Data is the new capital of the world, or so they say. It’s true that businesses have become more dependent on Data Mesh than ever before to reach their profit goals. And this data can be generated from various sources, including social media posts, website logs, call transcripts, and more.
The challenge many companies face today is how to effectively translate and use this data to implement smart sales strategies and boost revenue in the coming months. There are now many platforms available to help small and large companies sift through data mesh, but only a few can deliver the kind of quality needed by your business.
What is Data Mesh?
When you think of data, what comes to mind? If you’re like many people, you might imagine rows and columns in a spreadsheet, charts, and graphs from a PowerPoint presentation, or even customer records that are stored somewhere in your organization’s cloud. You probably don’t picture actors like Brad Pitt or Angelina Jolie—but maybe you should.
Have you ever heard the phrase “data is the new oil“? In some ways, it’s an apt comparison: data can power your business much like crude oil powers an engine. But the idea that data is something static—something inert—isn’t entirely accurate. Data is actually a living thing, constantly growing and changing as new information becomes available. People who use words and phrases such as “data lake” or “data warehouse” to describe where their company stores information are missing this important aspect of how data works. The idea that data can be neatly contained until someone needs it is inaccurate; any discipline that aims to manage data must understand its dynamism.
Data mesh is a movement towards improving how we manage our most valuable resource by making all aspects of this process more transparent and empowering, including those related to storage. That includes moving away from centralized storage systems (i.e., databases) and toward distributed architectures with decentralized responsibility for governance on account teams themselves instead of relying on centralized IT operations groups (Ops).
How can Data Mesh help your business?
The answer to this question depends on what your goals are, but there are a handful of reasons why data mesh can be beneficial for any company.
- Data mesh will help you make decisions faster. When data is spread across many teams and departments, it becomes much more difficult to access and interpret that data. This means that it takes longer for decisions to be made, or the decision isn’t informed by key pieces of information because those pieces were too hard to find. By using a data mesh, companies can avoid these delays by giving all employees easier access to the same information.
- Data mesh is more secure than traditional methods of storing customer information. In addition to being more secure than traditional methods, a data mesh helps teams create better privacy policies because they all have an easy time understanding how customer information flows through the system as well as where it’s stored. Having this knowledge makes it much easier to find potential vulnerabilities in your security infrastructure and plug them in before they become problems.
- Data mesh is simply a more effective way of storing customer information than traditional methods. Because individual teams no longer need their own databases, there’s less duplication between systems and, therefore less wasted effort on managing those systems separately from each other (or even duplicating them). It also means that new employees don’t have to spend as long getting up-to-speed about where different types of data are stored because everything is accessible from one place: their team’s database!
When is Data Mesh not useful for a business?
With all the hype around Data Mesh and its potential to transform business functions like data science, it’s easy to assume that it’s a good fit for every organization. However, in reality, Data Mesh might not be the best solution for you. If your company has a large number of data sources and generates a high volume of data—but you have no idea what the data is or what to do with it—then Data Mesh will be difficult to implement until you can sort through the data. Similarly, if your team isn’t prepared to dedicate the time and money needed to make sense of your data in order to accurately define your data requirements and architecture, then Data Mesh might not be worth trying.
Also, if your organization already has a relatively mature data strategy (for example, by investing in tools that allow your business users to access their own specific datasets), then you might not need to go so far as implementing a full-on Data Mesh. It’s important to assess whether or not this is something that will benefit your company before you plunge into an implementation phase that could distract from other initiatives.
With traditional databases, such as SQL Server or Oracle, when something happens within your system—for example, someone deletes a record or accidentally overwrites some information—you have to re-create the entire database from scratch just so it’s consistent with what’s currently being used. The other issue with traditional databases is that every time you install an update within those platforms, it will affect all the systems connected to them via shared file shares or through some kind of replication process. With distributed systems like SANs and Active Directory, if you don’t make sure all the servers are running the same version of the software before installing updates on one server—or if two servers installed different versions at different times—then things can go wrong very quickly. So basically what we’re dealing with when we talk about data mesh versus traditional storage infrastructures is similar but now using a decentralized approach rather than using centralized models and shared storage points like SANs and Active Directory (AD).
More and more businesses are using data mesh for data storage and to improve the effectiveness of their data
In part, this is because they want a decentralized way to manage their data, as well as an effective way to improve the quality of their data. Data mesh provides these benefits, giving organizations more flexibility in how they use their data. The future of effective data architecture is here, and it’s called the Data Mesh.