Data is the lifeblood of any SaaS company. You’ll be able to make better decisions and expand faster thanks to data. Few businesses, however, make the most of their existing data or collect the data they require.
This guide is intended to help you learn as much about analytics as rapidly as possible. It doesn’t matter if you’re starting from the ground up or simply looking to strengthen an existing data strategy.
Develop a solid overall strategy
Developing an effective data strategy is not a one-time task. Rather, your business plan must be the driving force behind it as well as the processes you use to operate and improve your business.
As a result, the first stage in developing a data strategy should be to concentrate on your business goals and important questions. This will ensure that you aren’t spending time and money collecting, processing, and storing incorrect data.
For a start, consider the following questions:
- What are my business goals and strategic priorities?
- What role does data play in achieving these goals?
- What insights can assist my company in making progress?
Use the right tools
Git is a version management system for computer files that tracks changes. Its primary goal is to keep track of any changes made to one or more projects over time. It aids in the coordination of project team members’ efforts and keeps track of progress over time.
Git also monitors project files for both programming pros and non-technical users. It can handle any project size, allowing multiple users to collaborate without interfering with one another’s work.
If your company uses Jira issues, you can migrate them to GitLab and work completely there. If you want to keep using Jira, you can do so by integrating it with GitLab.
You can choose between two types of Jira connections in GitLab, depending on the features you require. But keep in mind that it’s best to enable them both.
Also, there’s a possibility to cross-reference activity in your GitLab project with any of your Jira projects when you set up one or both of these connectors. And best of all, Bitband made the GitLab Jira integration solution to give your data planning even more power.
Once you’ve decided how you’ll use your data, you may consider the procedures that will be required to collect it. So, consider your data sources and whether you’ll require access to both internal and external resources.
You’ll also need to select how the data will be collected, including if it will be done manually or whether precise scheduling is required.
Data architecture is a methodology that focuses on constructing the IT infrastructure needed to realize the business outcomes defined in the data strategy. Simply expressed, it describes the collection, storage, transformation, distribution, and consumption of data.
The rules that govern structured forms (such as databases) and the technologies that connect data to the business processes that consume it are also part of data architecture. A data architecture that is both adaptable and scalable is essential for a successful data-driven strategy.
But building one isn’t necessarily simple. Data architecture frequently includes a variety of different components, such as first-party data, APIs, data pipelines, cloud storage, and real-time analytics, because it is responsible for defining the data standards that govern what kind of data will travel through it.
Creating a data scheme is one technique to attain these data standards. This will specify which entities should be collected, what type of data each piece should contain, and how entities in different databases should be linked.
Training and deployment
Consider training and deployment for a moment. The idea and goal are for your team to make data-driven decisions. And to do so, they’ll need to be trained on how the data is organized and how to use the tools you’ve put in place.
You should, at the very least, do the following three things:
- Team sessions. These are generic workshops that cover the essentials for your entire team. Over the course of a few months, run 1-3 of these sessions.
- One-on-one meetings. These meetings should be tailored to specific individuals and the reports that they are interested in. Run 3-5 of these sessions over the course of a few months.
- Create a dedicated analytics channel. You can do this on Slack (or something similar) if you use it. On this channel, people can send inquiries and receive prompt responses and explanations.
Data is now at the heart of every organization, and effective data management is becoming increasingly vital. A well-thought-out data strategy may provide a solid platform for consistent project methods, seamless integration, and overall corporate expansion.