The company of 2025 and the Data Mesh
4 min readFeb 5, 2022
Reading this McKinsey article The Data-Driven Enterprise of 2025 | McKinsey, in which definitely what will be a data-oriented company in the year 2025, and being quite in agreement with what is said in it, the doubt that assails me is how far today's companies are from this objective, and above all, what is the process that must be followed to achieve it. In this article, I will try to answer both questions, mixing some information with a lot of opinion as usual.
Although I recommend that you read the full article, I will also tell you that its conclusions are that these companies will have 7 fundamental characteristics, which would be:
- Data will be embedded in every decision, interaction and process
- Data will be processed and delivered in real time
- They will have flexible data stores that will provide integrated and ready-to-consume data
- Implement an operating data model that treats data as outputs
- The role of the CDO (Chief Data Officer) will expand to generate value
- Data ecosystems and their members will proliferate
- Data management tasks related to privacy, security and resiliency will be prioritized and automatedFrom a theoretical and conceptual point of view, how could it be otherwise, the article is impeccable, but from a practical point of view, I think some of those points are complicated to implement and in some cases, impossible to achieve for the most organizations. But let's go to the pit
How far is my organization from this prototype by 2025?
To a large extent, the effort required to reach that ideal company state defined by McKinsey obviously depends on the starting point, which I tried to summarize in this previous article, where I commented on the different stages of maturity of organizations with respect to data. Perhaps from the outset, it may seem to you that there is no relationship between one thing and the other, but in reality, if we have been able to complete the aforementioned stages of maturity, we will hardly have been able to achieve it without complying with several of the requirements mentioned by the McKinsey article, and as an example, the fact that if we have been able to reach the stage of maturity that leads us to implement prescriptive analytics solutions, we will have decisions based on data embedded in our decisions and processes, which is the first of the features highlighted by McKinsey. Bearing this in mind, my personal reflection is that the vast majority of companies are far, far away from having these seven characteristics. The positive part is that I think it is perfectly possible to reach them in the time frame of 3-4 years, no matter how big your organization is or how far behind it is in the maturity stage (or both, which is not strange to find :-))
What process should I follow to get it?
The first step is to accept, effectively, that it is a process, and that it will take time. There are no magic solutions and, in addition, you must be willing to iterate, which means establishing priorities, but always keeping in mind what the final objective is. Analyzing the points made in the reference article, I believe that there is one of them, which is the cornerstone on which the rest are based: “Implement an operational data model that treats data as products”. The first time I read about this concept was in the article in which Zhamak Dehgani defined the concept of Data Mesh (this concept deserves a series of articles for himself) back in the middle of 2019 and that you can find here How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh. This article defends that we should make a similarity between how we think about our data sets, and the processes of "product thinking" and defines the characteristics that these data products must have:- Discoverable
- Addressable
- Trustworthy and truthful
- Self-describing semantics and syntax
- Inter-operable and governed by global standards
- Secure and governed by a global access control
Basically, it is about defining a distributed architecture, but with governance of catalogs, metadata, processes and global security. Quite a challenge, but I believe that technology already supports it today, and the model is mature enough to base our data strategy for 2025 on it.Keep posted!!
https://medium.com/@antoniosql/membership