned by Martin Fowler, one of the most well-known people in the field of Agile, on his own website, gained interest and acceptance in a short time.

Here, in a very simple way, we can say that everyone is responsible for their own data. Just as micro service architecture has created a change in the software world, it is possible to say that data mesh has created a similar effect in the data world.

The concept of Data Product is also one of the terms used by this new concept. Units are responsible for their own data and its management. Data is actually a product and can be served as desired within or outside the company. For example, unit A is responsible for the quality and security of its own data in every way, and if unit B wants to use any of this information, it can consume it like a product. Here, unit A, of course, undertakes all the quality etc. regarding the data it offers or serves, as I mentioned. Unit B trusts this information and uses it as it wishes.

Data Mesh offers a distributed and domain-centric approach to data management. In traditional data platforms, all data is collected in a central data warehouse or data lake, and data processing and analytics are usually handled by a central team. Data Mesh aims to move beyond this centralized structure and distribute data ownership and management to specific production teams. These teams become the owners and custodians of data specific to their domains.

The basic principles of Data Mesh can be summarized as follows:

• Domain-Driven Ownership : Data responsibility is distributed across the various business areas of the company or organization. This allows data teams to better understand the business needs of the relevant domain.
• Data as a Product:  Data is designed not just as raw information but as user-friendly, reliable and multi-purpose products.
• Autonomous Data Platforms:  The technological infrastructure allows each area to produce and manage data independently.
• Federative Data Management:  Standards such as data sharing, quality and compliance are carried out in a coordinated manner by cross-teams.

It’s safe to say that Data Mesh is one of the most groundbreaking approaches to modern data management. The decentralization of data ownership and management provides organizations with greater flexibility, speed, and innovation. However, for this model to be implemented successfully, companies need to transform both their technical infrastructure and their business culture. Assessing the potential offered by Data Mesh is a critical step for the data-driven organizations of the future.

In conclusion;

Our data is extremely valuable. As institutions, we have learned, accepted and internalized this over time. Now it is time to look at how we can manage it better and create more value from it.

It is extremely critical that a data-driven perspective be an institutional strategy. It is equally important for institutions to have an approach to how they manage their data. In the coming period, the importance of data seems to increase and continue to be the focus of more institutions.