/ 10 things you need to know before implementing composable analytics
10 things you need to know before implementing composable analytics
In today’s businesses, data is often siloed and inaccessible to those who need it. This means that organizations need to connect and collect diverse data sources for analytics purposes. Luckily, there’s a solution: composable analytics.
Composable analytics is the term used to describe the ability to assemble analytical models by combining data and operations from multiple sources. In other words, it allows you to create custom analytical models that are specific to your organization’s needs.
However, implementing composable analytics isn’t always a quick or straightforward process. Although there is no single recipe for success, there are a few things you need to know before you jump into the composable analytics pool.
First, let’s define exactly what composable analytics is and why it’s valuable to your business.
What is composable analytics?
Composable analytics is a term used to describe the ability to assemble analytical models by combining data and operations from multiple sources.
In other words, it allows you to create custom analytical models that are specific to your organization’s needs.
For example, let’s say you work for a pharmaceutical company and need to know the impact of the introduction of a generic drug on your bottom line. You can configure an existing analytical model by specifying the data sources and operations that are important to you (such as market research, customer surveys, etc.).
Since composable analytics is software-driven, it allows you to use this information in real-time. This is particularly valuable when you’re dealing with urgent problems and need fast access to the right data and analytics at the right time.
Why is composable analytics useful?
Composable analytics provides your organization with many benefits such as:
Improved decision-making: Data can be combined in different ways to optimize business decisions.
Easier collaboration: Composable Analytics allows for improved collaboration among your employees by providing better access to data.
Increased ROI: With flexible, software-driven analytics, organizations can easily track and monitor big data.
Ten things you need to know before implementing composable analytics
Although composable analytics provides a number of benefits, it’s not without its challenges.
For example, implementing composable analytics into your business can be time-intensive and require certain IT expertise.
1. You need a top-notch data strategy before you can begin your implementation
Even if you don’t have the time or resources to build a completely new data infrastructure, a few small tweaks can make all the difference in your composable analytics implementation.
This means thinking about where your data is coming from, what you’re going to use it for, and where you’re going to store it.
2. You need a strong team of data experts
Composable analytics is a complex process that demands the knowledge and expertise of highly-trained professionals. This means hiring people with experience in data science, analytics, and engineering to help you make sense of your big data.
3. Consider cost of implementation
Even a small change in your data infrastructure can make an impact on your budget. This means carefully evaluating the costs associated with both your current and future analytic capabilities to ensure you’re making the right decision before you get started.
4. Identify potential issues and shortcomings
Data analytics has become increasingly more complex, which means there are more potential issues to watch out for when implementing new projects like composable analytics.
For example, you need to be prepared for things like integration problems (e.g., data sources aren’t connected properly) and security issues (e.g., your data could be exposed to unauthorized individuals).
5. You need to monitor the quality of your data
Even if you already have a strong presence in analytics, composable analytics can help define how well your current analytic capabilities are working for you. This means implementing tools that can track the accuracy of your data so you can make sure it is always up-to-date and relevant.
6. Spend time on analytics that will improve your business
Even though replicating existing analytic models isn’t necessarily bad, you should focus more on simply improving them instead of creating something new from scratch.
For example, if there are analytics tools already in place that are working well for you at this time, you should use them instead of investing more money into building something new.
7. Identify current use cases for analytics
Not all organizations are the same, which means there’s no one-size-fits-all approach to composable analytics. This means thinking about how your business uses analytics today and comparing it to other companies you might be able to learn from.
This means getting everyone in your business up to speed on all things data—from using analytics tools themselves to interpreting the statistical output.
9. Ensure all areas of your organization have access
Composable analytics is a complex process that requires extensive planning ahead of time to ensure you’re doing it right. This means knowing what aspects of composable analytics your business can benefit from and which ones just aren’t going to work for you.
10. Consider how analytics can integrate with your current systems and technologies
Even if you think composable analytics is the right decision for your business, it’s important to carefully consider all of the technological restrictions in place that may interfere with this process.
This means thinking about factors like system security and compatibility before you get started.
Ensuring successful data analytics
Implementing data analytics can be a significant challenge without the proper tools and knowledge. This means preparing your team members with the most up-to-date training on how to use composable analytics technology effectively before getting started.
With composable analytics, you can create custom analytic models that are specific to your organization’s needs.
Data analytics has become increasingly complicated, which means there are more potential issues to monitor when implementing new projects like composable analytics.
If you are looking for a way to improve your data analytics, it’s essential to carefully consider all of the ways composable analytics can benefit you and your business.
Check out some related resources:
Nucleus Research: Domo’s ROI as a Data Platform
Embracing the future of data with augmented BI
Data Never Sleeps 9.0
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