In this blog, we will look at what is the usefulness of a modern data stack for the business intelligence of organizations and companies.
A lot of organizations today cannot avoid data. They collect data and try to draw conclusions from it. This is very useful, but often simply necessary. In a data-driven world, not only are we expected to do something with data: organizations and companies that do nothing or too little with it are left behind. The consequences can be profound.
The way data is collected, analyzed and presented can have a big impact. On the one hand, it determines what and how many insights can be extracted from the data. More information, but especially better information, ensures that more successful decisions can be made within an organization. That is what makes data-driven work so important. On the other hand, this setup also determines how much time employees can spend on which things. In doing so, a lot of time (and money) can be lost or gained. We see, for example, that within organizations a lot of time is spent on keeping obsolete systems up and running. It is a shame if that time cannot be used to work on new business insights!
What is a data stack?
By data stack here, we mean the collection of tools needed to conduct an organization's business intelligence (BI). For example, we are talking about tools to store, edit, visualize, analyze data, etc. At The Information Lab Netherlands, we have specific ideas about which technologies are best suited for this, but the main question here remains what best suits an organization. The goal is to generate insights and information for business operations. That's what business intelligence is all about. How best to do that varies by organization. It also depends on what software is already being used, what knowledge is available in-house and what the information needs are.
A modern data stack
So the design of a data stack is different for each organization. That doesn't mean there aren't important guiding principles. First of all, we see the cloud taking an increasingly important position. That's also what modern in modern data stack mainly stands for. Moving to the cloud means more flexibility, wider availability and better scalability.
In addition, it is also important for an organization to have ownership of the data. This is not just about the data, but precisely how it is transformed and what resources are used in the process. It should always be clear what is being done and why. Ideally, this should include how the solutions have evolved over time. Equally important is that the data is collected in a central location and is accessible to everyone who needs to use it. Creating a single version of the truth is often one of the main reasons for implementing a modern data stack.
Furthermore, it is crucial to be able to collaborate well. The tools used must provide sufficient opportunities for this. This becomes increasingly important as an organization grows, but is also already essential in small teams. By the way, this does not only apply within a BI team. It is precisely the intention that other people within the organization also have access to the data and can extract insights from it themselves. In this regard, it helps if the tools enable broad data self-sufficiency.
What's the point?
Finally, a good, modern data stack is more than just the sum of its parts. When all the tools work well together, it creates something that can add tremendous value. Often, a data stack consists of several components. These must be good at what they are intended for, but most of all, they must combine smoothly with each other. This saves time and creates focus in the BI team that doesn't have to be busy with the technology, but with enabling business insights that move the organization forward!
Sometimes it's good to ask the following questions: are we getting enough insights from our data? Are we spending more time on technology than on gaining insights? Does our data stack form a working whole that gets information to the right people? These problems can hold an organization back. A modern data stack offers a way forward.
Want to know more?
Curious about how we can help implement a modern data stack? Then get in touch with us.
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