/ Ten things to understand before using composable analytics

Ten things to understand before using composable analytics

If you’re unfamiliar with the term “composable analytics,” don’t worry; it’s a relatively new concept in business intelligence. But if you want to learn more about composable analytics and how they can help your organization, keep reading.

In this article, we’ll cover what composable analytics are and why they’re essential. We’ll also discuss some common questions companies ask when considering whether or not to implement them—and what types of answers might be suitable for you.


What is composable analytics?

Let’s start with a simple definition: composable analytics is an approach to analytics that prioritizes low-code or no-code data solutions that are transferable between use cases.

Instead of building bespoke analytic solutions for every problem that they face, businesses can use simple building blocks to construct data strategies easily and with less technical oversight.

For example, let’s say you have access to ten data sources from different companies in your industry. With compositions from those sources, you can create over 500 new views on these datasets—each may present additional opportunities for your company.


Ten things to be aware of before integration

1. Build a robust data strategy

A data strategy is a road map for your data and how it will be utilized throughout the organization. It’s a business-driven process that should be developed by your business, not the IT department or analysts specializing in analytics.

The strategy should outline what data sources you have, where they’re stored, who has access to them, what they’re being used for now, and what impact they’re having on all parts of your organization—including sales and marketing teams as well as those responsible for customer service or product development projects.

When working with composable analytics tools, it’s important to understand which parts of the tool adoption lifecycle should happen at each step to ensure success. This is all part of a robust data strategy plan.

2. Have an experienced and innovative team

If you’re trying to figure out how to get started with composable analytics, one of the first things you need to consider is who will be responsible for its implementation within your organization.

If you want an effective composable analytics strategy, this team needs to consist of data scientists, data engineers, and possibly data architects. Collectively, these people are known as “DataOps” (short for “data operations”) professionals—and they work together as a cohesive unit to ensure that all aspects of your company’s analytics are successful.

Together, they make up what we call a “DataOps team.” Your DataOps team should have access to all the relevant business knowledge needed to deliver outstanding results from composable analytics and the technical know-how necessary to implement these innovations successfully within your company’s infrastructure.

3. Create a budget

As you consider whether to implement composable analytics, you should consider the cost of implementation. The size of your organization will impact the size and complexity of your composable analytics implementation. If you aren’t careful, an overcomplicated implementation could cost far more than what it’s worth.

It’s important to consider all factors before beginning a project that requires significant re-engineering. In particular, we recommend making sure that this is something that is actually needed by your company or agency before moving forward with it.


4. Identify challenges

You should also consider the possible issues and shortcomings of composable analytics. One potential point is that you must carefully choose the right tool for the job, as not all business problems can be solved by composable analytics.

Another potential issue is that it may take time to learn how to use such tools effectively, especially if your organization doesn’t have many data scientists or data engineers on hand.

Some businesses may not have the resources to properly implement complex strategies like composable analytics. In that case, business leaders and BI implementers will need to do what they can, with what they have.

5. Have active data monitoring

You need a data quality strategy. The most important thing you can do when using composable analytics is monitoring your data’s quality. You should have a clear plan in place for how you’re going to check that your inputs are clean, accurate, and consistent.

If you don’t monitor the quality of your data, then it’s likely that you could be making bad decisions based on incorrect assumptions or unreliable information—and this will lead to poor results at best and catastrophic failure at worst.

6. What analytics are most important

The key here is to analyze the data that will help you improve your business and make it more efficient.

This can include analyzing how your customers use your products, which features they prefer, and how they use them. You can also explore what kind of content drives engagement, who the most influential users are, and what type of content they share with their fans.

Analyzing this information will give you insights into what works best for your audience. Start experimenting with different things until you find something that really resonates with them.

You should also look at analytics regarding what kinds of employees perform best in particular roles and whether there’s any way to improve employee performance by adjusting their schedules or changing their job titles (if necessary).

By looking at these data points, you might discover a better way to structure teams based on skill sets rather than just seniority or tenure with the company.

7. How will you use your data-driven insights?

There are many great examples of composable analytics in use today, so it can be tempting to jump right into the deep end and start building your own products. Before you do that, however, it’s important to understand what kinds of problems these existing tools solve and how they work.

Asking yourself questions like “What are the current use cases for analytics?” and “How can I leverage these solutions to improve my business?” will give you a good idea of where your efforts should be focused before investing in new technologies or services.


8. Train your team to use this technology

In order to use composable analytics, you need a training program for employees. This is important because everyone needs to know how to get the most out of the system and how their role will change. Training can be done in-house or by a third party, but it should be ongoing and part of your culture as an organization.

Make sure this is system-wide throughout your organization. You want everyone to feel empowered by the addition of new technology, not overwhelmed.

9. Set access rules

If you want your employees to be able to make the best use of composable analytics, they need access to the data for their role. This means that you need to carefully monitor access to data and balance data demands with data governance.

Different businesses have different data rules, but when using these sorts of customizable data solutions, it’s very important to make sure that people don’t have improper data access. While it’s good to give people access to all the data they need to succeed, it’s generally a bad idea to give everyone access to everything.

10. Consider integrations system-wide

Integrating analytics into your existing systems and technologies is a crucial component of achieving success with composable analytics. With the right integration strategy, you can leverage existing systems and technologies to build better analytics that are easier to use. Consider how your existing processes and people can be integrated with analytics so they work together seamlessly.


Preparing for composable analytics is critical to success

If you are thinking about implementing composable analytics, it’s important to understand all the elements involved.

Composable analytics is a new way of using data that is at the heart of how we think about solving problems with data. It differs from traditional approaches because it emphasizes how multiple perspectives can be combined to create a single view on an issue.

This approach helps us gain insights into complex problems by providing context for each perspective and allowing us to see relationships between diverse sources of information.

There is a lot of information out there, so it can sometimes be overwhelming to integrate new technologies. However, remember that composable analytics is just a toolset for your data scientists and analysts to build better products for customers.

You have a lot of options when choosing which tools are right for you—but whatever you do, make sure they fit into your existing processes, so they don’t add another layer of complexity.

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